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

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

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

1. Empowering relationship managers

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

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

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

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

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

2. Improving the quality of client interactions

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

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

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

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

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

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

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

3. Modernizing risk and core systems

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

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

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

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

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

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

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

4. Transforming trade finance

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

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

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

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

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

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

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

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

From experimentation to operating advantage

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

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

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

Learn more

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

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

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

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

When 70% of Microsoft 365 Copilot users report that the integration of generative AI into their everyday applications and tasks makes them more productive,¹ firms can see how AI can fundamentally revolutionize their businesses—starting by empowering the people who keep their companies running.

Historically, technology innovations have often not focused foremost on the needs of the average worker. Rather, they were often focused on empowering executives or driving loftier business goals such as enhancing competitiveness and profitability or powering new products and services. Generative AI is distinct in that it is tailored to benefit employees first. 

Helping banks and other financial institutions take full advantage of AI is central to our work at Microsoft. In the past two years, we’ve worked intensively with firms around the world to explore new avenues of AI innovation, with use cases that span an incredible range of opportunities. None have been more noteworthy than employee empowerment.

For many users, this happens initially with Microsoft 365 Copilot, which is embedded into apps like Word, Excel, PowerPoint, Outlook, and Microsoft Teams to integrate AI directly into everyday work tasks. 

Copilot removes drudgery and empowers employees 

When every employee has an AI assistant that helps them work better and faster, the sky is the limit on innovation. And it couldn’t come at a better time. According to the Microsoft 2025 Annual Work Trend Index, 53% of leaders say their company’s productivity needs to increase, yet 80% of the global workforce reports lacking the time or energy to do their job.¹

This “capacity gap” is why 82% of leaders expect to use digital labor to expand their workforce in the next 12 to 18 months. For many, the journey starts with Copilot and related cloud solutions that remove the drudgery of work and help people do the same work better and faster. 

4 ways Copilot is delivering immediate impact in financial services 

Enhanced productivity is the broad term for an important set of benefits that generative AI can deliver. For financial services firms, the specific use cases and benefits span many areas but we will concentrate here on four in particular: summarization, content creation, process optimization, and real-time insights.

1. Summarization
One of the most valuable features of Copilot is summarization—the ability to instantly produce a customized summary of anything from a recently conducted meeting to a transcript of a customer conversation, to summaries of whitepapers and PowerPoint presentations. In a fast-paced environment where analysts, advisors, and other professionals juggle multiple tasks, having a tool that can immediately extract key takeaways and follow-up actions is invaluable.

A good example is the experience of Hargreaves Lansdown, a leading United Kingdom financial services company that was early to embrace Copilot. Until recently, advisors had to manually take notes for customer meetings and later transfer them into a branded document, a process that could take up to four hours. With Copilot summarization and Microsoft Teams Premium, the process is being cut to as little as one hour through the automatic generation of meeting summaries, documentation, and action items.

Copilot not only speeds summarization, “it’s also good quality information,” says Systems Operations Manager Daniel Toman. “We know nothing is being missed.”

2. Content creation
The process of drafting emails, building presentations, and writing important client documents can be time-consuming and frustrating. Copilot eases that burden by identifying relevant source materials, using natural language processing to create messages and documents, and pulling information from across the Microsoft Graph (an API that connects all Microsoft 365 data, documents, and users.) The benefits for financial services include improved client engagement, scalable workflows, and AI-powered insights.

Content creation is delivering major benefits to Bank of Queensland (BQQ), which adopted Copilot to enhance collaboration and productivity. It has helped decrease the time required to draft internal manuals by 99%, marketing content by 88%, and human resource document drafts by 75%. Those gains are credited with improved customer service and operational efficiency—plus greater innovation in a competitive market.

“Copilot puts the power in the hands of the employee to be able to find efficiency.”

—Hayley Watson, Head of Enterprise Capability, BOQ

And to offer another customer example, in the United Kingdom, Floww, a financial infrastructure platform provider, increased employee efficiency by up to 20%, using Copilot to process massive quantities of data spanning technical documents, regulatory compliance requirements, and financial information, then condensing and delivering reports in easily accessible, shared formats.

3. Process optimization
Too many important processes in financial services are still dependent on manual tasks that can slow productivity and drain resources. Copilot solves this by automating processes and enhancing collaboration. The net benefits for firms include streamlined operations, fewer errors, and more time for employees to focus on high-value work.

For example, Dutch wealth management firm Van Lanschot Kempen wanted to help advisors focus more on personal connections with clients and found that too much time was being spent on unautomated tasks. So, they enlisted Copilot to improve common workflows and processes. Copilot now helps save them time by drafting emails (in multiple languages) in response to prompts, taking notes in meetings, and identifying and automatically assigning action points. An added benefit is that it reduces the language barrier and increases the quality of emails and documents.

“Having to take notes and structure action points and recaps accounted for around 40% of my time, Copilot is now my assistant during and after meetings.”

—Johanna Albert, Digital Adoption Specialist

Elsewhere, LGT, a Liechtenstein-based international private banking and asset management group, is using Copilot in their legal and compliance departments—for instance, to simplify reviews of lengthy contract documents. What used to take up to four hours can now be done in about 30 minutes. And global payments platform Paysafe cut the amount of time spent building technical documentation by up to 50%, automating meeting documentation, information retrieval, and document creation.

4. Real-time insights
Copilot is redefining how financial services professionals work by providing real-time insights that empower employees and enhance decision-making. Imagine, for example, a mortgages operation manager who needs to ensure efficient loan processing while maintaining compliance and optimizing customer satisfaction. Throughout the process, Copilot instantly retrieves and summarizes critical data, improving both speed and accuracy. Across firms and roles, real-time insights help with everything from research and predictive analysis to collaboration, workflow automation, and decision-making.

The scope of the transformation this represents is reflected in Microsoft’s strategic partnership with Moody’s to co-create new products and services for research and risk assessment. Built on a combination of Moody’s robust data and analytical capabilities and the power and scale of Azure OpenAI Service, the partnership creates innovative offerings that enhance insights into corporate intelligence and risk assessment. One early offering is a new copilot, “Moody’s CoPilot,” deployed to Moody’s 14,000 global employees, which helps drive firm-wide innovation and enhance employee productivity in a safe and secure digital sandbox. 

Beyond Microsoft 365 Copilot: A new breed of financial services agents 

Microsoft 365 Copilot is really just the start. Microsoft also offers a range of Copilot solutions tailored for businesses sectors, including Microsoft Copilot for Finance, which connects to financial systems such as Dynamics 365 and other leading financial management platforms to assist with tasks like analysis, reconciliation, collections, and communications.

For firms who want to do more, Microsoft Copilot Studio makes it easy to build custom AI agents without requiring extensive coding expertise. It also includes a powerful new Researcher Agent that helps with complex, multi-step research. It delivers deep insights with greater quality and accuracy than previously possible to advance important tasks like market analysis, opportunity identification, and key reporting. 

Set the stage for new waves of AI innovation 

Employees who use copilots are not just more productive, they’re more likely to be engaged and innovative. For many, their embrace of AI will become career accelerators. Empowered employees will help drive the reinvention of even the most established firms, and make endless contributions to the upstarts and even those we can’t imagine today.  

As the rapid evolution of AI continues, we will soon see the adoption of new classes of AI agents that won’t simply provide assistance but will execute tasks and orchestrate action autonomously. The future is approaching rapidly: our research suggests that global business leaders expect their teams will be training (41%) and managing (36%) AI agents within five years.¹  

Soon, human-agent teams will upend the org chart, and every employee will become an agent boss. Now is the time to get them up and running. 

Learn more 

• Visit Microsoft for financial services to learn more about how Microsoft empowers financial services organizations to achieve more.
• Learn more about using Copilot in Financial Services and download the Financial Services scenarios kit.

Reinventing productivity

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¹ The 2025 Annual Work Trend Index: The Frontier Firm is born.

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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

Unlock business value and deepen customer relationships

Learn more

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

Today’s customers want personalized banking with the ubiquitous access that comes from a seamless, omnichannel experience. They expect to be able to communicate and interact with their bank anytime they want, anywhere they want. To stay competitive, banking organizations need to meet this expectation.

“A lot of the millennials and Gen Z…expect banking organizations to have the same level of convenience and personalized service they’re getting from Amazon and other online providers.”

– Chad Hamblin, Director Banking Related Solutions for Industry, Worldwide Financial Services Group, Microsoft

Different from hard-goods companies, many financial services organizations offer similar products and services as their competitors—which makes it challenging for organizations to cut through the sea of sameness to attract new customers. In a “switching economy” where consumers are more apt to shop around for the best deal or experience, it’s difficult to retain existing customers. Personalizing the banking experience is one key way these organizations can differentiate themselves, that’s why we’re seeing a lot of them invest heavily in customer channels.

What does a personalized customer experience look like?

Providing a personalized customer experience means delivering the right message at the right time in the right channel. Banks need to go beyond next-best offers and targeted marketing to create relevant end-to-end experiences for each individual customer.

To create this individualized banking experience, organizations must leverage their data in new ways to get a comprehensive view of each customer and pinpoint their individual needs. Banks should focus on four key areas, or pillars, of a seamless customer experience:

1. Customer engagement
2. Customer insights
3. Predictive analytics
4. Augmented intelligence

“[Your customers] aren’t in the market for a mortgage, [they’re] in the market for a home. So, the question becomes, how can a bank become an integral part of its customers’ journeys?”

– Chris Dickin, Executive Vice President, Ecosystems and Product Marketplace, Zafin

The four pillars of a seamless customer experience in banking

1. Customer engagement

The customer engagement pillar is where banking organizations get a 360-degree view of the customer and track their interactions, usually with a traditional customer relationship management (CRM) system or customer system of record. CRM systems help organizations establish a baseline and serve as an important foundation for customer data.

2. Customer insights

The second pillar, customer insights, provides a deeper understanding of these customers. This might involve adding other data elements like lifetime value and social presence. The goal is to add more context to the data you’ve already compiled so you can better tailor each customer’s interaction with your business.

3. Predictive analytics

The predictive analytics pillar is all about using advanced analytics and machine learning to identify trends and patterns, then using that information to identify latent needs and preferences and take proactive action.

4. Augmented intelligence

The final pillar is augmented intelligence which is critical to the creation of a truly seamless customer experience. Ideally, AI is used in conjunction with human-led channels as a way to augment and complement them.

To learn more about all four pillars of the personalized customer experience, watch the on-demand webinar, Digital Transformation in Banking: Relevant, Personalized Customer Experiences. 

The key to a truly personalized banking experience is combining all four of these pillars in one easy-to-use process. Banking professionals need solutions that not only capture but interpret data so they can tailor offerings to each individual customer’s needs and wants.

Microsoft Cloud for Financial Services

To help bank employees create more personalized, differentiated customer experiences, Microsoft has created Microsoft Cloud for Financial Services. With capabilities to deliver differentiated experiences, empower employees, combat financial crime, and increase compliance, this cloud solution is specifically designed to help financial services organizations reach their current and future goals.

We’ve also partnered with Zafin, a leading cloud platform for product and pricing lifecycle management​. With Zafin, banking organizations can extend and enhance the core capabilities of Microsoft Cloud for Financial Services. This product lifecycle management solution facilitates workflow, collaboration, and communication across different domains of the organization. Relationship managers and banking specialists can empower a smooth journey toward a mortgage application using Microsoft and Zafin solutions. If the potential customer lands on the bank’s website and wants to learn about what loans are available, it will display available products based on how relevant they are for that particular customer.

“So rather than presenting a customer with a whole range of hundreds of products, you can…[show] only those products that the customer is eligible for.”

– George Winch, Senior Director, Ecosystems, Zafin

This kind of data-driven feature ensures customers don’t get overwhelmed with the bank’s entire catalog of products and services (many of which are of no value to that customer). It helps banks improve their customer experience immediately.

Create a customized banking experience today

This is only one potential application of these solutions. To see a demonstration of how the combination of Microsoft and Zafin solutions help customers have a more personalized experience throughout their financial journey, watch the on-demand webinar, Digital Transformation in Banking: Relevant, Personalized Customer Experiences. 

Learn more about Microsoft Cloud for Financial Services and how it can help your organization unlock new value with deeper customer connections.

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