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.

The contact center agent who answered Matt’s call immediately authenticated him with voice biometrics, removing potential friction from this highly charged moment. Guided by AI-powered insights and suggestions, the agent then walked Matt through a quick yet complete intake process. Automatically, the contact center platform sent Matt an SMS link to upload photos of the damage, as well as follow-up communications with details of his claim. Afterwards, the platform generated a call summary, and extracted details and insights for future calls. And Matt, despite the fender bender, felt better about his insurer for making the process so easy.

This scenario, which until very recently would have seemed futuristic, is just one of many examples of how generative AI is redefining the very notion of a contact center for insurance companies, banks, and other capital markets firms. It is only one example of the kind of transformation we at Microsoft together with our global partners are helping customers realize with Microsoft Cloud for Financial Services.

The contact center cost conundrum

COVID-19 highlighted the critical importance of contact centers to financial services businesses, as housebound customers had only phone calls, emails, and digital access to connect with their financial service providers. It was suddenly left to contact center agents and customer-facing digital tools to handle the lion’s share of many key functions, such as responding to customer problems, providing important information, and assisting with transactions and account issues.

As COVID-19 pressures ease and new business requirements emerge, companies now face new challenges to keep their contact centers vital and efficient. Among them:

• Modern customers expect personalized experiences, and they get frustrated by disconnected interactions and incomplete information that can result from siloed systems and data.
• Customers demand simple and effective self-service experiences and are turned off by limited solutions.
• Contact center agents want to focus on solving problems for customers, but struggle to use cumbersome tools and difficult training requirements, which can result in high rates of attrition.

The leaders we’ve talked to in financial services firms recognize that contact centers represent a major opportunity to enhance the bottom line and drive new revenue. But contact centers have traditionally been cost centers. Now with generative AI, companies have quickly recognized that the new capabilities of large language models (LLMs) and data analytics can further unlock business value.

The generative AI contact center

Generative AI is transforming contact centers with a set of powerful new capabilities such as:

• Content creation: authoring content that is similar in style and tone to what people are used to.
• Summarization: writing summaries from large volumes of data.
• Semantic search: using natural language to understand the intent behind queries.

These provide the basis of innovations such as intelligent chatbots, virtual assistants, and customized solutions that use natural language processing, interactive voice response (IVR), trend and sentiment analysis, and much more.

In practical terms, generative AI transforms the contact center into a highly efficient asset that understands abstract context, provides engagement at a near-human level, and empowers agents and employees to deliver service faster and more effectively.

The potential impacts of generative AI innovations are huge. Personalized interactions can dramatically boost service-to-sales conversions. Perceptive self-service tools can spike customer satisfaction through instant gratification. And tools that reduce data entry can improve agent productivity. On the expense side, AI can reduce labor costs by reducing training requirements for agents who, in turn, can deliver better customer experiences that increase customer loyalty.

We see generative AI providing near-term benefits for financial services firms in the following broad categories of use cases:

• Agent assistance: Empowering customer service and helpdesk agents with bots to provide insights and next-best-action guidance derived from disparate knowledge sources across the organization. Assistance can also incorporate multi-language transcription, management bots for forecasting simulations, and automated call scoring.
• Fraud detection: Continuously monitoring and analyzing incoming data streams for potential fraud. For example, advanced biometric detection not only enables immediate authentication based on a customer’s unique voiceprint, but it also recognizes an attempt to impersonate the customer.
• Interaction analytics: Identifying patterns in customer behavior to deliver increasingly personalized responses. Generative AI can look across every conversation (voice, text, email, and so on) to identify trends, inform product improvements, enhance agent coaching, and ensure compliance.
• Self-service: Automating routine tasks and repetitive queries with conversational chatbots and IVR capabilities that enable computers to have conversations with customers. Over time, generative AI can also create personalized responses in customer engagements based on experience and learning from across the organization.

Put generative AI to work in your contact center

Microsoft offers a broad suite of solutions and capabilities that addresses the contact center needs of financial services organizations. The generative AI use cases listed above, enabled by Microsoft technology, can be provided on any contact center platform including those from NICE CXone and Solgari, which run on Microsoft Azure. In addition, they can be provided through solutions in Azure from partners such as Verint and Callminer. Microsoft Azure data and AI services application programming interfaces (APIs) integrate easily with call center platforms to enable new use cases, which are enhanced greatly through the solutions and contributions of our global partner ecosystem and can be integrated into Microsoft Teams and Dynamics 365.

Contact center transformation at Money20/20 USA

We were excited to showcase Microsoft’s remarkable innovations in contact center transformation at the Money20/20 USA conference in Las Vegas, Nevada from October 22 to 25, 2023. We demonstrated innovative solutions and highlighted the progress our customers and partners are making with solutions built on Microsoft Azure OpenAI Service and the Microsoft Cloud. Among the highlights:

• At the Microsoft AI Lounge Powered by Nvidia, we hosted 30 presentations from more than 20 partners across a broad range of demos, case studies, and thought leadership topics. We were delighted to showcase how Microsoft is using generative AI in our own contact centers (with all 55,000 of our agents), which is delivering remarkable benefits.
• Microsoft partners presented an innovative set of solutions featuring generative AI scenarios in our lounge and in the Money 20/20 pavilion.
• Microsoft’s Daragh Morrissey, Director, Worldwide Financial Services, discussed Microsoft’s broader generative AI approach in a panel discussion on “AI, Quantum, and Crypto: Get Ready for the Impact on Payments.”

Get started on your journey today

Using generative AI to reinvent the contact center is an unparalleled opportunity not just to deepen customer relationships but to unlock new business value. With generative AI and the Microsoft Cloud, financial services businesses can optimize costs, reduce time to value, enhance collaboration, and use data to deliver more impactful business outcomes.

Learn more by visiting our website where you can discover how Microsoft and our partners are working together to help our customers transform financial services. If you want to accelerate your transformation journey, feel free to reach out to us or contact your Microsoft representative or technology partner.

Microsoft Cloud for Financial Services

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The post How Microsoft and generative AI are transforming financial service contact centers appeared first on The Microsoft Cloud Blog.

With Microsoft Cloud for Financial Services, our customers are managing financial services data at scale and building solutions that improve customer experiences and operational efficiencies. With the advent of generative AI capabilities in Azure OpenAI Service, businesses can now unlock new value from their data not only to drive better customer outcomes but also to improve their protection against various kinds of financial crime—including fraud, electronic crime, and money laundering.

The financial costs and scale of these crimes are staggering. Worldwide, the estimated total of laundered money in a year is at least two percent of global gross domestic product, or USD800 billion.¹ For financial services organizations, the cost of financial crime compliance reached USD213.9 billion in 2021²—USD56.7 billion in Canada and the United States alone in 2022,³ a 13.6 percent increase from 2021.

Until recently, financial services organizations have felt hamstrung in their ability to combat the worst forms of criminal activity. They play a cat-and-mouse game with bad actors who use a wide variety of financial instruments in sophisticated ways, exploiting the distributed nature of the financial system to perpetrate their crimes. Criminals might, for example, engage in small transactions across many different institutions, or across different accounts in the same financial institution, to mask their activities.

Protecting privacy while advancing security

The global focus on digital privacy in an increasingly interconnected world is a cornerstone of trust, human rights, and individual empowerment. Privacy is mandated by legislation around the world, such as the Digital Charter Implementation Act 2022 in Canada and in the European Union’s General Data Protection Regulation. And of course, banks and other financial services firms also know that customers will vote with their feet if their data is leaked or mishandled.

At its core, privacy is about protecting personal information. And this poses some challenges in fighting financial crime, because it impairs organizations from knitting together a complete picture of what an individual bad actor or a group of bad actors may be doing. The keys are all there in transaction records, account information, customer relationship databases, and so on. But they remain off limits when they are associated with personally identifiable information.

Fortunately, businesses can now attack the problem using novel technologies such as confidential computing and AI that allow multiple parties to safely gain insights from financial data without violating privacy requirements.

Confidential computing and de-identification: New layers of protection

A host of modern, cloud-based capabilities and methods enables this shift. For one, data can be better protected in the cloud with solutions like Azure confidential computing. This unique service encrypts data while it’s being processed, meaning that data is no longer only protected at rest and in transit, but also in use. While in memory, it simply cannot be accessed by cloud operators, malicious administrators, or even privileged software such as a hypervisor.

The root of trust in Azure confidential computing resides in independent hardware. Not even Microsoft operators can access the encryption keys. This is what enables government customers to independently, cryptographically verify the identity and “known good state” of the cloud operating environment they are relying on.

Concurrently, regulators are beginning to recognize the impact of new techniques for de-identification, which obfuscates or removes personally identifiable information from data sets. Data masking, data perturbation, and differential privacy are some of the powerful tools and methods of de-identification that are proving their effectiveness by making data available to AI to deliver important insights without putting privacy at risk.

While securing the benefits of strong privacy protections, financial services organizations are now able to work across enterprise data sets—to reason over data from not just one location, but across different locations and potentially even different institutions. This dramatically changes how a firm handles data. Swift is just one recent example of a financial services firm that has benefited from these innovations in building an anomaly detection model for transactional data without copying or moving data from secure locations. And, significantly, it means that AI and related tools and technologies will now be able to explore, analyze, and spot trends and insights that not only help their businesses, but can have positive societal impact as well.

How AI helps financial services organizations

With AI, financial services firms have new capabilities for risk assessment and scoring, which can help prioritize investigations and resources. They can also benefit from pattern recognition, which can detect anomalies and suspicious activities across large sets of financial transactions, customer data, and other sources. This has significant implications for fraud management, which financial services organizations rely on to mitigate their risks. If a firm can show new levels of due diligence, underwriting costs can potentially be reduced.

Additionally, generative AI can be used to analyze a wide array of unstructured data from a variety of internal repositories to spot indicators of potentially suspicious activities. Natural language processing will assist in the delivery of regulatory documents, legal texts, and compliance reports. And financial institutions may realize broad organizational benefits through integration into productivity applications. At Microsoft, we’re all about democratizing AI and making these tools approachable and available not simply to the data analysts and mathematicians, but to people across the business. This is reflected in the broad innovations announced recently at Microsoft Build 2023, in which we have integrated AI into Azure, Microsoft 365, our development tools, and much more. These AI-powered products help surface more useful information for better decision-making and greater efficiencies across the organization.

The art of the possible

In our work with customers, we see a wave of interest in exploring the potential of these powerful new tools to fight fraud, money laundering, and other forms of financial crime. In Canada, privacy enhancing capabilities have long been bolstered by affirmation from the Information and Privacy Commissioner of Ontario that de-identification is a legitimate and valuable way to protect information, and enterprises have been provided with guidance on how to proceed. It’s powerful confirmation that organizations can leverage new approaches to address privacy considerations as they explore new opportunities. Once we light up the art of the possible, the dialogue quickly shifts and we can work collaboratively to solve these tough challenges.

Fight financial crime with the Microsoft Cloud

Collaboration is the key to industry-wide progress in the fight against all kinds of financial crime and fraud. Working well together is a core Microsoft value, and that means much more than ensuring that our products and tool sets are integrated. It means that we recognize that these challenges are bigger than us or any one company, organization, or entity. So, we promote and support the roles that every player in the ecosystem performs, from industry partners to government officials, regulators, law enforcement agencies, and of course customers.

For financial services organizations who want to explore these new possibilities, an exploratory engagement or proof-of-concept is a good way to examine how the technology and process puzzle pieces fit together. We’re constantly amazed at the inventive and impactful ways that customers are employing these tools to do better for their organizations and the world at large.

Read further in a recent post about how the Microsoft Cloud helps banks manage risk and discover real-world customer examples and other resources that show how Microsoft and our global partners can help banks deepen risk insights, facilitate regulatory compliance, and combat financial crime.

¹24 Alarming Money Laundering Statistics [New Data 2022 & Infographic], BusinessDIT.

²Global spend on financial crime compliance at financial institutions reaches $213.9 billion, Finextra.

³True Cost of Finacial Crime Compliance Study for the United States an Canada, LexisNexis Risk Solutions.

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