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.

A glimpse of what the future may hold can be seen in a collaborative effort involving leading global banks and Microsoft. Specifically, a new prototype is demonstrating how agentic AI can solve longstanding problems while enabling more seamless, embedded client experiences.

An average international trade shipment can involve up to 50 separate documents exchanged between as many as 30 different stakeholders.¹ The result is an avalanche of paper. More than 4 billion documents are estimated to move through the global trade system every day,² and only 1-2% of these are handled digitally.¹

Complicating matters, disconnected trade platforms and fragmented workflows often suffer from a reliance on paper and a slow adoption of data standards. Critical data, even when digitized, often needs to be manually rekeyed into disparate supply chain systems and bank platforms. This leads to delays, discrepancies, and persistent inefficiencies, creates a drag on financing, and introduces a broad range of risks.

Simply converting paper to image or text is not enough to modernize trade finance processes. True transformation requires that data be structured, understood, and actionable. This is where the latest advancements in generative AI and large language models (LLMs—systems trained on vast datasets to understand meaning and generate content that feels human) are poised to fundamentally change the game for global trade.

A collaborative proof of concept to streamline data exchange 

Working in partnership with ANZ, HSBC, and Lloyds, Microsoft has built a technology proof-of-concept (POC) solution that demonstrates how AI agents, powered by LLMs, hold the potential to transform trade workflows. The prototype demonstrates how AI can be embedded directly into ERP systems to extract, validate, and digitally transmit structured trade data to banks, enabling seamless, standards-based integration. 

Demonstrated at the Sibos 2025 conference in Frankfurt, Germany, the POC uses advanced AI and API technologies, together with the Key Trade Documents and Data Elements (KTDDE) framework developed by the International Chamber of Commerce’s (ICC) Digital Standards Initiative (DSI), to help enable a decentralized, end-to-end data exchange based on standardized core data elements used across trade and trade finance documents. 

The demo illustrates what “agentic AI in the trade finance workflow” can look like. The POC simulated a corporate seller receiving an MT700 Letter of Credit (LC) message. An AI agent built on a generative AI model automatically parsed the LC, identified the key data elements (such as buyer and seller information, credit amount, shipment terms, and dates), and cross-checked them against the invoice and shipping data in the ERP. In the demo, the AI agent quickly detected data discrepancies across documents such as currency and amount and suggested a correction in natural language. Once verified, the data was transmitted securely to the bank.

Crucially, the POC also illustrated how treasury users can interact with the data in trade documents through a conversational AI interface. For example, a treasury manager could ask the AI agent questions like “Is this letter of credit compliant with the agreed terms?” and receive instant answers grounded in both ERP data and third-party trade documents. Data sources can extend to real-time market data such as foreign exchange (FX) and risk ratings to enable more complex treasury questions such as FX hedging and LC discounting.

This kind of agent-based interaction with enterprise data marks a breakthrough in usability. Instead of poring over documents or portal screens, stakeholders can simply ask questions and get AI-generated insights, dramatically speeding up decision-making in the trade process. 

Because LLMs interpret documents with contextual understanding—not just spotting keywords but grasping meaning and relationships—AI agents can help surface subtle red flags, such as references to sanctioned entities or ambiguous descriptions of dual-use goods. By referencing regulatory frameworks such as EU dual-use export control laws, these agents can flag potential compliance risks early, enabling proactive intervention before a transaction proceeds. 

By enabling direct, standards-aligned data exchange between corporates and banks, this kind of solution potentially helps to: 

• Reduce document discrepancies by validating data at the source. 

• Improve accuracy and auditability through structured, machine-readable data and end-to-end traceability. 

• Support standards-driven interoperability across ERP systems, bank platforms, and logistics networks. 

• Shorten time to funding by eliminating paper dependencies and courier delays. 

• Strengthen risk management and compliance by automatically checking trade data against rules and watchlists. 

The potential benefits of such a solution extend beyond banks and trading companies. Governments and customs authorities can use ERP-aligned data to potentially streamline filings and improve tax collection. Shipping and logistics providers can potentially gain earlier access to accurate data, ultimately improving planning and reducing delays. By emphasizing data interoperability and AI-powered insights, the POC offers a repeatable model that can extend beyond trade finance to other complex, document-intensive processes. 

Leading banks and Microsoft: A shared vision on digital trade finance

This successful proof of concept was built on a strong collaboration between Microsoft and three leading global banks, uniting Microsoft’s AI and enterprise technology expertise with the banks’ deep trade finance experience. Together, we are shaping a new model for intelligent, data-driven trade finance. 

ANZ 

ANZ is exploring opportunities to apply AI in ways that can support business processes and enhance customer experience. Where appropriate, we aim to move beyond the role of a back-end transaction processor to deliver trade finance as a seamless part of client existing workflows. By safely and responsibly integrating AI into corporate ERP systems, our goal is to offer a more intuitive, built-in trade finance experience.

Hari Janakiraman, Head of Industry and Innovation, Transaction Banking, Institutional 

HSBC

Trade finance is still overwhelmingly document-driven, which is why the industry needs practical interoperability: common data standards and consistent, bank-defined data sets that exporters—from small businesses to large multinationals—can exchange electronically. This proof of concept shows how aligning to frameworks like the ICC’s Key Trade Documents and Data Elements can reduce discrepancies and help move validated data securely from ERP to bank platforms, making trade more accessible and efficient for companies of all sizes.

Bhriguraj Singh, Chief Product Officer, Global Trade Solutions 

Lloyds

This new development creates a strong opportunity to improve the trade finance ecosystem by moving away from paper being transferred between parties to simply exchanging data. By using open standards (aligned, structured data for key trade documents), we can integrate more easily with clients’ technology and logistics partners. Combined with AI-driven data exchange through the Microsoft connector, this allows information to flow securely and accurately between platforms. We are committed to building a more connected and collaborative digital trade environment, and this approach is an important step forward.

Surath Sengupta, Head of Transaction Banking Products

Opening the benefits of AI and interoperability 

Under the hood, the prototype featured a modern decentralized architecture designed to integrate with multiple ERP systems (Microsoft Dynamics 365 and others), bank platforms, and third-party supply chain applications.

The solution was built on Microsoft Foundry, a unified Azure platform for developing, deploying, and governing AI applications and agents. Foundry brings together models, tools, governance, and observability under a single control plane, which is critical for handling sensitive trade data and ensuring enterprise-grade security. 

LLMs power deep document understanding, data extraction, validation, and conversational interactions. In contrast to traditional Optical Character Recognition (OCR) or template-based systems, which can struggle when layouts change or data is missing, LLMs can adapt to varied document formats and more robustly extract and cross-check information. These capabilities are increasingly being employed across the financial services industry for innovations in payments, risk, and compliance.

Putting AI to work in trade finance

We invite organizations across the trade ecosystem—banks, corporates, fintech, and governments—to co-innovate with us on the future of international trade.

To learn more about this collaborative initiative and explore how generative AI can transform your trade and international banking operations, contact your Microsoft representative.

For more information on Microsoft’s approach to building AI agents and industry solutions, visit Microsoft for Financial Services.

¹ ICC United Kingdom, “‘The Roadmap to Digitalise UK Trade,”  June 16, 2025 (https://www.tradeforprosperity.co.uk/the-roadmap-to-digitalise-uk-trade/)

² Fortune, “Global trade still depends on 4 billion paper documents daily,” October 2023

The post Reimagining trade finance with AI: A collaborative proof of concept from Microsoft, ANZ, HSBC, and Lloyds appeared first on The Microsoft Cloud Blog.

Across banking, capital markets, and insurance, some of the fastest-moving institutions are not simply “adopting AI.” They are becoming Frontier Firms, AI-powered organizations built around human-agent collaboration. In a sector where the cost of error is high, the financial services sector is emerging as an early proving ground for the Frontier Firm model.

The Microsoft 2025 Work Trend Index highlights a widening AI divide. While many organizations remain stuck in pilot mode, Frontier Firms are scaling agentic AI across their operations.

Our work with financial services leaders worldwide shows a clear pattern. The winners in the next generation of innovation will be those that combine human judgment with AI and agents, without compromising security, compliance, or customer trust. Critically, these advantages are best enabled through migration to a modern cloud foundation that can scale AI responsibly and reliably.

The crossroad: Modernize or let legacy debt grow?

Legacy systems have powered financial services for decades. Yet the very qualities that once made them indispensable—custom integrations, tightly coupled architectures, and deeply embedded processes—now create friction and fragility. Increasingly, they can be expensive to maintain, slow to change, and difficult to secure end-to-end. Worse, they can inherently constrain data access across the business, which limits advanced analytics and AI from delivering full value in key areas like customer engagement, fraud prevention, credit decisions, underwriting, and financial crime.

In many institutions, this accumulated technical debt is, in effect, an understated balance-sheet liability. It can increase operational overhead, complicate resilience planning, and broaden the cyber-attack surface. At the same time, regulators are demanding that firms prove stronger controls while, competitively, digital-native challengers are showing what’s possible when technology is designed for continuous change.

Modernization can help answer many of these challenges by helping position firms to gain competitive advantages that go well beyond cost efficiency. As workloads become increasingly cloud-native (in other words, designed to be built, updated, and scaled continuously in the cloud rather than tied to legacy infrastructure), organizations can launch new services faster, respond with agility, and use AI as part of everyday operations.

Waiting to migrate can increase risk and cost

A variety of factors are converging to increase the urgency of modernizing.

• Regulatory pressure is growing. Requirements for operational resilience, third-party risk oversight, data governance, and AI accountability are becoming more explicit and more enforceable. In Europe, the Digital Operational Resilience Act (DORA) raises the bar on stress testing, incident reporting, and information and communication technology (ICT) governance. In parallel, the European Union AI Act introduces demanding expectations for high-risk AI, including transparency, explainability, and bias mitigation. Globally, frameworks shaped by Basel guidance and securities regulators continue to push for stronger risk management, auditability, and controls across financial operations.
• Customer expectations are becoming non-negotiable. “Digital-first” now means more than building a polished mobile app. It means enabling instant transactions, proactive service, and personalized guidance—delivered consistently across channels. Doing all this at scale means that data must move securely and quickly, products should evolve continuously, and controls must be embedded rather than bolted on.
• The threat landscape is getting scarier. Threat actors are using automation and AI to increase both scale and sophistication. In a legacy environment, security improvements often arrive as point solutions, unevenly applied, and hard to validate. Cloud architectures, implemented with the right governance, help enable consistent identity controls, continuous monitoring, and policy-based protection that can be audited and improved over time.

Migration as a lever for innovation

Migration is too often framed as a technology initiative. For business and risk leaders, the more useful long-term view is as to regard it as a control and value strategy, a way to embed governance into the operating fabric of the firm.

This is why many transformation leaders manage cloud adoption as a sequence rather than a singular initiative, with a pathway from rehosting (“lift-and-shift”) through optimization and ultimately to AI acceleration. In this framing, modernization is not the finish line; it is the first step of compounding advantage.

Cloud migration, when managed well, can support a compliance‑by‑design approach, by which policy, identity, and data protections are consistently enforced. It can strengthen operational resilience through architectures that are built for redundancy, automated recovery, and continuous validation. And it can create an innovation pathway by making agentic AI practical to deploy and manage.

The AI-first divide: Cloud as operating model

As we see with Frontier Firms in financial services, innovation leaders tend to treat cloud architecture as more than an infrastructure choice. They use it as an operating model to standardize controls, build reusable platforms, and design processes that are increasingly AI-operated but human-led. The payoff can show up in faster deployment cycles, a lower cost per transaction, and predictive insights that make customer experiences more personal and operations more resilient.

Reaching that maturity typically requires progress across four transformation engines:

• Infrastructure modernization
• Legacy systems migration
• Systems modernization (including new business systems)
• Data modernization with AI integration

Financial services firms face stricter scrutiny than most industries, so the differentiator is not speed alone, it’s the ability to sustain speed while continuously demonstrating security, compliance, and control effectiveness.

We see this in practice across the industry. For example, UBS, following its acquisition of Credit Suisse, migrated a mission‑critical records platform from mainframe to a cloud‑native service on Microsoft Azure, reducing total cost of ownership by nearly 60% and improving their ability to meet regulatory demands. After LSEG migrated its high-volume, mission-critical Autex Trade Route (ATR) trading network from on-premises to Azure, the gains in scalability and resilience helped them absorb a sudden 400% surge in trading volumes with zero incidents. And the National Bank of Greece modernized document processing to improve accuracy and enable faster, more digital customer journeys. The common thread is not a single tool or model, it’s a cloud foundation that supports governed data, resilient operations, and repeatable innovation.

Turning migration into long-term value

For many firms, the hardest part of migration is not the technology; it’s making the journey auditable, repeatable, and aligned to risk appetite. That’s why a structured approach matters.

The Microsoft Cloud Adoption Framework, tailored for financial services, is designed to help institutions align cloud modernization to business outcomes while addressing the governance realities of the industry: data sovereignty expectations, operational resilience, and security-by-design. Importantly, cloud migration need not undermine data sovereignty; done right, migration strengthens locality, control, and compliance through governed architectures.

In practice, migration means helping businesses to build a compliant foundation, innovate responsibly, and maintain continuous control visibility as they scale. Microsoft supports this with financial-services-ready architectures, built-in governance and security capabilities, and a broad set of certifications and controls. Just as importantly, we work closely with customers and regulators globally to help ensure that cloud adoption can be evidenced properly in terms of risk reduction, resilience, and measurable operating improvement.

Trustworthy AI starts with the cloud foundation

Boards and regulators are right to focus on AI governance. Generative AI, agentic systems, and intelligent automation can improve productivity and customer outcomes, but only when they operate on governed data, with strong identity controls, clear lineage, and auditable policies. Those prerequisites are difficult to achieve in fragmented legacy environments.

Cloud migration creates the conditions for AI to be adopted responsibly, with modern data platforms and pipelines, elastic compute for experimentation and scale, consistent policy enforcement, and continuous monitoring.

To help institutions navigate migration with confidence, Microsoft combines a financial-services-tailored methodology with practical tooling and built-in governance. The Cloud Adoption Framework for financial services provides a proven, risk-aligned approach to planning and executing secure migrations. Azure Migrate and the Azure cloud migration and modernization programs help accelerate discovery, modernization, and execution with guidance and incentives. And capabilities like Microsoft Purview and Microsoft Defender for Cloud help establish compliance guardrails and security posture management from day one.

Lead the next generation with cloud

Migration is not the end state of digital transformation. It is the foundation for Frontier transformation, one which can enable firms to innovate faster, demonstrate stronger controls, and adapt quickly to new demands and opportunities.

The financial services firms that lead in the next generation of financial services will not be those that move the fastest in a single quarter. They will be the ones who modernize with technology that is durable, designed for operational resilience and evidence-based governance, and that makes innovation repeatable. Cloud migration is the inflection point where these powerful advantages become possible.

Learn more

• To explore a five-phase, risk-aligned approach to cloud migration that helps build the foundation for AI-first financial services, read the e-book: Becoming an AI-First Firm in Financial Services.
• Register for the Migrate and Modernize Summit to learn from industry leaders.
• Explore the Microsoft Cloud Adoption Framework for Financial Services.

The post Why cloud migration is key to realizing AI value in financial services appeared first on The Microsoft Cloud Blog.

As capital markets firms push toward the frontier, success increasingly depends on turning AI ambition into secure, repeatable operating impact at global scale. In this independent IDC guest blog, Thomas Shuster examines how agentic AI is reshaping capital markets operating models—and why firms are gravitating toward platforms and partners that combine technological leadership, deep industry expertise, strong governance foundations, and proven experience delivering AI value across the end-to-end value chain.

When capital markets leaders talk about Frontier Firms, it is important to recognize that the term’s definition has shifted. It is less about being first to experiment with new tools and more about translating AI investment into measurable, repeatable operating gains. That distinction matters as the operating environment tightens. Settlement cycles continue to compress, regulatory expectations change, and risk controls must remain effective as markets evolve. At the same time, technology teams are expected to modernize while continuing to support large legacy environments. In this context, agentic AI emerges as a practical marker of frontier operating models.

From tools to operating models

Early generative AI tools improved drafting, summarization, and search. These capabilities were helpful but not transformative or differentiated. The step change occurs when firms shift from task acceleration to workflow redesign, deploying AI agents to execute multistep processes across systems under bounded human oversight.

Frontier Firms focus on workflows characterized by high friction, frequent exceptions, and material costs when delayed. They redesign processes so agents perform the coordination and context gathering work that typically slows teams down: pulling data, checking policies, identifying breakpoints, proposing actions, and routing tasks to the right owners. Humans remain accountable for decisions but no longer act as the connective tissue that holds workflows together. This shift has important workforce implications because human effort moves away from manual orchestration and toward judgment, escalation, and decision-making.

By contrast, non-Frontier Firms often attempt to layer AI onto workflows still defined by manual handoffs and fragmented systems. These initiatives may succeed in pilots but frequently stall when exposed to real-world operational variability.

Integration, not intelligence, is the limiting factor

Many operational breakdowns in capital markets stem from fragmented information. Trade exceptions can span execution data, reference data, allocations, settlement instructions, and counterparty communications. Know your customer (KYC) refreshes depend on sanctions data, beneficial ownership structures, customer documentation, and policy interpretation. These are inherently cross-system and, increasingly, cross-organization challenges.

Frontier Firms treat data access as a core capability rather than a downstream integration problem. They invest in ecosystems that support secure, permitted access to internal and external data with auditability and clear economic and contractual rules. In practice, the operating framework often matters as much as the underlying technology. Questions of data ownership, computational rights, value sharing, and dispute resolution frequently determine whether an agentic use case can scale. Where these foundations are absent, teams compensate with manual workarounds that are slow, error-prone, and difficult to audit.

Governance as an accelerator

There is a persistent tendency in capital markets to defer governance until a use case has demonstrated value. That approach breaks down with agentic AI. Agents act within workflows and can trigger downstream consequences if controls are weak.

IDC’s research shows that only about 4% of financial institutions believe AI agents should operate with full autonomy. More than 75% rate transparency as very or extremely important, with the share rising to roughly 88% among Frontier Firms. How frontier organizations operationalize trust reflects these preferences. They define which decisions require human approval, log agent inputs and actions, establish clear escalation paths, and design workflows that make overrides straightforward. Many organizations also prefer to rely on platform-level governance capabilities rather than bespoke controls for each use case.

When done well, governance becomes an enabler rather than a constraint. It allows firms to deploy agentic workflows more broadly and with fewer surprises, aligning risk and innovation teams. Where governance lags, organizations often see the opposite outcome: Risk teams perceive AI as uncontrolled, innovation teams view governance as blocking progress, and value remains trapped in isolated proof points.

Where Frontier Firms pull ahead first

IDC finds that Frontier Firms adopt functional and industry use cases almost twice as much as their peers. Expectations for automation are also rising. In IDC’s resiliency and spending research, 87% of firms expect providers’ agentic AI capabilities to eliminate manual and semi-manual workflows within 18 months.

The gap widens most quickly where speed, exception handling, and control converge. In post-trade operations, many organizations still manage exceptions through email and informal handoffs, slowing resolution, and weakening auditability. Frontier Firms move toward agent-supported, structured case management. In onboarding and due diligence, event-driven regulatory expectations are making periodic refresh models brittle. While only about 10% of financial institutions used AI for regulatory compliance in the past year, nearly 90% plan to do so in the next 12 months. In research and intelligence functions, agents increasingly monitor sources, summarize changes, and map exposures, shifting human effort from aggregation to decision making.

AI is reshaping business models

The frontier advantage is not limited to efficiency. IDC’s research shows that organizations using agentic AI report a 2.3-time return on investment (ROI), with average payback periods of about 13 months. These attractive economics are accelerating investment. Building customized AI agents to automate business processes ranks as the top area of significantly increased IT spending among capital markets firms in 2026, which more than 80% of organizations have cited.

As these agents mature, firms are also reassessing their application strategies. In IDC’s survey, 84% of financial services firms agree that AI agents are emerging as a new layer of enterprise capability, prompting renewed scrutiny of investments in packaged applications.

Closing thought

Agentic AI is not a shortcut around complexity. It is a way to absorb complexity without scaling cost and risk linearly. Ambition alone does not distinguish Frontier Firms. Differentiating them are data access, governance discipline, operating model design, workforce readiness, and organizational habits required to turn agentic AI into a durable source of advantage.

Explore more insights on agentic AI in capital markets

• Read IDC’s Analyst Connection How and Where Frontier Firms Are Bending the Curve on Innovation and explore the infographic Expanding the Frontier: Agentic transformation in capital markets for deeper insights into the strategies shaping the future of capital markets.
• Watch Transforming Financial Services – Leaders in AI adoption and Innovation to see how leaders in capital markets are successfully using AI to transform complexity into opportunity.
• Get a practical transformation framework and see how Frontier Firms lead with agentic AI in our Becoming a Frontier Firm: AI in Financial Services e-book.
• Explore capital markets solutions and resources on Microsoft for Capital Markets.

The post How Frontier Firms use agentic AI to gain an edge in capital markets appeared first on The Microsoft Cloud Blog.

Yet despite broad cloud adoption, migration and modernization remain complex. Legacy architectures, fragmented environments, and persistent skills gaps continue to slow progress, pushing organizations to find ways to migrate faster while minimizing operational risk. 

The IDC study highlights agentic AI as a critical unlock. These intelligent systems automate assessments, orchestrate migration and modernization efforts, and optimize operations across hybrid environments—helping organizations shift from periodic, manual initiatives to continuous, adaptive modernization. This momentum is driving unprecedented growth, with IDC forecasting the public cloud services market will reach USD1.9 trillion by 2029. 

While migration frameworks may be horizontal, their real-world impact is industry-specific. Healthcare, financial services, and manufacturing each face unique constraints shaped by regulation, operational risk, and mission-critical systems. 

In this blog, we explore the key migration and modernization challenges across these three industries—healthcare, manufacturing, and financial services—through real customer stories that highlight the tangible impact cloud adoption is delivering today.

Healthcare: Modernizing securely while powering next-generation clinical experiences

Healthcare faces the toughest modernization headwinds: strict regulations (HIPAA/HITECH, HITRUST), fragmented clinical data across electronic health records (EHRs) and imaging systems, aging on-premises infrastructure resulting in high Capex, and heightened exposure to ransomware.¹ Clinical environments also demand extremely low latency and high reliability.

The IDC study notes that these constraints slow modernization—but accelerate the need for it, as organizations push to scale telehealth, imaging workloads, genomics pipelines, and AI-powered clinical workflows.¹ 

What healthcare organizations need, according to the IDC study: 

• Secure, compliant integration across EHRs, picture archiving and communication systems (PACS), genomics systems, and Internet of Things (IoT) medical devices.¹
• Elastic compute for high-throughput imaging and genomics. 
• Stronger disaster recovery and recovery time performance.¹
• Ambient documentation and AI-supported diagnostics.
• Secure clinician collaboration and modern patient digital front doors.

Customer spotlight: Franciscan Health

Facing aging infrastructure and disaster recovery risks, Franciscan adopted a pragmatic workload placement strategy—moving its Epic EHR to Microsoft Azure.

The results included: 

• $45 million in savings over five years after migrating Epic to Azure.
• 90% faster disaster recovery compared to the prior environment.
• Around a 30-minute failover, reduced from hours.
• $10–$12 million per day in potential downtime risk avoided.

Learn more about Franciscan Health’s journey to migrate its Epic EHR to Azure.

Healthcare’s modernization mandate is clear: reduce operational risk, meet regulatory demands, and harness cloud AI to improve patient outcomes. 

Financial services: Enabling real-time intelligence and automated compliance

Financial institutions operate in one of the most regulated environments, including the payment card industry data security standard (PCI DSS), the Sarbanes-Oxley Act (SOX), the Gramm-Leach-Bliley Act (GLBA), Basel capital frameworks, and know your customer (KYC) and anti-money laundering (AML) requirements, and rely heavily on legacy mainframes that are difficult to modernize. Today, regulatory pressure is intensifying further as new frameworks such as the EU’s Digital Operational Resilience Act (DORA) and the EU AI Act raise the bar for operational resilience, third-party risk management, model transparency, and ongoing compliance monitoring. Under DORA, financial services firms must demonstrate continuous information and communication technology (ICT) risk management, advanced incident reporting, and resilience testing across critical systems and cloud service providers. Meanwhile, the EU AI Act introduces governance requirements for high-risk AI systems, including explainability, data lineage, human oversight, and auditability—with direct implications for fraud models, credit scoring, and customer decisioning platforms.

IDC interviews highlight accelerating demand for real-time risk analytics, fraud detection, digital onboarding, and infrastructure elasticity to support peak activity—capabilities that are increasingly mandated, not optional.¹

Key challenges the IDC study identifies: 

• Strict data residency, model risk governance, explainability, and eDiscovery requirements.¹
• Heightened expectations for operational resilience, cyber defense, and third-party risk oversight.
• Legacy systems and common business-oriented language (COBOL)-based batch processes resistant to change.
• Rapidly evolving regulatory mandates requiring continuous compliance rather than point-in-time audits.

Cloud—especially especially platform as a service (PaaS) and managed services—helps financial institutions shift from static, batch-driven compliance to continuous controls and real-time observability. By reducing batch windows from hours to minutes, modern cloud platforms enable real-time insights, automated evidence collection, resilient architectures, and policy-driven compliance workflows aligned with DORA and AI governance requirements.¹ Learn more about how Microsoft can help financial institutions navigate these requirements. 

Customer spotlight: Crediclub

To accelerate product innovation and meet expectations from Mexico’s national banking and securities commission (CNBV), Mexican fintech Crediclub modernized its databases to a serverless platform as a service (PaaS) architecture and adopted microservices.¹

The impact:

• Uptime improved from around 80% to 99.5%.
• 90% reduction in network latency through Multiprotocol Label Switching (MPLS) and dark fiber.
• Rapid deployment of new financial products via Kubernetes and DevSecOps.

For financial institutions, modernization is no longer just about efficiency—it is foundational to resilience, trustworthy AI, and regulatory compliance at scale. 

Manufacturing: Unifying IT and OT for predictive, data-driven industrial operations

Manufacturers operate in one of the most complex operating environments—defined by legacy and proprietary operational technology (OT) protocols, historically air-gapped manufacturing execution systems (MES) and supervisory control and data acquisition (SCADA) systems, and globally distributed supply chains. Stringent low-latency requirements for safety-critical systems, intermittent connectivity at the edge, and the need to protect intellectual property further compound the challenge. The ability to modernize and unify these environments—without compromising safety, reliability, or performance—represents a critical inflection point for industrial transformation.

Unique modernization challenges according to the IDC study:

• Ultra-low latency requirements for safety-critical operations.
• Massive telemetry ingestion and time-series analytics at scale.
• Operational complexity across global, distributed supply chains.
• Secure protection of intellectual property across edge and cloud environments.

Opportunities unlocked by cloud:

• Predictive maintenance with IoT ingestion.¹ 
• Reduced unplanned downtime and improved overall equipment effectiveness (OEE).
• Digital twins for plants, lines, and products.
• Computer vision for real-time quality and safety. 
• High-performance computing (HPC) simulations for engineering and design. 
• Standardized, global data models.

Customer spotlight: ASTEC Industries

ASTEC unified fragmented systems across its rock to road value chain—from aggregate processing through asphalt production and paving—by adopting Azure, modernizing to timeseries databases, and building a universal connectivity platform using Azure IoT Hub, Azure Events Hub, and Power BI.¹

The results:

• Realtime operational visibility across fleets.
• Predictive maintenance for reducing downtime.
• New digital services supported by connected equipment.

Manufacturing’s modernization imperative: unify OT and IT, scale real-time intelligence, and enable global efficiency. 

Microsoft’s approach: Continuous, intelligent, collaborative modernization 

Microsoft’s strategy is grounded in a simple principle: modernization should be continuous, intelligent, and collaborative. The IDC study emphasizes that successful enterprises adopt a balanced, multipath migration strategy, blending rehost, replatform, refactor, and software as a service (SaaS) substitution based on workload criticality.¹

Microsoft enables this approach through a comprehensive set of tools and offerings, including Azure Copilot and GitHub Copilot. Agentic automation enables:

• Discovery and dependency mapping.
• Security assessment and 6R recommendations.
• Application refactoring, code remediation, and modernization. 

Azure Migrate provides unified discovery, assessment, migration execution, and modernization services. Azure Accelerate complements this with a coordinated framework that includes:

• Guided deployments through Cloud Accelerate Factory.¹ 
• Funding and Azure credits for planning, pilot, and rollout. 
• Expert partners and tailored skilling programs.

The IDC study concludes that organizations using Microsoft Azure for migration and modernization achieve lower operational costs, improved resiliency, faster modernization timelines, and stronger security postures—especially in regulated industries.¹

Looking ahead: Agentic modernization as the foundation for AI-ready enterprises

Across all industries, IDC’s findings are consistent: agentic AI is emerging as the new force multiplier for modernization, enabling organizations to keep pace with rising complexity, regulatory demands, and competitive pressure. 

Healthcare, financial services, and manufacturing each face unique constraints—but cloud modernization remains the foundation for innovation, operational excellence, and enterprise AI. 

Microsoft’s approach gives organizations the unified automation, intelligence, and tooling they need to modernize securely and at scale. 

• Explore how Azure Copilot accelerates cloud migration.
• Discover GitHub Copilot for app modernization.
• See how Azure Accelerate supports transformation.

¹ IDC White Paper, Cloud Migration and Modernization Strategies for Healthcare, Financial Services, and Manufacturing, February 2026.

The post Modernizing regulated industries with cloud and agentic AI appeared first on The Microsoft Cloud Blog.

Today, a new architectural approach is finally emerging, and it is agentic. The rapid advance of agentic AI represents an evolution from reactive interactions to goal-oriented experiences across all aspects of banking. Unlike traditional keyword-based bots, agentic assistants can understand intent, maintain memory, take initiative, and orchestrate tasks across systems. They can support multi‑step workflows, operate within defined policies, and assist customers in a single, intelligent pane of access.

For banking, these advanced capabilities have finally aligned to ever-higher levels of customer expectations to make agentic AI not only viable but increasingly leveraged by leading banks.

Why banking needs a new model

Most customer-facing automation in banking is now rule-based. Traditional chatbots merely answer questions. They don’t finish tasks, much less resolve important needs. They rely on keyword matching, offer minimal personalization, and they operate as single channel interfaces that usually escalate issues instead of resolving them. Too often, this leads to low containment, long cycle times, and customer frustration.

Agentic AI assistants change the equation. They can integrate deeply into core systems, understand identity and consent policies, and provide end-to-end workflow orchestration that delivers more positive outcomes.

AI models now support multistep reasoning, secure APIs allow policy-aware actions, and cloud environments enable industry-grade identity, consent, and auditability.

The time is now for agentic AI

The rapid adoption of broad-scale agentic AI solutions in banking is the product of the convergence of some powerful trends:

• AI-native experiences have reset customer expectations: Consumers increasingly expect proactive, personalized, and frictionless digital interactions.
• Industry competition is intensifying: Highly innovative banks and financial institutions are scaling customer-facing AI capabilities and raising the bar for the entire market.
• Secure orchestration is now achievable: Banks have built robust foundations for consent, governance, compliance, and identity, all of which are essential for safe agentic actions.
• Models can now execute multi‑step tasks: Banking no longer needs to settle for static flows and limited interactions; assistants can complete complex journeys from disputes to onboarding.

As these factors accelerate, agentic banking is fast gaining momentum. In fact, it is already operational today for many financial institutions.

A three-step blueprint for agentic solutions

Microsoft’s blueprint to help banks develop game-changing innovations includes a structured, deliberate path for adopting agentic AI across internal and customer-facing scenarios. Rather than layering AI onto outdated workflows, institutions must redesign experiences with outcomes in mind. This can be done through the development of three steps of AI innovation:

Step 1: Internal employee assistants

In this step, banks strengthen the maturity of AI innovations internally, by improving employee productivity and supporting back office workflows such as Anti-Money Laundering (AML) routing, document gathering, and payment operations. This phase establishes the organizational readiness needed for external experiences.

Step 2: External customer assistants (owned channels)

In this step, banks introduce customer-facing assistants within their digital properties, such as websites and mobile apps. These solutions initially target a narrow set of journeys to help validate measurable outcomes and build confidence, setting the stage for scale, including deeper transactional use cases.

Step 3: External customer assistants on third-party platforms

Once confident, banks can deliver rich, new AI-enabled experiences beyond their own digital properties, helping to stay foremost in the customer relationship. Even as the front door shifts to non banking platforms, banks can retain primary engagement by anchoring identity and execution within governed, policy driven solutions that can incorporate agentic AI assistants from multiple platforms (ChatGPT, Gemini, Microsoft Copilot, and so on).

Real-world impact in agentic banking is well underway

Across the customer journey, agentic experiences are transforming outcomes. Here are just four areas where we work with customers to deliver measurable benefits.

Disputes and fraud resolution

Disputes and fraud incidents are among the most stressful and urgent customer interactions in banking. These moments demand precision, empathy, and speed —which traditional chatbots usually can’t deliver. Agentic assistants change this experience by understanding transaction context in real time, anticipating customer needs, explaining next steps with clarity, and orchestrating complex actions across compliance, fraud, and operations systems. They help manage escalation intelligently while keeping customers informed with conversational transparency.

Commerzbank’s introduction of an AI-powered assistant called “Ava” demonstrates the impact of this shift. Built with Microsoft Foundry Agent Service, Ava reportedly now resolves about 75% of customer conversations autonomously. The result is a dramatic reduction in response times, more consistent fraud handling, and meaningful relief for human agents who can focus on high complexity cases requiring expertise and judgment.

Product discovery and onboarding

Even when banks offer strong products, customers often struggle to understand differences, evaluate eligibility, or navigate onboarding processes. Static comparison charts and rigid forms create barriers that trigger abandonment. Agentic assistants address this gap by offering contextual, conversational discovery. They can analyze eligibility, financial behaviors, and long-term goals to guide customers toward the most relevant products, compressing the time from interest to completion.

For instance, ABN AMRO’s migration to Microsoft Copilot Studio showcases these benefits at scale. Their customer facing assistant “Anna” now supports millions of customer interactions annually, automating more than half of them. Customers receive tailored recommendations and seamless onboarding, while the bank benefits from reduced abandonment and increased conversion rates across key products.

Payments and money movement

Customers today simply expect that payments should be fast, intuitive, and free of error. Instead, many people frequently encounter multiscreen forms, confusing validation steps, and interfaces that are prone to mistakes. Agentic AI helps eliminate much of this friction. Customers can simply say what they want to do—for example, “send rent,” “transfer to my savings,” “pay my credit card”—and the assistant determines the optimal method, confirms details, and applies safeguards automatically.

A good example of this is Bradesco’s deployment of generative AI into its virtual assistant “BIA.” After integrating Microsoft Azure OpenAI and Data Lake services, BIA reportedly achieved an 82% first level resolution rate and an 89% retention rate in the first week. Response times fell from days to hours, and usage surged. Payments became conversational, secure, and reliable, helping build long term customer confidence while improving operational efficiency.

Financial guidance and servicing

Financial decisions are deeply personal and often complex. Customers want clarity, reassurance, and the sense that their institution understands their broader financial picture. Agentic assistants support this by combining institutional expertise with personalized context. They can remember life events, adapt to changing goals, and help explore scenarios, understand options, and stay informed about their financial commitments.

Virgin Money embodies this evolution through its award-winning assistant, “Redi.” Built with Microsoft Copilot Studio and Dynamics 365 Customer Service, Redi reportedly now supports millions of customers and delivers what they need more than 90% of the time. The guidance feels informed and tailored, strengthening trust and deepening long-term relationships. Employees report smoother workflows, while customers experience consistency and clarity across channels.

Advancing digital transformation with agentic AI

For banks, technology is finally catching up with customer expectations. The shift is transforming digital experiences from reactive support into proactive engagement.

Agentic AI solutions are defining the next generation of customer experiences, and banks that move now can better position themselves to gain durable competitive advantages by modernizing operations from the inside out and engaging customers in ways that were not previously possible.

Microsoft provides an unmatched set of platforms and services that combine data intelligence, orchestration, and observability to help build, deploy, govern, and scale agentic assistants. Our investments in Security for AI, Zero Trust, and AI governance, help banks keep agentic experiences safe and trusted across the AI lifecycle. This means that with the right blueprint banks can navigate this moment with confidence, clarity, and control.

Explore how agentic AI can modernize banking experiences

• Get a practical transformation framework and see how Frontier Firms lead with agentic AI in our Becoming a Frontier Firm: AI in Financial Services e-book.
• Watch Leading at the Frontier: AI in Financial Services to see how leaders in financial services are successfully using AI to transform complexity into opportunity.
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.

The post The agentic moment in banking: A blueprint for better customer experiences appeared first on The Microsoft Cloud Blog.

With advances in AI—especially intelligent agents and the automation patterns emerging from agentic design—insurers worldwide are recasting their most critical operations and offerings. From marketing and customer engagement through underwriting and claims processing, the industry is rapidly evolving, with AI as a central driver.

At Microsoft, we identify organizations that embed AI agents deeply across their operations as Frontier Firms. These are innovation leaders who are blending human judgement with AI agents and who, according to a November 2024 IDC study commissioned by Microsoft, report returns roughly three times higher than slow adopters.¹

Insurers and other financial services companies make up the highest concentration of Frontier Firms, which is not surprising given the competitive nature of the sector and the outsized impact of agentic AI.

Maximize business value with AI

Discover a practical framework and real-world examples

Become a Frontier Firm

How AI is transforming the end-to-end insurance value chain 

Insurers can potentially realize transformative benefits with AI without needing to replace their core platforms, but rather by augmenting and accelerating them through targeted, extensible, AI-powered capabilities. Through advances such as intelligent agents and the automation patterns emerging from agentic design, insurers are consolidating fragmented workflows into connected, intelligent, adaptive systems.

Consider the impact on claims processing. In 2024, more than 30 million personal auto claims were reported in the US alone.² Each one typically required adjusters one to three days just to gather, read, and interpret documents. The slow, manual nature of traditional claims processing is one of the most labor intensive and high impact functions in insurance. It is also where agentic AI delivers some of the fastest return on investment (ROI). For example, AI can help automate document understanding and summarization for faster and more accurate processing. In policy and coverage validation, it can help reduce back-and-forth queries between adjusters and underwriters and speed the approval of well-qualified claims. In contextual triage and routing, it can help improve the productivity of employees across claims processing by enhancing early fraud detection and reducing delays caused by manual sorting or misrouting. With millions of claims processed annually and cycle times measured in days or weeks, even modest improvements can potentially create significant financial and customer experience gains.

Agentic AI is reshaping much more than claims. Across the value chain, a unified agentic ecosystem can deliver measurable outcomes.

In underwriting, agents can automate information gathering processing to help sales agents submit more complete requests for quotes to underwriters. Agents can help interpret submissions, orchestrate scenarios and catastrophe modeling, and assist in generating proposals aligned to client mandates.

In marketing and distribution, agents can redefine the customer experience by increasing personalization at scale with speed and boosting sales opportunities. Agents can flag top renewals and generate personalized outreach, help prioritize leads, optimize campaigns and prepare tailored client briefs and pitch materials in seconds.

In customer onboarding and service, service become more anticipatory and less reactive. Agents can help validate information across documents automatically and detect missing forms or inconsistencies early. Virtual assistants can answer inquiries around-the-clock with contextual accuracy and trigger proactive outreach if a customer shows signs of churn or claim frustration.

In risk and compliance, teams move from firefighting to orchestrating safe, scalable operations. Under the direction of qualified processionals, agents can help monitor exposures continuously across economic, climate, and portfolio data, read regulatory updates and support assessment workflows, and help detect fraud by surfacing potential issues to the appropriate teams and workflows.

How agentic AI is benefiting insurers worldwide

Already, we’re seeing the impact of agentic AI building on the benefits of generative AI to deliver transformative new benefits for insurers.

For example, Generali France is transforming insurance operations with intelligent agents that empower front‑line workers and experts across the business to achieve a people-centric vision for product and service delivery. The firm has built more than 50 agents with Microsoft Copilot Studio and Azure OpenAI to address a broad range of specialized used cases. These agents do more than generate content, they act across complex information flows, from extracting information from unstructured data and running hyper-personalized marketing campaigns, to assisting with content creation and standardizing responses to requests for proposals (RFPs). These powerful solutions allow experts to focus on judgment and customer care, measurably helping Generali achieve top‑ranked customer satisfaction.

Elsewhere, a major global insurer strengthened its crisis response in near real-time by using AI to rapidly compare property locations with public wildfire evacuation data. Instead of hours of manual analysis, teams quickly generated clear, actionable risk insights, improving situational awareness and enabling faster, more confident communication with stakeholders.

Another insurance and financial services company took a proactive approach to risk mitigation, using AI to scan records for a brittle material linked to structural failures in older buildings, helping to identify and assess risks before losses could occur.

These real-world scenarios are only the tip of the iceberg, giving an early view of the broader transformation that is quickly redefining the competitive landscape. In upcoming blogs, we will share deeper examples and customer‑aligned scenarios across the end-to-end insurance value chain.

The journey to becoming a frontier insurer starts now

To unlock the value of agentic AI, Microsoft offers an end‑to‑end cloud and AI platform that insurers can incorporate powerful agents into their technology ecosystems. Microsoft Foundry provides the developer platform for building, testing, deploying, and orchestrating AI agents and applications, and Microsoft Agent 365 offers a control plane to help govern, secure, monitor, and manage agents across an enterprise, regardless of where they were built. This means that insurers can design, customize, deploy, and integrate intelligent agents across the value chain, with enterprise‑grade governance and a comprehensive suite of AI models and services.

Microsoft further strengthens this foundation with industry‑specific data models, process frameworks, and prebuilt connectors that simplify integration with core insurance systems, analytics environments, and workflow applications. This helps ensure faster time‑to‑value and accelerates modernization of claims, underwriting, servicing, and risk operations.

And critically, insurers also benefit from a deep, global partner ecosystem of trusted technology and solution providers who are well versed in delivering mission-critical solutions on the Microsoft Cloud, combined with our deep, long‑standing expertise in the insurance sector. Together, this ecosystem empowers insurers to innovate confidently, scale securely, and realize measurable impact with agentic AI.

The journey to agentic AI involves identifying high-impact workflows early, creating a unified data platform, addressing governance from the start, and empowering teams with smart change management. By embracing a frontier firm mindset—human led, agent operated—insurance leaders can unlock new value and innovate in the new competitive landscape. To continue your AI journey, contact your Microsoft representative or technology partner.

Next steps on your journey to agentic AI

• 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.

¹ IDC InfoBrief: sponsored by Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, November 2024.

² Verisk, ClaimSearch Trends Report, 2024 Year-end Analysis

The post From bottlenecks to breakthroughs: How agentic AI is reshaping insurance appeared first on The Microsoft Cloud Blog.

The insurance industry stands at a pivotal moment in its digital transformation journey. With deep data reserves, a tradition of analytic decision-making, and a workforce skilled in actuarial and underwriting practices, insurers are uniquely positioned to benefit from the ongoing advances in AI.

However, despite early enthusiasm and pilot projects, only 7% of insurers have successfully scaled AI initiatives across their organizations.¹ The adoption of increasingly powerful AI agents—systems that can support autonomous tasks, help make decisions, and take action under human oversight—offers a promising path forward. By embedding intelligent agents into workflows, insurers can tackle legacy constraints, talent shortages, and operational inefficiencies to unlock transformative value.

Challenges in adopting agentic AI

The broad adoption of agentic AI in insurance is hindered by several entrenched challenges.

First, a persistent talent shortage affects specialized roles such as actuarial analysis and underwriting, which limits the industry’s ability to leverage data effectively. Adding to the challenge is legacy infrastructure, including outdated systems and fragmented data architectures, which can impede integration and scalability.

Financial strain across the insurance sector is another major factor, with catastrophe losses exceeding $100 billion annually for six consecutive years, making high-frequency property losses a structural issue.²

Organizational resistance also plays a significant role; siloed teams, unclear priorities, and a conservative corporate culture slow the pace of AI adoption.

Opportunities with agentic AI

Despite these hurdles, agentic AI presents transformative opportunities. Workforce augmentation is one of the most promising areas. For instance, Sedgwick’s Sidekick Agent, developed in collaboration with Microsoft, enhances claims processing efficiency by more than 30% through real-time guidance and decision support.³

AI also enables personalized customer experiences at scale, using embedded systems to tailor communications and support. Operational efficiency can be improved significantly in some implementations, with end-to-end redesigns yielding 30–40% gains in net efficiency.¹

Furthermore, agentic AI, under human guidance, can enhance quality assurance by improving consistency and helping insurers adhere to compliance processes, which is especially important as seasoned professionals retire and institutional knowledge can be lost.

With agentic AI, chatbots can also be improved to more effectively enhance customer experience. Instead of answering a question and handing a customer off to a queue, an agentic solution can help orchestrate a more end-to-end experience. Potentially, this can include everything from capturing first notice of loss, to requesting missing documentation, updating policy and billing systems, scheduling appraisals, and proactively notifying customers of next steps (all subject to insurer workflows and regulatory review, of course).

This “resolve, not route” approach is already showing measurable impact in claims operations: For example, according to McKinsey, one major insurer rolled out more than 80 AI models in its claims domain, cutting complex-case liability assessment time by 23 days, improving routing accuracy by 30%, and reducing customer complaints by 65%.⁴

For carriers, such outcomes translate into faster cycle times, higher customer satisfaction, and better loss-adjustment expense control—all while preserving necessary human oversight where judgment, empathy, and regulatory accountability are required.

Strategies for success with agentic AI

To successfully adopt agentic AI, insurers must align technology initiatives with customer needs and business goals.

Establishing an AI Center of Excellence (CoE) is a foundational step. A CoE provides governance, strategic direction, and technical expertise, helping organizations avoid fragmented AI adoption and scale responsibly.

Iterative testing is also essential, beginning with high-volume, repeatable tasks that help insurers refine models through feedback loops and production pilots.

Targeting scarce talent areas, such as fraud detection and underwriting, can maximize impact by augmenting roles that are difficult to fill.

Industry accelerators like Cognizant’s Agent Foundry offer prebuilt tools and frameworks that can help reduce implementation time and support compliance efforts. This platform-agnostic solution supports the full lifecycle of agent deployment, from discovery to scale, and integrates seamlessly with existing enterprise systems.

Finally, cultural transformation is critical. Since 70% of scaling challenges are organizational, insurers must foster a culture of change, accountability, and long-term thinking to fully realize AI’s potential.¹

Move to agentic AI with confidence

Agentic AI is not just a technological upgrade, it is a strategic imperative for insurers seeking to thrive in a rapidly evolving landscape. By addressing structural challenges and embracing AI as a catalyst for transformation, insurers can unlock new levels of efficiency, personalization, and resilience.

The path forward requires bold leadership, cross-functional collaboration, and a commitment to continuous learning. Those who invest in scalable frameworks, such as AI Centers of Excellence and industry accelerators, will be best positioned to lead the next era of insurance innovation.

Explore solutions for agentic AI in insurance

• Explore insurance solutions and resources on Microsoft for Insurance.
• To learn about how Cognizant and Microsoft are partnering to help insurance companies advance their cloud modernization journeys, see the Cognizant and Microsoft Partner Page.

¹ Insurance leads AI adoption. It’s time to scale

² 2025 marks sixth year insured natural catastrophe losses exceed USD 100 billion, finds Swiss Re Institute

³ Sedgwick optimizes claim workflows with AI application Sidekick and Microsoft integration

⁴ The future of AI in the insurance industry

The post Microsoft and Cognizant: Delivering on the promise of agentic AI adoption in insurance appeared first on The Microsoft Cloud Blog.