Insurers are navigating climate volatility, cyber threats, evolving customer expectations, regulatory pressure, and an overwhelming volume of data. They need trusted AI-powered solutions that help them manage amid these headwinds. AI creates an opportunity to do more than accelerate isolated tasks. It can help insurers bring together data, organizational knowledge, and human expertise to support better, faster, and more trusted decisions across the enterprise.

Managing AI agents across the enterprise

At ITC Vegas 2026, the shift from isolated AI pilots to practical, governed AI that can deliver measurable business outcomes was hard to miss. Across discussions about underwriting, claims, distribution, and risk, the focus has moved beyond experimentation toward solutions that help insurers make better decisions at scale. After 10 years at ITC, we see a clear inflection point: the industry is no longer asking whether AI will reshape insurance, but how to scale it responsibly while keeping people accountable for consequential decisions. Beyond the event, the durable challenge is how insurers orchestrate data, expertise, workflows, and AI agents across the enterprise without compromising security, compliance, and governance standards.

When I first attended ITC a decade ago, the conversation centered on digital experiences and connected ecosystems. In returning this year, I saw how that ambition has expanded to reimagining and optimizing every function across insurance through AI. This new realm of innovation makes orchestration, governance, and security essential to scaling responsibly—and that is where Microsoft’s foundation of trust sets us apart.

Jeffery Williams, Managing Director, Microsoft

Four areas stand out as especially consequential: accelerating underwriting and decisioning, evolving the actuarial function into a real-time risk intelligence hub, modernizing claims operations, and empowering relationship managers. Underpinning all four is a secure data and AI foundation that turns fragmented signals into actionable intelligence, and actionable intelligence into stronger decisions, operations, and customer trust.

1. How AI can accelerate underwriting and decisioning

Underwriting is where the promise of AI becomes tangible: a faster path from fragmented submission data to a decision an underwriter can stand behind. Teams face growing volumes, increasing risk complexity, and pressure to respond with greater speed and accuracy.

AI can synthesize data, surface the signals that matter, and streamline workflows so underwriters spend less time gathering information and more time applying their expertise, evaluating risk, and making informed decisions. This helps insurers modernize underwriting processes while improving speed, consistency, and decision quality. Agents can parse broker emails, forms, and attachments; enrich submissions with internal and third-party data; and present prioritized worklists based on risk, value, and complexity.

For example, Shift Technology uses Azure OpenAI, Azure AI Vision, and Azure AI Document Intelligence to help insurers automate the classification and extraction of information from policies, claims, and supporting documentation, reducing process time from weeks to days. For more complex risks, AI agents can synthesize engineering, financial, and medical documentation into concise, auditable briefs with clear source attribution. The result is faster, more informed decisions, more consistent risk assessment, greater underwriting capacity, and improved experiences for brokers and customers.

Importantly, autonomy should remain bounded by clearly defined roles, authority levels, and escalation paths. AI can summarize risk, propose draft terms, or recommend next actions within defined thresholds, while higher-impact decisions are escalated to human underwriters with the relevant context, source attribution, explainability, and audit trail needed to exercise judgment and act confidently.

2. How AI can evolve actuarial function into a real-time risk intelligence hub

The same shift extends beyond underwriting. For decades, actuarial leaders have talked about flipping the familiar 80/20 equation: too much time spent gathering, reconciling, and preparing data, and too little spent on analysis, interpretation, and strategic decision-making. Cloud, generative AI, and agentic systems are making that ambition more achievable.

AI agents can support model-run orchestration, data validation, experience studies, assumption management, scenario generation, variance commentary, and regulatory documentation. They can ingest and normalize geospatial data, satellite imagery, hazard layers, sensor feeds, and unstructured field reports, then coordinate model execution and flag changes in exposure, climate conditions, or model behavior that warrant actuarial review. Emerging multi-agent approaches can also test models overnight, identify anomalies, document adjustments, and prepare review-ready summaries before the workday begins.

As a first-time attendee at InsureTech Connect, I learned a tremendous amount from the discussions on new data sources and the rapidly expanding applications of agentic AI. These capabilities will help actuaries better understand and model emerging risks, which is increasingly important as societies face evolving global challenges and protection gaps that affect financial, health, and overall community well-being.

Rich Moyer, Chief Product Officer, Milliman Vehicle Telematics Practice

This does not diminish the actuarial role; it elevates it. The actuary of tomorrow will be increasingly AI-enabled and human-led—with actuaries directing agents, validating outputs, challenging assumptions, explaining tradeoffs, and translating risk intelligence into better-informed decisions across pricing, reserving, capital, product, and business strategy. As AI takes on more of the technical execution, human differentiation will increasingly come from creativity, cross-disciplinary judgment, communication, and accountability.

That evolution depends on disciplined governance. Actuarial teams need model lineage, reproducibility, confidence thresholds, bias and drift monitoring, peer review, and clear human ownership of outcomes. AI can inform and strengthen decisions, but it should never obscure how a conclusion was reached or who is accountable for the outcome.

Partners are already helping insurers modernize in this direction. Milliman Integrate brings cloud-scale actuarial modeling, workflow management, collaboration, and reporting to life insurers, while Aon PathWise supports high-performance actuarial and balance-sheet modeling. These platforms point toward a more open, interoperable actuarial ecosystem in which compute, data, models, and AI services can work together without sacrificing auditability.

3. How AI can modernize claims operations

If underwriting is where risk begins, claims are where the insurance promise is tested. After a loss, what matters to a policyholder is not a workflow; what matters is whether their insurer understands what happened and can help. What matters is speed, accuracy, empathy, and the quality of the response.

AI can help insurers modernize claims operations by reducing manual effort, accelerating intake and triage, and equipping adjusters with the information they need to act quickly and confidently. Agents can capture loss details, verify coverage, organize documents and images, identify missing information, surface potential fraud, and recommend next actions.

Organizations such as Gallagher are already applying AI to claims workflows. Gallagher uses claims summarization capabilities that reduce their review times from hours to minutes, all while helping adjusters identify critical information more quickly. Low-complexity claims may move through defined workflows with minimal intervention, while complex or high-impact cases are routed to experienced professionals with the context needed to exercise judgment.

This is especially important as catastrophe frequency, litigation, and repair complexity put pressure on claims organizations. The goal is not automation for its own sake. It is to reduce friction, improve consistency, and create more capacity for the human judgment and empathy that policyholders need in difficult moments. The strongest operating models will pair speed with clear escalation paths, explainability, and human accountability so that every claim decision can be made and communicated with confidence.

4. How AI can increase relationship manager effectiveness

The same expectation for immediacy extends to every customer and broker interaction. Relationship managers are expected to be trusted advisors while navigating growing volumes of account information, open issues, market signals, and internal knowledge.

AI can help employees turn organizational knowledge, customer context, and market signals into actionable items, allowing them to spend more time strengthening relationships, advising customers, and delivering value. Before a meeting, an agent can assemble account history, open issues, renewal timing, relevant products, and recent interactions. During and after the conversation, it can capture notes, draft follow-up, assign actions, and keep commitments from slipping. Generali France, for example, uses AI to help employees access the right information quickly, synthesize customer data, and better understand client intent across millions of interactions. This helps teams deliver more relevant and personalized experiences while spending less time searching for information and managing administrative tasks.

When connected to governed customer and enterprise data, these capabilities can make interactions more timely and personal without losing context across channels. The value is not just faster preparation. It is better engagement, stronger follow-through, and more time for relationship managers to focus on advice, negotiation, and trust.

Building insurance intelligence on a foundation of trust

AI is only as valuable as the intelligence behind it and the trust that supports it. As AI adoption accelerates, insurers need to govern how data, intelligence, and actions move across people, systems, agents, and workflows. Realizing the full potential of AI requires more than adding new technology. Insurers need solutions that securely connect data, knowledge, processes, and people while maintaining the privacy, compliance, and governance standards the industry demands.

That starts with turning organizational data into actionable intelligence and grounding AI in business context and institutional knowledge. It also means bringing AI into the tools and workflows employees already use, rather than creating another disconnected layer of work.

As agents take on greater responsibilities, governance must become part of how work is designed and executed. Insurers should define what an agent can do, on whose behalf it can act, which data it can access, and under what authority. Controls for identity, access, data protection, monitoring, testing, auditability, and human escalation should be designed into the workflow from the beginning. Responsible AI principles—including fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability—should translate into practical controls that employees, regulators, and customers can understand.

In a trust business, governance is what makes innovation scalable. The insurers that combine enterprise-grade security and compliance with transparent, well-governed AI will be better positioned to move quickly, improve decisions, and earn confidence at the same time.

In insurance, governance isn’t what slows down AI innovation; done correctly, it’s what gives organizations the confidence to accelerate. The goal isn’t to govern AI at the speed of compliance but to use governance to enable AI at the speed of the business. The maturity question for insurers is no longer simply how many AI use cases they can create. It’s how safely, repeatedly, and quickly they can turn those use cases into enterprise capabilities.

Dan Alecia, Partner, IBM Consulting Americas Microsoft Practice, Financial Services Leader

Frontier Firms deliver in moments that matter

The competitive question is no longer who can launch the most AI pilots. The question is who can scale AI across the enterprise with the governance, security, compliance, and accountability required to turn intelligence into a trusted operating advantage. That advantage will help people make better decisions, serve customers more effectively, and respond with confidence when the stakes are highest.

The next generation of Frontier Firms will not simply automate processes. They will redesign work around outcomes, bringing people and AI agents together across connected data, expertise, and enterprise, workflows within a secure, governed foundation built on intelligence and trust. For actuaries, underwriters, claims professionals, and relationship managers, that means less time spent assembling information and more time exercising judgment, interpreting uncertainty, making informed decisions, and serving customers.

That is the strongest signal we took from this year’s ITC conversations: AI is moving from a collection of experiments to a trusted operating layer for insurance. For insurers, the long-term advantage will come from combining governed data, responsible AI, and human judgment across underwriting, actuarial work, claims, and customer engagement. The insurance organizations that lead will combine human expertise with AI-powered intelligence to make better decisions, move with greater speed, and deliver stronger outcomes while preserving the trust on which the industry is built.

The post The next insurance advantage: Intelligence and trust appeared first on The Microsoft Cloud Blog.

That advantage will come from three reinforcing moats:

• Reputation
• Alpha
• Distribution

Reputation is the permission to operate in consequential financial workflows. Alpha comes from the proprietary data, institutional knowledge, judgment, and insight an institution can compound over time. Distribution is the ability to put that intelligence into the hands of employees, advisors, clients, and partners at the moments where decisions are made.

What agentic AI means for banks and financial institutions

A financial institution of the agentic era uses governed AI agents, trusted data, and human oversight to connect work across banking operations, risk, compliance, and client service. Rather than deploying isolated AI tools, banks can build reusable intelligence into workflows such as onboarding, lending, payments, treasury, and service while maintaining identity, permissions, auditability, and accountability.

The next phase connects context, execution, and control with intelligence. Context consists of institutional data, market intelligence, relationship history, risk signals, documents, and operating knowledge. Execution means enabling agents and applications to help coordinate work across workflows, not just answer questions in isolation. Control is about ensuring that every action is governed by identity, permissions, observability, auditability, resilience, and human accountability.

Microsoft’s role is to provide an integrated foundation that helps financial institutions retain control of their data and compound their intelligence across cloud, data, AI, security, and collaboration—bringing governed capabilities into the applications and workflows where work happens.

Modernize banking operations with trusted intelligence

Connect onboarding, lending, payments, treasury, and service

Banking value chains have been organized around products and functions:

• Onboarding
• Lending
• Payments
• Treasury
• Service

Trusted intelligence can connect these activities while preserving their controls. A commercial client’s onboarding information could inform treasury setup. Approved cash-flow insights could support a liquidity conversation. Trade-finance documentation could be checked and routed for expert review. By reducing handoffs and carrying permissioned context across workflows, institutions can create a more coherent client relationship.

Capital markets face a related shift. Research, pre-trade analysis, execution, post-trade processing, and custody can become connected services rather than isolated steps. For example, an appropriately authorized agent could assemble approved research and portfolio exposures for an investor, while another could investigate a settlement exception and prepare an escalation. These illustrative scenarios do not justify giving every workflow the same level of autonomy.

Build on a governed data foundation

Modernizing the operating core is about more than technology; it is about improving client outcomes. Institutions need adaptable platforms, governed data foundations that make intelligence reusable, and systems that enable people and agents to collaborate across traditional boundaries.

BNY demonstrates how a global financial institution can embed intelligence into its operating model. Its Eliza platform brings together governance, agent-building capabilities, learning resources, and AI tools in one environment, now supporting more than 300 solutions across the institution. The impact is already showing up in core workflows: AI-assisted onboarding is 20% faster, more than 10% of client settlement inquiries are resolved or assisted by AI with 80% faster processing, and digital employees now handle more than 10% of payment-repair activities globally.

The opportunity is not just to automate work, but to also deepen collaboration through our partner ecosystem and embed Azure intelligence directly into the processes that drive operational performance.

—Deanna Lanier, Global Head of Strategy, Data & Analytics, BNY

None of this works without a governed data foundation. LSEG is using Microsoft Fabric to unify its systems and datasets in a governed platform, improving data quality, and bringing new-product development timelines down from years to months. Genworth is a second illustration, closer to the desk: Microsoft 365 Copilot in investment operations has reduced the time needed to construct complex portfolio trades from days to hours, inside the workflows the team already uses, with the decision itself still resting with people. Neither example creates a competitive moat on its own. The advantage comes from turning trusted intelligence into better decisions across workflows.

Strengthen AI risk management, compliance, and resilience

In financial services, trust cannot be an assurance attached to a technology. It must shape how that technology operates. To operate with trust, every agent needs an identity, a defined purpose, authorized data access, and limits on action. Institutions need to know where data resides, how an answer was grounded, what changed, and who remains accountable.

Security, sovereignty, resilience, recovery, model governance, and auditability belong in the design. Identity, permissioning, auditability, data protection, lifecycle management, and policy controls must travel with the information itself rather than be added later as separate layers, so they still hold when work crosses teams, partners, and differing licensing or confidentiality contexts. Suitability and explainability belong in that design by default, not as a review bolted on at the end.

Keep consequential decisions human-led

The direction is human-led and agent-operated, not autonomous everything. An agent can gather evidence, reconcile information, or propose a response. A person keeps authority where professional judgment, fiduciary obligations, or consequential decisions require it. Escalation paths, approval thresholds, and the ability to stop an action belong in the design before scale, not after an incident.

Bradesco’s use of AILA to support audit planning illustrates how AI can help professionals focus more attention on analytical depth and important business risks. The broader opportunity is to make controls more informed and timely without confusing automation with assurance. A generated explanation is not evidence of correctness, and a faster process is not necessarily a safer one.

The solution has transformed the dynamics of auditing. We’ve evolved toward a more strategic performance, based on analytical intelligence and delivering outcomes that generate direct value for the organization.

Vivian Gentille, Senior Manager, Bradesco

This is how the reputation moat is renewed in everyday operations. Better intelligence surfaces exceptions and informs oversight. Stronger controls make responsible use of intelligence possible in more consequential workflows. Trust and intelligence reinforce each other when institutions test that relationship in practice, measuring decision quality, control effectiveness, and client outcomes alongside productivity.

Empower banking advisors and client-facing teams

The distribution moat is not a larger set of channels. It is the ability to win every client moment: to show up with the right context, at the right moment, in a way that earns the next conversation. For a corporate banker, that means connecting a client’s operating needs with treasury expertise. For a wealth advisor, it means bringing relevant research and approved product information into a discussion of long-term goals.

Morningstar uses Microsoft 365 Copilot and Copilot Studio to bring its trusted research and investment intelligence into advisors’ existing workflows, helping them create research-backed portfolio insights and client-ready materials faster.

Microsoft Copilot and Copilot Studio create a powerful ecosystem. It’s plug-and-play access. You can connect tools that fuel the full advisor lifecycle with AI, creating agents to support discovery and research, portfolio construction, monitoring, and client communication, while grounding AI workflows in trusted data, research, and investment IP.

—Thomas Aviles, Head of Advisor Software, Morningstar

As clients move between digital interactions and human advice, consistency matters as much as personalization. A service agent, a relationship manager, and a specialist should work from compatible, permissioned context rather than ask the client to reconstruct their story. Connecting experiences must not dissolve confidentiality barriers or become indiscriminate cross-selling. Distribution strengthens reputation only when clients can see that their interests, choices, and privacy remain central.

How banks can prepare for the agentic era

For leaders, the implication is straightforward: do not begin with the model. Begin with the outcome. Identify the client, market, operating, or risk outcome that matters most. Determine what proprietary intelligence the institution needs to own. Map the value chain where that intelligence must move. Define which decisions remain human-led. Then build the data, governance, security, and workflow foundation required to scale.

At Sibos 2026, where the industry is focused on trust, cross-border value, AI-powered markets, and the future of financial infrastructure, this conversation is especially timely. The opportunity is no longer to prove that AI can help with individual tasks. It is to build financial institutions that can operationalize intelligence safely, responsibly, and at scale.

The winners will be those that bank on intelligence—not as a feature, but as a trusted institutional capability.

For more on building the banking and capital markets institution of the agentic era, watch my FinTech Futures Sibos interview.

Watch this video for a look back at Microsoft’s week at Sibos—and a look ahead at how financial institutions can turn AI ambition into real business impact.

The post Bank on intelligence: Building a trusted financial institution for the agentic era appeared first on The Microsoft Cloud Blog.

An AI-enabled investment platform connects governed data, institutional knowledge, investment workflows, and AI agents under human oversight so capital markets teams can make faster, better-informed decisions. Rather than adding isolated AI tools, firms can use this platform approach to modernize work across research, due diligence, portfolio analysis, risk, compliance, and client service while preserving accountability and control.

Most AI initiatives in investment management begin with point solutions for research, due diligence, reporting, document review, portfolio analysis, or client service. However, these applications represent only the first stage. The larger opportunity is to redesign the investment lifecycle itself so that information, decisions, controls, and execution can flow continuously across the full ecosystem, built around persistent context.

Nowhere is this truer than in capital markets, where the explosive growth of private markets and the convergence of public and private asset distribution have created a complex, multi-stakeholder ecosystem characterized by intense data dependencies, operational complexity, and regulatory scrutiny.

Major global capital markets participants—from large asset managers to investment banks, sovereign wealth funds, and wealth managers serving high-net-worth clients—all seek integrated technology solutions that can unify data, accelerate AI-powered decision support, and help collapse fragmented workflows while ensuring regulatory compliance. Agentic AI can help orchestrate work across these stages, reducing manual handoffs and enabling more intelligent, responsive business processes.

As policymakers and market participants explore broader access to private-market investments, the industry faces a significant scale challenge. Legacy approaches to capital formation, allocation, and distribution will not support a global alternatives market projected to reach $32 trillion in assets under management (AUM) by 2030 unless firms transition to integrated, scalable operating models.¹

Why capital markets firms need more than isolated AI use cases

As AI becomes embedded across capital markets, value increasingly comes from connecting people, data, and processes across the broader ecosystem. This is why the strategic challenge is not deploying more AI tools, but rather creating an end-to-end, integrated platform that enables firms to leverage shared value while protecting core intellectual property.

The firms that succeed will treat AI as a platform for coordination, rather than isolated intelligence. By enabling secure collaboration across participants while preserving governance and accountability, institutional knowledge compounds, which strengthens each insight and decision over time.

What an AI-enabled investment platform should support

At its core, an AI-enabled investment platform should do three things:

1. Connect governed data and work context across portfolio data, market intelligence, research, operating metrics, client information, risk analysis, legal documentation, and internal knowledge. Bringing this context together in a governed environment can reduce fragmentation and create a more consistent foundation for decision support. It can also help teams understand how a conclusion was reached, what sources informed the work, and where human review remains necessary—an approach LSEG is putting into practice by using Microsoft Fabric to unify 30 systems and 1,200 datasets in a governed platform, improving data quality and reducing new-product development timelines from years to months.
   
2. Support reusable expertise and workflows. Organizations develop advantage through their investment processes, institutional knowledge, relationships, and accumulated experience. Reusable AI-assisted capabilities can help teams scale that expertise more consistently across research, portfolio management, investor engagement, compliance, and distribution activities, while keeping people responsible for material decisions.
   
3. Maintain trust, accountability, and control. As AI becomes more deeply embedded in investment workflows, identity, permissioning, auditability, data protection, lifecycle management, and policy controls need to move alongside information rather than being added later as separate layers. Suitability and explainability of investment decisions and advice must be built in by default.
   

This is how Microsoft’s AI platform approach becomes relevant. Microsoft IQ can provide governed context that agents can readily access within applicable permissions, while Microsoft 365 Copilot brings intelligence into familiar workflows. Microsoft Foundry and Copilot Studio help organizations build and manage reusable AI agents, matching each task with an appropriate model and cost profile. For regulated industries, that flexibility must be matched by strong controls. Microsoft Purview, Microsoft Entra, and Agent 365 support the governance, identity, security, and observability requirements needed to deploy agents responsibly at scale.

How agentic AI can redesign investment operations

1. From origination to investment committee

Investment context is often recreated as opportunities move through sourcing, diligence, risk and legal reviews, and investment committee preparation. An intelligence layer helps maintain a governed, permissioned view of information across that lifecycle, bringing together investment data, research and diligence materials, proprietary firm knowledge, and approved external market data as shared context rather than isolated artifacts.

AI-assisted capabilities surface in the flow of work such as in Microsoft 365 Word or Excel, rather than compelling users to switch applications. Copilot then becomes the orchestration surface: a deal-screening agent can pull private-company signals from PitchBook, while a valuation-review agent can cross-check against prior assessments, identify supporting evidence, and prepare materials for review. Investment decisions remain with people, but teams spend less time assembling information and more time evaluating it.

Genworth demonstrates the potential in investment operations, using Microsoft 365 Copilot to reduce the time required to construct complex portfolio trades from days to hours while strengthening decision support within existing workflows. The result is better decision-readiness, fewer manual handoffs, and a clearer institutional record that can be reused when similar questions, risks, or opportunities arise.

2. Creating a stronger organizational learning loop

The investment lifecycle does not end when a decision is made. Portfolio performance, advisor feedback, investor demand, compliance reviews, operating results, and market developments all generate valuable intelligence, but many firms struggle to make those signals reusable across future decisions. Important insights often remain buried within meeting notes, presentations, email threads, and individual workflows.

With a foundation of intelligence and shared context, decisions that traditionally took weeks to propagate through investment, risk, operations, and distribution functions can become immediately available. Over time, that can make the firm’s experience more durable: patterns from prior investments, recurring client questions, risk signals, and operational lessons can become part of the next workflow rather than remaining isolated in the last one. Morningstar illustrates this model by extending trusted research into reusable, governed agents within advisor workflows, reducing hours of preparation to minutes while keeping intelligence grounded in approved data.

As Martina Cheung, CEO of S&P Global, noted in her recent LinkedIn post, with S&P Global’s data, insights and analytics in Microsoft 365 Copilot workflows, an analyst, for example, can ask Microsoft Copilot for a company’s latest EBITDA trends, market capitalization or peer comparisons, with answers grounded in cited S&P Global intelligence. From there, that data can flow into everyday workflows, whether that’s building a comps table in Excel or preparing a competitive landscape deck in PowerPoint.

3. Collaborating without compromising confidentiality

Investment due diligence relies on coordination across teams. That collaboration requires access to information, but it also requires strong controls around confidentiality, entitlements, licensing requirements, and regulatory obligations. In practice, AI-assisted workflows can help drive scale while respecting organizational policies and access boundaries. That is especially important when workflows span internal teams, external partners, and data from different licensing or confidentiality contexts. As firms scale AI adoption, this balance between collaboration and control becomes increasingly important.

Making collaboration possible without weakening control depends on governance that travels with the information. Identity, permissioning, data protection, auditing, and policy enforcement should ensure that people and AI systems can access only the data they are authorized to use. Nasdaq Boardvantage demonstrates this principle in a particularly sensitive context: it uses AI to support confidential board workflows while preserving established security, access, and governance controls. The approach also delivers measurable efficiency gains, reducing directors’ reading time by up to 60% and overall board preparation time by 25%.

How governed data and human oversight build trust

Capital markets organizations have always competed through expertise, relationships, judgment, and privileged information. In the AI era, those advantages will increasingly depend on how effectively firms connect proprietary data, institutional memory, governance, and human expertise. These characteristics make them distinctive while creating value across the broader ecosystem.

Achieving that requires a platform that connects data, workflows, controls, and institutional knowledge without compromising accountability or trust. The goal is not to replace investment professionals, but to give them better context, stronger governance, and greater leverage across increasingly complex public and private markets. The organizations that lead in the AI era will be those that combine trusted data, human expertise, agentic workflows, and strong governance to improve decision-making across the enterprise.

In this future, the AI-enabled investment platform is more than infrastructure. It is the operating foundation through which investment knowledge is created, governed, and applied at scale.

The next competitive advantage in capital markets

• For a practical roadmap on how to turn isolated AI initiatives into enterprise-wide impact, explore Frontier Transformation in Capital Markets.

• For insights on bringing context into flow of work, read our blog, “AI in financial services: Bringing trusted data into the flow of work.”

• To learn more about scaling agentic AI in asset management with “Trust as infrastructure: How agentic AI is rearchitecting asset management at scale.”

Frequently asked questions

What is an AI-enabled investment platform?

An AI-enabled investment platform connects governed data, institutional knowledge, investment workflows, controls, and AI agents in a unified environment. It gives teams trusted context and reusable capabilities across the investment lifecycle while keeping people accountable for material decisions.

How is agentic AI used in capital markets?

Agentic AI can orchestrate multi-step workflows across research, due diligence, investment committee preparation, risk, compliance, and client service. Agents can gather approved information, analyze evidence, prepare materials, and coordinate handoffs, while investment professionals retain responsibility for review and decisions.

How can capital markets firms use AI while protecting confidential data?

Capital markets firms can protect confidential data by applying identity, permissions, data protection, auditing, and policy controls wherever information and AI agents are used. These safeguards limit access to authorized people and systems, while explainability and human oversight help ensure accountable decisions.

What should firms prioritize when modernizing investment operations with AI?

Firms should prioritize governed data, reusable workflows, clear accountability, and measurable business outcomes rather than deploying disconnected tools. A staged approach can begin with high-value use cases, establish controls and success measures, and then scale proven capabilities across investment operations.

¹ Preqin, “Private Markets in 2030: Data Driving the Future of Alternatives,” October 16, 2025.

The post The AI-enabled investment platform: How capital markets firms can redesign the operating model appeared first on The Microsoft Cloud Blog.

It is thus the natural evolution of Microsoft’s role that HM Treasury has designated Microsoft Ireland Operations Limited (MIOL) as a critical third party to the UK financial sector, a designation publicly announced on July 10, 2026.¹ MIOL recognizes its responsibilities as a critical third party and is fully committed to complying with the applicable oversight requirements and the UK’s cybersecurity and resilience laws. As with its designation as a Critical Third Party Service Provider under the EU Digital Operational Resilience Act, MIOL will continue to focus its responsibilities on supporting the financial services industry and enhancing overall operational resilience of the financial ecosystem.

Understanding the UK critical third parties regime

The CTP regime created under the Financial Services and Markets Act 2023, gives HM Treasury the power to designate third-party service providers as “critical” to the UK financial sector, and gives the Bank of England, the Prudential Regulation Authority (PRA), and the Financial Conduct Authority (FCA), collectively “the regulators,” the ability to set rules for, gather information from, and oversee those critical third party providers.

The regime exists because financial firms increasingly rely on a small number of third parties for services that are critical and important to their business operations. The regulators’ aim is to manage the risk that a failure in, or disruption to, one of those services could affect many firms at once and, in turn, threaten the stability of the wider financial system. Notably, the UK regime is technology-neutral (it is not limited to cloud providers), and designation is made by HM Treasury on the recommendation of the regulators.

What the designation means and what it does not

As a designated critical third party, MIOL is subject to direct oversight by the regulators in relation to the services identified as systemic. In practice, this involves engagement with the regulators under a set of fundamental rules, alongside expectations in areas such as self-assessment, scenario testing, incident management, and incident reporting. A core principle of the regime is that a critical third party should deal with the regulators in an open and cooperative way, an approach MIOL fully embraces as a critical third party under the UK CTP regime.

It is equally important to be clear about what the designation does not change. As with the EU’s DORA regime, financial firms remain primarily accountable as regulated institutions. As set forth in Supervisory Statement SS6/24:²

The CTP duties complement the requirements and expectations for firms on operational resilience, outsourcing and third party risk management. The CTP oversight regime sits alongside these requirements and expectations but does not eliminate, reduce nor replace the accountability of firms, their boards and senior management (including individuals performing SMFs).

Microsoft, as a technology provider, is committed to meeting its own obligations and helping its customers meet theirs, while the regulatory responsibilities of a financial institution remain with the institution. For our customers, it is business as usual: there are no material changes to the Microsoft services they rely on today.

Supporting our customers’ operational resilience

Microsoft has long invested in the security, compliance, and resilience of its cloud platform, and that investment is what enables us to support financial institutions as they meet their own regulatory obligations. We take a global, scaled approach to these requirements, ensuring that customers can have confidence that consistent operational and security controls apply wherever they operate. The capabilities most relevant to operational resilience include:

• Continuous threat monitoring with Microsoft Sentinel: real-time threat detection and continuous security monitoring, with automated incident handling and evidence workflows that support operational-resilience expectations.
• Threat protection with Microsoft Defender XDR: cross-platform threat protection and advanced response capabilities.
• Governance and compliance management with Microsoft Purview Compliance Manager: regulatory assessment templates that help organizations assess and track compliance across Microsoft cloud services.
• Compliance and policy enforcement with Azure Policy: compliance monitoring and policy adherence across hybrid and multicloud environments.
• Lifecycle security and compliance management with Microsoft Unified: helps operationalize incident management and resilience controls aligned with regulatory expectations.

For customers with deeper compliance assurance needs, Compliance for Microsoft Cloud (EDE) is an optional Microsoft Unified Support add-on that provides a dedicated engineer focused on compliance-related scenarios, helping customers interpret Microsoft controls and support regulatory and assurance discussions. These tools are designed to help customers strengthen their own resilience posture; they do not transfer or discharge a firm’s regulatory responsibilities, which remain with the firm.

A consistent, global approach

This is not Microsoft’s first such designation. MIOL was designated a critical Information and Communication Technology (ICT) third party service provider in November 2025. Under the European Union’s Digital Operational Resilience Act (DORA), regulators have direct oversight of companies identified as critical third-party providers (including MIOL).

The UK regulators have designed their regime to be compatible with similar approaches in other jurisdictions, and Microsoft’s goal is to meet these expectations consistently in the provisioning of the systemic services, engaging constructively with regulators and maintaining the resilience of the services on which our customers depend.

Our commitment

Microsoft is fully committed to complying with the UK’s operational resilience requirements and to cooperating with the regulators. We will continue to focus on earning and maintaining the trust of financial institutions and investing in the resilience of the platform that underpins their most critical workloads. We will keep our customers and partners informed as the regime continues to take shape.

Explore how Microsoft helps financial leaders navigate regulatory requirements

• Explore how Microsoft helps financial leaders build trust, apply AI responsibly, and turn data into more confident decisions in our Trust and Intelligence in Financial Services e-book.
• Learn more about Microsoft’s platform strategy for regulated financial services: Microsoft for Financial Services
• See how Microsoft frames regulatory compliance as a long-term strategic challenge: Compliance Overview

Learn More

UK regulatory sources

• Bank of England: Critical Third Parties (CTPs)
• Bank of England SS6/24: Critical third parties to the UK financial sector
• FCA PS24/16: Operational resilience: Critical third parties to the UK financial sector
• HM Treasury: Approach to Designating Critical Third Parties
• Outsourcing and third party risk management

Related Microsoft perspectives

• As regulation intensifies, Microsoft helps financial leaders meet growing demands (Apr 2026)
• 3 ways Microsoft is helping the financial industry prepare for new DORA regulations (Sep 2024)

¹ – UK financial regulators to begin overseeing Critical Third Parties announced by HM Treasury | Bank of England

² – SS6/24 – Critical third parties to the UK financial sector | Bank of England

The post Microsoft designated a Critical Third Party to the UK financial sector appeared first on The Microsoft Cloud Blog.

The shift is already underway. Major financial institutions are quickly moving beyond limited use cases toward agentic AI solutions designed to help drive impact at scale. Frontier Firms—organizations reimagining business processes around human-agent collaboration—are integrating intelligence into the workflows, data environments, and governed systems where decisions are made and work gets done.

The challenge is that in the heavily regulated constraints of financial services, access to data is tightly controlled, critical data is often fragmented across legacy platforms, and workflows span multiple systems, teams, and jurisdictions. Internal data must be integrated with external data, including market data, research, and third-party insights. Firms must be certain that the right people can access the right data in the right ways, while keeping it all safe.

Our partnership with Microsoft is shaping the next era of AI ready data and intelligence‑driven workflows. We are meeting clients where they work—embedding S&P Global’s trusted, high‑quality data directly into their workflows and unlocking agentic capabilities that turn insight into action, enabling accurate, faster, more-informed decision‑making. Together, we are accelerating how clients drive growth, manage risk, and capture opportunity in an increasingly complex and fast‑moving market.

Sally Moore, Chief Client Officer, S&P Global

Trust is paramount, and the best way to scale AI is to leverage the inherent strengths and attributes of a trusted cloud environment and the applications that professionals use to do their work. This way, data used in agentic solutions can be configured to inherit existing permissions, licensing constraints, and governance controls, rather than requiring firms to rebuild trust around new layers.

This requires viewing AI as an operating capability that unifies data, governance, tools, and workflows, enabling intelligence to show up where work happens. Approaches that simplify access to critical data and embed AI into everyday decisions and actions are best positioned to succeed.

Putting AI at the point of work—where data, decisions, and action come together

Financial work rarely happens in a single system. Professionals move across data, spreadsheets, meetings, messages, and business applications, in scenarios that span all aspects of financial services. To name just a few:

• A banker preparing for a client meeting needs more than a market summary; they need relevant signals, prior context, comparable activity, potential risks, and a path to the next best action.
• A risk professional needs intelligence that can be applied within existing controls, not outside them.
• An investment analyst needs current financial data in the spreadsheet where assumptions are being tested, not a separate answer that has to be copied and reconciled later.

AI creates more value when it is embedded in workflows across the value chain, helping to reduce handoffs, preserve context, and close the gap between analysis and action. AI that sits apart from the workflow can generate useful responses, but it still requires that data be copied, reconciled, and validated across systems.

The next phase of AI in financial services is about connected intelligence that brings the right data into the right context at the right moment. Our connectors ensure Morningstar’s independent research and trusted data sit alongside firms’ own data within their workflows, enabling investment professionals to generate deeper insights, move with greater speed, and make more confident decisions with clarity and control across the entire investment process.

Adam Wheat, Chief Technology Officer for Direct Platform, Morningstar

Our focus is to narrow the distance between trusted information, professional judgment, and the action that follows—in other words, to enable governed AI execution in context, allowing people to move from insight to action within familiar tools. This is why we, and our partners, are bringing financial data and AI capabilities directly into the everyday applications and tools many professionals use to get work done, including Microsoft Excel, Teams, Outlook, and Microsoft 365 Copilot. It enables intelligence that can be accessed in the spreadsheet where analysis is being built, in the meeting where a client discussion is being prepared, or in the collaborative space where teams coordinate the next step.

The platform for advanced AI experiences

For financial institutions, the next model for AI begins with the data that anchors the business: market intelligence, risk signals, research, client information, transaction context, policies, and operational knowledge.

Microsoft’s approach brings together trusted data, enterprise context, and intelligence on one integrated platform. It spans a rich ecosystem of financial services data providers, helps to orchestrate models, and is built upon the core strengths of the Microsoft Cloud: enterprise-grade security, compliance, and governance.

In this approach, the following key features work together to connect trusted data, enterprise context, and AI-powered capabilities:

• Federated connectors provide live data by accessing information without moving or copying it.
• Synced connectors provide enterprise data by indexing content from business and partner systems.
• Microsoft IQ provides context by understanding how people work, how the business operates, what the organization knows, and how the broader business and market environment is changing.
• Skills provide expertise by defining how specific business tasks are performed.
• Plugins combine connectors and skills to package data access and task expertise into reusable capabilities.
• Copilot Cowork helps coordinate and assist with tasks across systems, people, and workflows—bringing agentic capabilities into Microsoft 365 Copilot and, by extension, into Excel, Word, PowerPoint, and Outlook.

Together, these features empower financial services professionals to ask more precise questions, receive more relevant answers, and take action directly within their workflows, all while supporting compliance efforts and helping preserve context.

Expanding the ecosystem of financial data providers

In recent months, we’ve unveiled this strategy through milestones that reveal an expanding ecosystem of financial data providers and capabilities.

Federated Copilot connectors extend Copilot’s reach by securely integrating external real-time data directly into Copilot in Excel, Copilot Chat, and Researcher agent. Recently, we announced federated Copilot connectors from LSEG and Moody’s, and the momentum continues today with new federated Copilot connectors from CB Insights, Daloopa, FactSet (in preview), Morningstar, PitchBook, and S&P Global. Together, these integrations can provide access to up-to-date market data, company intelligence, research, portfolio analytics, investment data and more—within supported workflows, subject to provider availability, licensing, and system integration.

As financial institutions scale AI, the combination of trusted data and enterprise platforms becomes critical. Our collaboration with Microsoft enables clients to bring LSEG’s data into AI-powered workflows, supporting more consistent, informed decision-making across their business.

Emily Prince, Group Head of Enterprise AI, LSEG

Copilot Cowork plugins embed specialized partner expertise directly into task-oriented workflows, enabling Copilot to support role-specific financial analysis and decision-making. Building on the recent launch of Copilot Cowork plugins from LSEG and S&P Global Energy, we’ve now expanded the list of partners with Copilot Cowork plugins from CB Insights, Moody’s, Morningstar, and PitchBook. These plugins package proprietary data, models, and domain knowledge within defined workflows, depending on partner capabilities and implementation, allowing financial services professionals to move more readily from insight to action.

The defining question for financial institutions today isn’t whether to use AI, it’s whether they can trust the intelligence powering it. From Microsoft 365 Copilot to Excel to Copilot Cowork, Moody’s is embedding its decision-grade connected intelligence directly into the Microsoft workflows where consequential decisions are made every single day, allowing our customers to move with greater confidence and speed without sacrificing the rigor, transparency and accountability that high-stakes decisions demand. 

Cristina Pieretti, Head of Digital Content and Innovation, Moody’s

We’ve also announced new skills and capabilities for Copilot in Excel, one of the most widely used tools in financial services. In addition, we’ve introduced a starter library of pre-built finance skills, alongside the ability for organizations to create and integrate their own. Combined with new customization and control features, these enhancements enable financial services professionals to customize their workflows, apply consistent logic, and scale analysis more efficiently.

This integrated platform supports a shift from information retrieval to governed execution. For firms and institutions, this can mean more efficient preparation, more consistent use of trusted information, less manual stitching across applications, and a stronger connection between insight and execution.

Trust as the foundation

For financial institutions, this shift can only succeed if trust is built into the architecture. Organizations must be able to innovate without separating intelligence from the controls that make enterprise adoption possible.

This is where Agent 365 makes the trust model tangible. It lets organizations see which agents are operating, understand what data they are using, monitor the actions they take, and apply policies that govern how they work. It also supports the traceability and citations institutions require to understand how data was used, what sources informed an output, and how agent-driven work was generated.

As agentic workflows become the new standard for how analysis gets done, the data underneath must be trustworthy and verifiable. Through Kensho’s AI innovation and S&P Global’s domain expertise, we’ve done that work, creating a trusted data retrieval foundation designed for how AI systems reason and act. Our partnership with Microsoft is about bringing that foundation into the AI-native experiences where the industry is already headed.

Bhavesh Dayalji, Chief AI Officer, S&P Global and CEO, S&P Kensho

The same environment that brings data and AI closer to work should inherently provide the capabilities required to use agents responsibly at scale. In regulated industries, this combination is a material advantage: firms should not have to choose between moving faster with AI versus maintaining control over how data and actions are managed.

For financial services leaders, the next phase of AI will not be defined by isolated experiments, disconnected tools, or AI architectures that require firms to add a separate layer of trust, permissions, and context on top of the systems they already use. As firms move from experimentation to scaled adoption, Microsoft’s advantage is the ability to combine trusted data, context, workflow integration, and governance in one coherent operating model for AI, allowing firms to leverage the platforms they already depend on.

Learn more

• Learn how agentic AI is rearchitecting asset management at scale.
• For a practical framework on using AI to drive coordinated, enterprise-scale impact across the firm, read Frontier Transformation in Capital Markets.
• Visit Microsoft for Financial Services to learn more about how Microsoft empowers financial services organizations to achieve more.

The post AI in financial services: Bringing trusted data into the flow of work appeared first on The Microsoft Cloud Blog.

With the global banking system intermediating hundreds of trillions of dollars in funds, corporate and commercial banking sits at the center of the global economy.¹ It spans geographies, regulatory regimes, client types, and balance sheet structures. And critically, it relies on decision‑making that unfolds across extended, non‑linear processes.

Where past transformation efforts often fell short in accelerating decision-making and streamlining cumbersome processes, the emergence of agentic AI is changing what is possible. Combined with enterprise‑grade platforms, new AI capabilities are being designed to help coordinate work securely, governably, and at scale.

This shift is best understood through four ways that AI is starting to reshape how corporate and commercial banking can operate.

1. Empowering relationship managers

Relationship managers have long been the lifeblood of positive client experiences, often serving as the trusted human face of the business. In this role, the ability to synthesize insights from complexity is a critical skill. Yet even the best managers often struggle to manage the growing sprawl of data and requirements.

In many cases, relationship managers function as human middleware—responsible for manually aligning information that the organization itself cannot easily connect as environments become more fragmented across treasury and credit/lending systems, client data, risk platforms, documents, and collaboration across channels.

Agentic AI addresses this by shifting from passive or responsive assistance to active work coordination. AI-powered agents can assist in monitoring where a client, deal, or request sits across multiple systems, identify what is missing, and surface the right context at the moment decisions are made.

Standard Chartered equipped more than 6,000 bankers with a unified platform spanning 53 markets, giving relationship managers real-time insights and more time to spend on client-centered engagement. Likewise, UBS deployed Microsoft Copilot across its employee base (including relationship managers), transforming legal research with an AI assistant that surfaces precise clauses across 26 million documents using natural language queries. The system eliminates manual search and accelerates information retrieval, freeing experts to focus on judgment-intensive work and giving relationship managers more time for client interactions.

The result is not “AI advice,” but improved decision readiness for teams. Relationship managers spend less time assembling inputs and more time applying expertise, consistently, audibly, and earlier in the process.

2. Improving the quality of client interactions

Clients do not experience the bank as a set of systems. They experience it as a series of moments across onboarding, credit, treasury, and servicing. Too often, these moments are disconnected, which creates friction and can erode confidence.

Agentic AI helps close these gaps not just by connecting interactions, but by making them more meaningful. AI agents enable financial professionals to understand the client in context by maintaining continuity across all channels, while also helping anticipate needs, surface relevant insights, and making sure requests move forward without repeated inputs or dropped handoffs.

Commerzbank, for example, built an AI agent that now handles more than 30,000 customer conversations per month, resolving approximately 75% of requests autonomously. By maintaining shared context across interactions and orchestrating workflows in real time, the bank reduces the need for customers to repeat information while ensuring requests move forward without disruption. The result is a more consistent, responsive experience at scale.

AI also helps bankers maintain continuity across interactions and generate consistent, empathetic responses by unifying previously siloed workflows in sales, service, and communications. First National Bank is using Copilot for Sales to reduce fragmented touchpoints and strengthen ongoing, relationship-driven engagement with commercial clients across channels and teams.

This extends across other complex and high-stakes moments in banking. In investment banking, for example, AI-assisted meeting preparation pulls together internal context and external market data, so bankers walk into conversations already aligned to the client’s situation. In corporate and commercial banking, onboarding and KYC agents can intelligently scan documents, cross-reference sanctions lists and adverse media, and surface key findings for relationship managers.

This can turn what has traditionally been a weeks-long, manually intensive process into a more efficient and consistent experience for both the bank and the client. 

The result is a shift from fragmented, episodic service to coordinated, proactive engagement, where interactions are more timely, more relevant, and more aligned to the client’s evolving objectives.

3. Modernizing risk and core systems

Banking operations have rarely struggled because of a lack of automation alone. They often struggle because complexity compounds.

Credit assessment depends on risk inputs, risk depends on documentation, documentation depends on counterparties, onboarding depends on compliance and core systems rely on all of the above. These processes span teams, systems, and timelines that were often not designed to operate in sync, and as a result work can slow down, fragment, and stall.

Agentic AI helps address these challenges by not only automating tasks but also coordinating workflows across stages. It can track progress across processes, resolve handoffs dynamically, and surface exceptions to the right experts when judgment is required, reducing friction by connecting workflows, aligning dependencies, and helping work move more smoothly across systems and teams.

Bank of Queensland, for example, used Microsoft Copilot to streamline complex, multi-step workflows, reducing risk analysis from weeks to a single day while improving quality by 22%. By automating document-intensive tasks and simplifying cross-functional coordination, teams accelerated decision-making and enabled more adaptive, efficient execution across core banking processes.

At an ecosystem level, Swift applied federated learning and confidential computing to detect financial crime across institutions without centralizing data, which enabled more coordinated risk modeling while preserving privacy and regulatory boundaries.

At Scotiabank, rather than replacing core systems, the bank embedded AI agents into its payment operations to coordinate data transformation, reconciliation, and exception handling across fragmented workflows. As a result, processes that once required weeks can now be executed in seconds, with clear auditability and control.²

When processes are streamlined and coordination costs fall, measurable outcomes can improve. Faster credit decisions, strengthened compliance, improved reconciliation and materially better banker and borrower experiences are enabled, without requiring wholesale system replacement.

4. Transforming trade finance

Innovation in corporate and commercial banking typically falls short because complexity has historically resisted scale.

In trade finance, structured lending, and cross-border treasury, work spans multiple stakeholders, documents, and exceptions across boundaries. These are not linear processes, but complex, judgment-driven environments where traditional automation can break down.

The shift enabled by agentic AI is reimagining how these processes operate. Instead of forcing complexity into fixed workflows, banks can execute work adaptively, coordinating across participants in real time and responding to exceptions as they arise.

Agentic systems open the door to different operating models. They can address deviations, help route actions to the right specialists, and capture how complex work is completed. Over time, these patterns can be encoded into reusable playbooks, so banks can scale expertise, deliver more consistent outcomes, and maintain continuity across complex, multi-party processes.

A glimpse of this future can be seen in a Microsoft-led trade finance proof of concept with ANZ, HSBC, and Lloyds. By embedding AI agents within ERP systems, the solution parses letters of credit, cross-checks them against invoice and shipping data, flags discrepancies, and securely transmits structured, standards-aligned information to bank platforms. Rather than simply accelerating existing steps, it demonstrates how trade workflows can evolve toward a more consistent, data-driven model, ultimately reducing fragmentation, minimizing manual rekeying, and improving traceability across trade finance processes.

What emerges here is not just more efficient execution, but a potentially fundamental new way of operating, one in which complex, exception-heavy processes can be coordinated, governed, and continuously improved at scale.

Realizing this shift depends on enterprise-grade AI platforms that combine advanced AI models, secure integration across systems and partners, and scalable data and analytics foundations.

Leading institutions are using GenAI to streamline trade finance and drive the shift from paper-based to digital platforms.

From experimentation to operating advantage

Experienced banking leaders are right to approach AI with caution. The industry has seen many moments where enthusiasm outpaced operational reality. What differentiates this phase is not model capability alone, but fit. Agentic AI aligns with how corporate and commercial banking functions. It streamlines key processes, respects variability, reacts to change, and reduces coordination costs rather than oversimplifying work.

Equally important, platforms matter. Without security, governance, and integration at enterprise scale, AI coordination becomes a risk rather than an advantage.

This is where disciplined adoption—not experimentation for its own sake—will separate leaders from laggards.

Learn more

• For a practical playbook to improve customer experiences, embed AI in high‑value workflows, and act on the shift to agentic AI, read The Microsoft Business Blueprint for Agentic Assistants.
• For a practical blueprint to scale AI, drive productivity, and deploy across banking, insurance, and capital markets, read Frontier Transformation: AI in Financial Services.
• To help unlock business value and deepen customer relationships in the era of AI, visit Microsoft for Financial Services.

¹ McKinsey & Company, “Global Banking Annual Review 2025,” October 23, 2025.

² Scotiabank, “How AI Agents are Transforming Scotiabank’s Payment Operations,” September 17, 2025.

The post How AI is reshaping corporate and commercial banking appeared first on The Microsoft Cloud Blog.

This momentum is changing not only how insights are generated, but also how decisions are prepared, governed, and executed. As Microsoft Build 2026 made clear, the next phase of agentic AI is not just better outputs. It is governed execution grounded in enterprise context, with systems designed to coordinate action across research, portfolio construction, risk, and compliance under human supervision. For asset managers, this shift is profound.

This is not simply a technology transition; it is a fiduciary moment. For asset managers, Microsoft’s Frontier Tuning points toward institution-shaped intelligence: models tuned not only on enterprise knowledge, but on the workflows, conventions, and decision patterns that define how a firm operates inside its compliance boundary. In a fiduciary setting, that is indispensable because trust depends not just on what AI knows, but on whether its outputs and actions align with approved controls, human oversight, and reproducible governance.

The critical challenge, then, is to maintain trust. AI cannot merely improve performance; its actions must be governed, explainable, and attributable in line with fiduciary duty.

From analytics to homeostasis: AI as a trust-control layer

In biology, homeostasis is how living systems stay stable by sensing change, making small corrections, and restoring balance. In asset management, agentic AI makes it possible to build a similar closed-loop layer across workflows defined by governance parameters, helping keep activity within agreed boundaries of risk appetite, policy, and intent even when markets move faster than humans can triage.

Historically, AI in asset management has centered on prediction, such as forecasting spreads, detecting anomalies, or scoring counterparties. Agentic systems add the ability to observe, decide, and act across workflows under human guidance, gathering evidence, reconciling sources, drafting outputs, escalating exceptions, and coordinating next steps. Used well, these systems can create a homeostatic effect, helping firms maintain operational equilibrium and resilience.

The shift to a homeostatic approach

AI is being used in capital markets not only to deliver new capabilities but to help strengthen trust as a cornerstone of infrastructure. Trust can be infused throughout the platform by design, helping to ensure that data and operations remain safe, responsive, and governed even in fast-changing conditions.

In trading workflows, for example, the LSEG and Microsoft partnership is integrating licensed market data into Microsoft 365 Copilot and agent experiences so that decisions can be informed with authoritative, permissioned context at the point of execution. This is increasingly the right pattern for agentic systems: not intelligence separated from governance, but intelligence grounded in trusted context and controlled inside the systems where work happens.

By combining licensed data with built-in governance capabilities, trust is embedded into how intelligence is delivered, orchestrated, and acted on across the workflow, rather than managed downstream.

Likewise, Moody’s has federated decision-grade credit intelligence directly into Microsoft 365 Copilot, Researcher, and Excel, grounding AI-assisted analysis in authoritative, auditable context so that trust can be enforced by the system, not retrofitted after the fact.² Similarly, Morningstar has embedded proprietary research as entitlement-aware context for copilots and agents, keeping advisor workflows reviewable and aligned to licensed sources and user permissions. UBS is also bringing together internal and market data into AI-assisted advisor workflows, enabling client advisors to access unified, permissioned insights in real time.

Further, at Nasdaq, AI is being applied directly into the boardroom experience while preserving the strict controls those workflows demand. Its Nasdaq Boardvantage platform helps members turn hundreds of pages of board materials into concise, decision‑ready insights. This reduces review time by up to 60% while ensuring that outputs are fully auditable, remain grounded in proprietary data, and are protected by design.

Together, these examples show that intelligence can scale when workflows are auditable, permissioned, and attributable by default.

Getting started: Three moves asset managers can make now

The path forward is not theoretical; it is operational. Asset management firms can begin by focusing on three priorities:

• Invest in data readiness. Start by unifying internal and external datasets into a governed data foundation that can support agentic AI. Platforms like Microsoft Fabric are increasingly designed not only to bring data together, but to create the shared business context that agents need to operate consistently across teams and workflows. That matters because the bottleneck in scaling agents is no longer just model capability. It is whether each system can access the same governed definitions, permissions, and operational data foundation needed to move from isolated experiments to production-ready agent systems.

• Operationalize governance and enterprise ontology. Define ownership, approval paths, and audit checkpoints directly into AI-enhanced workflows under human supervision. A broader contextual intelligence layer is now emerging to connect how people work, how the business is modeled, and how knowledge is grounded across enterprise systems. Within that picture, Microsoft Work IQ remains an important component, helping make sense of activity across emails, meetings, documents, and chats while honoring existing permissions, sensitivity labels, and governance controls. Applied consistently in AI-assisted experiences, this kind of shared context helps turn governance from a static framework into a living system embedded in everyday work.

• Enable model-agnostic intelligence. Explore the comprehensive catalog of AI models so that you can select the best ones as needs evolve. Microsoft Foundry increasingly represents the production layer for agents, bringing together models, hosted agent services, memory, observability, evaluation, and guardrails in one governed environment. That gives firms more flexibility as requirements change while helping them move from pilot use cases to durable, production-scale systems.

Once these foundations are in place, firms can layer agentic workflows across research, operations, and risk while keeping clear human ownership at every material decision point and standardizing evidence trails so material outputs are reproducible and traceable. The goal is not automation alone, but structural operating leverage that scales intelligence with observability, governance, and security.

Making trust measurable and operational

Trust is not abstract in capital markets; it is the product of repeatable controls including lineage, entitlements, validation, and oversight. In practice, this is increasingly operationalized within the systems where work already happens. The idea is to bring productivity, security, identity, and agent management into a more unified operational layer, so that trust-related controls are enforced upstream in the flow of work rather than applied after the fact. Microsoft 365 E7 for Enterprise is one example of this broader architectural direction.

Accordingly, many firms focus on AI-enabled workflows defined by a common set of “trust signals,” including:

• Traceability: Every output must link back to authorized data, policies, and model versions.
• Explainability: Decisions must be not just technically correct but contextually defensible.
• Human accountability: Ownership must be explicit: who reviewed, who approved, and who can intervene.
• Controls and monitoring: Guardrails must operate continuously, especially under stress conditions.
• Regulatory evidence: Every decision path must be reproducible when challenged.

These are not abstract ideals but key operational expressions of responsible AI practices, helping asset managers ensure that AI is compatible with fiduciary duty.

The bottom line: Improving operational leverage

As firms mature in AI adoption, the focus evolves from products to platforms and ultimately to systems. The difference is not incremental; it is structural.

Value is no longer created by isolated use cases, but by integrated intelligence loops running on trusted infrastructure that connect external market signals with internal data, workflows, and decision rights. When orchestrated well, AI can serve as a coordination layer for action at scale in line with fiduciary requirements.

That is what drives operational leverage. Instead of adding incremental tools or headcount, firms can embed intelligence directly into workflows so the organization can better align decision-making, sensing, and action. Trust is thus inherent within systems and is no longer merely a byproduct.

Take the next step

We work with institutions to move from experimentation to production by embedding governance into multi-agent systems and operations so that fairness, reliability, privacy, and accountability are built into the foundation where AI runs. Our end-to-end approach can help unify enterprise and market intelligence into a shared operating context that turns fragmented insights into coordinated action at scale. Because most financial markets professionals already use Microsoft tools, it can also help lower deployment effort and improve adoption while keeping human oversight at every material decision point.

This is a rare and urgent invitation to the custodians of fiduciary capital to architect a homeostatic system of intelligence. It marks the convergence of capital and cognition, redefining how enduring organizations create and sustain value over time.

Explore how to best adopt AI

To learn more about how your organization can adopt AI, start by engaging with your Microsoft representative or service provider and explore these resources:

• Read our blog for stories about how Frontier Firms are using agentic AI to gain an edge in capital markets.
• To learn how Frontier Firms in capital markets lead with AI, visit Microsoft for Financial Services.
• For a practical transformation framework, read our new ebook, Frontier Transformation in Capital Markets.

¹ EY, Unlocking strategic advantage: Generative AI in wealth and asset management, September 16, 2025.

² Moody’s, Moody’s advances decision-grade credit intelligence across enterprise AI workflows, powered by Microsoft 365 Copilot, April 21, 2026.

The post Trust as infrastructure: How agentic AI is rearchitecting asset management at scale appeared first on The Microsoft Cloud Blog.

Financial service institutions have long been among the sectors requiring the greatest levels of security, compliance, and reliability. Today, in the age of AI, organizations in the financial sector are looking to apply AI to alleviate some of these burdens, while also unlocking meaningful competitive advantage through AI applications.

The good news: If you’re in this industry you will likely have decades of sensitive data you can use for learning and insights that can lead to real customer solutions.

The bad news: Yesterday’s data infrastructure might not be up to the task. Delivering the scale, speed, predictive maintenance, access, and performance that today’s financial data platforms need—along with the standard security and compliance—requires rethinking your database solution for the modern era.

The stakes are higher with sensitive data

Maintaining always-on services and meeting stringent regulatory requirements have been baseline expectations in finance for years. Now, with surging digital transactions and AI-powered projects, the pressure has only intensified. In some financial organizations, even a few minutes of downtime can be disastrous, given the reliance on every day availability. Aging, self-managed databases struggle to keep up with high-volume transactions and real-time analytics demands. The operational overhead of managing such systems (like patching, scaling hardware, and manual failovers) drains resources that could be better spent on innovation.

It’s telling that predictive maintenance and infrastructure automation have become focal points for banks to avoid costly outages. Yet, acting too fast also carries risk: one fintech leader recently cautioned that “in financial services, harm historically scales faster than benefit,” underscoring that if you scale up without a solid foundation, problems can amplify rapidly. In other words, bad data or brittle systems will only wreak havoc faster when accelerated by AI. This reality makes it clear that simply layering AI on top of old infrastructure isn’t viable—the core data platform needs modernization.

An investment in PostgreSQL on Microsoft Azure

Azure PostgreSQL managed services, including Microsoft Azure Database for PostgreSQL, address these modern challenges, combining PostgreSQL’s versatility with Azure’s cloud resilience and ecosystem. It’s a fully managed service, meaning Azure handles the heavy lifting of maintenance, updates, and scaling behind the scenes, so teams can focus on value-add work instead of upkeep. Crucially for financial institutions, Azure Database for PostgreSQL offers flexible performance scaling. You can start with a small instance and scale up to large multi-vCore servers or even scale out across elastic clusters to distribute heavy workloads across multiple nodes. This elasticity ensures that sudden surges in trading volume or customer usage won’t degrade application performance.

Enterprise-grade resilience and availability

Downtime isn’t an option for these critical applications, so continuous availability is baked into Azure PostgreSQL services. With a few clicks, you can enable zone-redundant high availability, deploying a fully synchronized standby server in a different Azure availability zone. In the event of an outage or even an entire datacenter zone failure, the service triggers an automatic failover to the standby typically within 60 to 120 seconds with zero data loss. This architecture delivers up to a 99.99% availability service level agreement (SLA) for mission-critical workloads, which is a key assurance for financial apps that cannot go down.

For read-intensive scenarios, Azure Database for PostgreSQL supports read replicas which asynchronously replicate data and allow you to offload analytics or reporting queries without impacting the primary database’s performance. These replicas can even be in different Azure regions, doubling as a disaster recovery option to keep services running through regional disruptions. The bottom line: whether it’s handling a hardware failure or scaling out reads, the service preserves uptime and consistency so your customers and applications see uninterrupted service.

Security, compliance, and an integrated ecosystem

Azure Database for PostgreSQL helps simplify compliance for sensitive and highly regulated data by providing layered security controls out of the box. All data is encrypted at rest by default, and you have the option to use customer-managed keys for encryption if you need full control over key rotation and access. Network isolation is straightforward: you can deploy your PostgreSQL server into an Azure Virtual Network with private endpoints, so that database access stays entirely on your private Azure network with no exposure to the public internet.

For identity and access management, Azure Database for PostgreSQL supports Microsoft Entra ID authentication, allowing you to manage database users and permissions through centralized Entra ID identities instead of static credentials. This means you can use existing corporate security policies and easily onboard and offboard users per compliance needs. Together, these features help meet strict standards like payment card industry data security standard (PCI DSS) and Security Operations Center (SOC) compliance by controlling who has access to what data and ensuring data is protected at rest and in motion.

Because it’s an Azure service, PostgreSQL integrates naturally with the broader Microsoft ecosystem. You can connect your data to analytics and AI services (such as Microsoft Fabric and Azure AI) without complex Extract, Transform, and Load (ETL), accelerating the development of AI-powered apps on top of your operational data.

In fact, after modernizing its platform, BNY Mellon reported that its teams could “innovate faster in areas such as data management, analytics, AI, and machine learning” once they were running PostgreSQL on Azure. Developers also retain the full power of PostgreSQL’s extensibility. Azure’s managed service supports a wide range of popular Postgres extensions (from PostGIS for geospatial analysis to pg_cron for scheduling), so developers can continue to use specialized plugins for financial calculations, time-series analysis, or even graph queries as needed.

A transformation with returns in nine months

To see these benefits in action, consider BNY Mellon, a global financial services company that modernized a critical data platform by migrating to Azure Database for PostgreSQL. BNY Mellon’s Data Vault system ingests and manages mission-critical, multitenant data for clients—it demanded high resilience, scalability, and agility that their legacy self-managed database couldn’t easily provide. Working closely with Microsoft, BNY Mellon moved this workload to Azure Database for PostgreSQL, completing the migration in just nine months.

By adopting Azure’s fully managed Postgres, the company achieved simplified data storage and analytics and built a “cohesive, customized solution” aligned with their microservices architecture. Resiliency improved immediately, with Azure’s high availability and backup capabilities, and BNY Mellon’s engineering teams gained more time for innovation now that routine database maintenance is offloaded to Azure. This new foundation is not only handling today’s needs but is flexible enough to evolve with future AI and analytics initiatives, exemplifying how a modern cloud database can empower a venerable financial institution to stay on the cutting edge.

A step toward readiness for the era of AI

Modern financial services requires a database platform that can scale effortlessly, stay secure and compliant by default, and free up your teams to innovate with data. Azure Database for PostgreSQL, with its combination of performance, high availability, advanced security, and rich PostgreSQL compatibility, rises to that challenge. It’s a solution that lets developers and Database Administrators (DBAs) spend less time wrestling with infrastructure limitations and more time building the next generation of financial applications.

Ready to take the next step? Explore our PostgreSQL for Financial Services solution guide for architectural best practices and implementation tips.

The post AI is requiring financial services to modernize their data platforms appeared first on The Microsoft Cloud Blog.

Property and casualty (P&C) insurers face growing challenges, including macro-economic factors and cyber-attacks, but none is bigger than climate risk. Catastrophic events are nothing new, of course. What has changed is the scale and frequency of weather-related losses and the operational strain that follows. Swiss Re estimates global insured losses from weather‑related natural catastrophes have exceeded $135 billion in 2024, marking the fifth consecutive year insured losses topped $100 billion, and underscoring a structural escalation in climate‑related risk.¹

In response, many insurers are rethinking how to best deliver customer value, profitability, and growth. Mutual and cooperative insurers are under sustained pressure to balance financial strength with their purpose of providing protection in an environment marked by increasingly severe risks and closer regulatory scrutiny. It is a challenge that AI is well suited to answer, helping to expand the role of insurers from risk transfer providers to proactive risk partners.

Insurers and AI: early adoption and opportunity

A 2024 survey by the International Cooperative and Mutual Insurance Federation (ICMIF) found that 62% of respondents were already using AI, with a further 19% planning adoption within the next year. In practice, however, most deployments were commonly concentrated in specific functional areas, such as supporting underwriting, claims processing, and customer interactions. About 67% of insurers expect AI to become more central to their operations, even as many cite data quality and talent gaps as key challenges.²

According to a recent BCG study, only about 7% of insurers have successfully scaled initiatives, with 67% engaged in pilots, fragmented across functions. The opportunity now is to move from isolated use cases to AI embedded across end‑to‑end processes, extending to more automated, interconnected workflows and setting the stage for a shift toward risk prevention.³

How AI helps improve efficiency, service, and relationship management

Prevention does not replace excellence in risk transfer. Forward-looking organizations pursue both. They modernize service and core operations across the customer engagement cycle, while investing in prediction and prevention-oriented capabilities that help reduce future risk and strengthen long-term resilience.

One area where AI delivers important benefits is in enabling faster, more consistent client service by helping representatives locate and validate policy information faster. At Unum Group, for example, a new AI-powered application lets representatives search across 1.3 terabytes of policy and related documents and receive highly relevant answers in four to five seconds, with reported accuracy of up to 95%. This reduces time spent on manual lookup and frees representatives to focus on higher-value client interactions.

Likewise, NFU Mutual uses Copilot for Sales with Microsoft Dynamics 365 to establish a centralized “single source of truth” for customer data and interactions. By capturing and summarizing communications in real time, employees can quickly understand customer needs and respond with greater precision, helping to reduce response times and deliver more informed, personalized engagement.

AI can also streamline First Notice of Loss by ingesting call transcriptions, images, and videos, and guiding representatives to capture the right information in the first conversation, helping accelerate remediation.

In claims review, AI can turn static documentation into insights that inform action. Gallagher, for example, built an internal AI platform that summarizes complex claims files in minutes rather than hours, helping adjusters move faster and apply those insights more effectively across claims and client workflows.

In cases of widespread impact, such as a storm that causes power outages that result in many food spoilage claims, AI can route low-complexity claims through specialized AI agents that can help validate coverage, correlate weather data, detect fraud, calculate payouts, and generate audit trails. This increases service representative capacity for higher-impact cases by addressing low-risk claims with autonomous AI.

These innovations use document processing, contextual summarization, natural language interface and workflow automation, all of which can be used to help improve other processes across core insurance capabilities, customer service, and relationship management.

How AI helps with prevention and protection

The impact of prevention‑led approaches, whether applied to customer risk or enterprise risk, is twofold: financial resilience and stronger trust. This positions insurers as partners that mitigate, not just transfer risk for their customers.

Prevention‑led use cases extend well beyond field‑level interventions, such as property risk scoring or event‑readiness outreach. Increasingly, they focus on identifying and reducing risks earlier, before disruptions, security incidents, or service failures occur.

This shift is visible in how organizations are applying AI to support faster, more informed decisions. At Aon, which has an enterprise grade platform that can operate across solution lines, teams use AI-enabled tools to better assess and respond to risk. To enhance decision quality while maintaining strong governance, they built an Azure-based AI platform called AonGPT that securely connects data and supports consistent, governed analysis, especially in fast-moving situations. During recent California wildfires, Aon’s teams combined near real-time satellite imagery with proprietary data to generate timely insights that helped clients assess damage and plan their response.

AI also enables a shift from paying claims to helping customers reduce exposure before losses occur. Zurich Insurance Group deployed more than 200 AI tools to interpret unstructured inputs in the form of images, reports, and emails in multiple languages, and translate them into clear, consistent risk signals for underwriters. This improves the accuracy and timeliness of risk assessments, helping customers anticipate and reduce potential exposures before losses occur, and supports better informed underwriting decisions.

Prevention can also take the form of making dormant risk visible early enough to act. For example, AI can analyze large volumes of historical risk engineering reports to identify patterns, such as construction materials or design features that are associated with higher structural risk. This can distinguish specific higher-risk properties for expert review—in weeks rather than months in some cases—letting insurers engage earlier, prioritize inspections, and reduce the likelihood of disruption.

Emerging external data sources help improve risk prevention

Many prevention types depend on spotting and interpreting early signals, often from outside of core insurance systems. Using generative AI and machine learning, insurers can integrate third-party signals with internal data to help create new ways to refine risk selection, pricing, event readiness, customer outreach, and more. Sources such as external research, disclosures, regulatory filings, sensor data, and geospatial imagery can have immense impact, provided they are reliably accessible.

Initiatives from Microsoft Research and AI for Good highlight advances in third-party data that can significantly enrich the power of predictive solutions:

• First, Aurora is a foundation model of the atmosphere that produces fast, high-resolution forecasts, especially during extreme and fast-moving conditions. For insurers and reinsurers, that means more timely environmental intelligence to support underwriting, catastrophe modeling, claims surge planning, and reinsurance response.
  
• Second, SPARROW uses solar-powered devices with cameras, microphones, and sensors to detect meaningful changes on the ground and send near real-time insights to the cloud. For insurers, it shows how AI and sensor data can enable earlier risk detection, faster intervention, and reduce loss severity.
  

Earlier, more precise forecasting can inform proactive risk alerts, giving customers and commercial clients time to take preventive actions (for example, securing property or adjusting operations) and support coordination among insurers, risk engineers, brokers, and public authorities. The objective is straightforward: Improve analysis, lead time, and decision quality to mitigate large losses.

Priorities for success with AI and risk prevention

For leaders, realizing measurable value from AI across the business, including enhancing prevention, can happen in a matter of months or quarters. Microsoft’s view of industry patterns indicates that successful approaches often prioritize the following:

• Define a clear strategy and start with a small number of high‑value, extendable use cases aligned to core business priorities.
  
• Build strong data foundations and effective governance.
  
• Balance innovation with credibility and responsible adoption.
  
• Pursue business-led process re-architecture, change management, and talent skilling.
  
• Commit to stretch goals with active leadership, resourcing, and accountability.
  

Insurers who employ this comprehensive approach and tailor AI to their unique business requirements can improve the most critical aspects of their operations. Critically, they can enhance prevention as an important part of their future growth strategies.

Learn more

• To explore how leading insurers are using agentic AI to transform claims, underwriting, and customer experience, read our ebook.
• To explore solutions and resources for insurers, visit Microsoft for Insurance.
• To learn how frontier firms in financial services are using AI to improve efficiency, innovation, and customer satisfaction, get the e-book.
  
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.
  

¹ Swiss Re, “Hurricanes, severe thunderstorms and floods drive insured losses above USD 100 billion for 5th consecutive year, says Swiss Re Institute,” December 2024

² International Cooperative and Mutual Insurance Federation, “Balancing AI innovation with member-driven values at mutual and cooperative insurers,” February 26, 2025

³ BCG, “Insurance Leads in AI Adoption. Now It’s Time to Scale.” September 04, 2025

The post From risk transfer to risk prevention: How AI supports long-term financial resilience in insurance appeared first on The Microsoft Cloud Blog.

While major technology shifts certainly introduce new risks, the good news is that they also provide capabilities and solutions to not only help meet regulatory expectations from a compliance and risk perspective, but also greatly improve operational reliability, resiliency, security, and governance.

In the European Union, two landmark regulations are redefining expectations and requirements regarding cybersecurity and operational resilience:

• The Digital Operational Resilience Act (DORA) focuses specifically on financial institutions, requiring firms to demonstrate end‑to‑end operational resilience, including Information and Communication Technology (ICT) risk management and incident handling. For the first time, it also broadens regulatory scope by giving regulators direct oversight of companies identified as critical third parties (which includes Microsoft) because of the role these companies play as key providers within the wider financial ecosystem.
• The European Union Network and Information Security Directive 2 (NIS2) sets a new benchmark for cybersecurity obligations by implementing stronger compliance requirements and expanding its scope to cover critical sectors and requirements for risk management, incident reporting, and governance.

While DORA and NIS2 originate in the EU, their impact extends well beyond Europe, illustrating the “Brussels Effect,” whereby EU rules influence global business and security practices. Indeed, Microsoft often takes a global view of and a scaled approach to these regulatory requirements, which means firms can have confidence that these operational and security controls are applied consistently, irrespective of the jurisdictions in which they operate.

Additional jurisdictions, including the US, UK, Australia, Singapore, and Canada are also strengthening expectations around cybersecurity, risk management and incident notification. In the US, the Security and Exchange Commission’s amended Regulation S-P (Reg. SP), requires “covered institutions” to adopt written incident response plans, notify customers of data breaches, maintain oversight of service providers, meet new recordkeeping requirements, and notify regulators of major incidents. Parallel obligations also apply under the US Federal Bank Agencies’ Security Incident Notification Rule (formally titled the “Computer-Security Incident Notification Requirements for Banking Organizations and Their Bank Service Providers”).

Reg. SP is intended to protect sensitive customer information. It applies to broker dealers, registered investment advisors, registered investment companies, and registered transfer agents. After Reg. SP came into force on December 3, 2025, for large firms and June 3, 2025, for smaller firms, boards are accountable for ensuring that customer data protection and incident response are governed, resourced, tested, and enforced at the enterprise level. Firms must implement policies to oversee service providers, including:

• Due diligence and monitoring
• Contractual arrangements establishing service provider responsibilities
• Breach notification requirements obligating service providers to notify the firm of security incidents (no later than 72 hours after discovery)

Harmonizing these requirements on a global scale can be challenging for any multi-jurisdictional financial firm. With Microsoft’s integrated approach to regulatory compliance, companies can trust that their compliance requirements will be supported wherever they operate globally.

Financial firms remain primarily accountable as regulated institutions

One of the most important and often misunderstood aspects of Reg. SP and similar regulations are the notification responsibilities and timelines. Financial services firms and technology providers have distinct responsibilities:

Financial services institutions: According to Reg. SP, NIS2, DORA, and similar regulations, notification requirements are directed at the regulated entity, meaning the financial institution itself is responsible for compliance. These rules define when financial institutions must assess incidents, determine materiality, and notify regulators or customers when required. While technology providers must provide timely notice when they discover a material incident, financial institutions remain accountable to adhering to these obligations, which include:

• Determining whether an incident is material under the relevant regulation
• Meeting regulatory disclosure and notification deadlines
• Maintaining governance, oversight, and documentation to support those decisions

Technology providers: Microsoft is committed to adhering to applicable regulations and ensuring that our services will enable customers to meet their regulatory requirements worldwide. Although we do not assume the regulatory disclosure obligations of financial institutions, we do provide assistance and resources to help customers meet their compliance requirements, including:

• Security and compliance capabilities that support regulatory alignment
• Transparency and documentation to help assess incidents
• Integrated support that assists with incident management and regulatory mapping
• Customer notification of incidents (as contractually committed to), in alignment with applicable regulatory requirements

How Microsoft helps support a unified approach

Financial services leaders need consistent, integrated capabilities rather than one-off compliance fixes. Microsoft applies a global approach in helping firms comply with applicable regulations by aligning our commercial commitments and underlying services to manage compliance across jurisdictions. We help customers navigate these disparate regulatory requirements by offering the following capabilities:

• Compliance mapping with Microsoft Purview Compliance Manager: Provides NIS2 and other regulatory assessment templates to help organizations assess and track compliance across Microsoft cloud services.

• Control mapping with Compliance for Microsoft Cloud (EDE): An optional enhanced support package, delivered through Microsoft Unified Support, that assigns a dedicated engineer to help an organization interpret relevant Microsoft controls and gain assurance when responding to regulatory and compliance requirements.

• Continuous threat monitoring with Microsoft Sentinel: Enables real‑time threat detection and continuous security monitoring, with automated incident handling and evidence workflows that support NIS2 requirements and align with DORA ICT risk management and operational‑resilience expectations.

• Security protection with Microsoft Defender XDR: Delivers cross-platform threat protection and advanced response capabilities.

• Compliance and policy enforcement with Azure Policy & Security Center: Enforces compliance monitoring and policy adherence across hybrid multi-cloud environments.

• Customer managed keys with Azure Key Vault & Intune: Supports cryptography, secure key management, and device security controls.

• Identify management with Microsoft Entra ID: Provides identity and access management, including strong access-control capabilities through multifactor authentication and privileged identity management.

• Lifecyle security and compliance management with Microsoft Unified: Helps operationalize incident management and resilience controls aligned with regulatory expectations under frameworks including DORA, NIS2, and the EU AI Act, and supports the security and response capabilities that underpin SP obligations.

For customers with deeper compliance assurance needs, Compliance for Microsoft Cloud (EDE) is an optional Microsoft Unified Support add-on that provides a dedicated engineer focused on compliance related scenarios, helping customers interpret Microsoft controls and support regulatory and assurance discussions across Microsoft’s core online services.

The elements of accountability

The requirements under Reg. SP, DORA, and NIS2 assign clear accountability on firms to establish and maintain governance, management oversight, and documented operational processes necessary to notify regulators and customers of incidents in a timely manner. Boards and executives are increasingly expected to:

• Understand regulatory exposure across jurisdictions
• Ensure that incident response and disclosure processes are in place
• Maintain appropriate governance and oversight of technology providers as part of their third-party risk management programs

Financial services leaders should consider these essential points:

1. Reg. SP is the catalyst, not the exception. It reflects a broader global trend toward cybersecurity and resilience expectations.
2. Regulatory disclosure obligations remain with the financial organization. Microsoft supports compliance but does not assume customer notification timelines.
3. A capability‑led approach scales better than rule‑by‑rule responses. Microsoft Purview, Azure Policy and Security Center, and Microsoft Unified form a practical foundation for managing regulatory change across regions.

Microsoft’s comprehensive security, governance, and compliance portfolio enables financial organizations to address changing regulatory requirements with confidence. Microsoft remains dedicated to supporting the financial services industry as a reliable partner, fostering growth, adaptability, and effective management of ongoing transformation.

Learn how Microsoft helps financial leaders navigate regulatory requirements

• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.
• See how financial institutions strengthen security and resilience—without slowing down modernization—in our video series.
• To learn more about Microsoft’s overall platform strategy, including compliance, for regulated financial services, see Microsoft for Financial Services.
• For more on how Microsoft frames regulatory compliance as a long‑term strategic challenge for financial services, visit our Compliance Overview.

The post As regulation intensifies, Microsoft helps financial leaders meet growing demands appeared first on The Microsoft Cloud Blog.