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Financial institutions are awash in quality data that informs them about many of the essential priorities and concerns of their customers. This data comes from interactions with customers via multiple internal channels as well as from third party channels such as social media or other online interactions. But the challenge remains: how to make sense of the data and turn it into actionable insights? Security measures and know-your-customer (KYC) requirements may have made banks very adept at discerning customers’ identities, but very few institutions understand their customers and their specific needs. This disconnect has far-reaching implications.

Outsider perceptions

The most notable problem, from customers’ perspective, is the dissonance that occurs when they are forced to jump through additional hoops when interacting with their financial institution. For example, when applying for a loan or a line of credit, most banks will ask for proof of income, even though 82 percent of workers in the U.S. have their paychecks directly deposited. This disconnect calls into question not only what a financial institution is doing with the data they collect but also whether different parts of the institution even understand what the other parts are doing.

Would you accept financial product advice from an institution that obviously doesn’t understand your situation or even try? Failure to understand and employ data properly for personalized service undermines the trust that banks are hoping to rebuild and that has been the cornerstone of their relationships with their customers for decades.

Creating cohesiveness in the cloud

But what if a financial institution employs a different data model, one where the data does not reside in separate systems, but rather a secure data lake? The benefits of such a model are numerous, but there are two that are clear: cybersecurity and advanced analytics. From a security perspective, there are fewer attack surfaces, and from an analytical perspective, consolidated data is the underpinning for machine learning and AI, thereby allowing for true learning about customers and their individual needs.

Imagine harnessing the near infinite computing power of the cloud to drive real-time AI decisioning that could identify customers immediately and use a financial institution’s wealth of data to offer customized products and services. No longer are customers an amalgamation of account numbers and credit scores, but individuals with unique circumstances to whom a bank can present tailored offerings.

Digitally restoring loyalty

In the early part of the 20th century, a bank manager might have known all his clients personally—for example, in terms of the yields on their crops or the profitability of their stores—and could recommend financial products that would help customers succeed in their endeavors. But unfortunately, this quaint ideal was not scalable with the growing complexity of goals, relationships, and financial products. Cloud computing, and the analytics and AI that it enables, has changed the game for how financial institutions are able to interact with their customers: now they can do so individually, yet at scale.

Clients are looking for an intuitive and orchestrated experience—a partner who understands their needs and offers them proactive assistance. Financial institutions are uniquely positioned to be orchestrators of highly differentiated, personalized services and value in the form of intelligence derived from vast stores of customer data. The key to delivering on this promise is mastering the data.

Enabling innovation is Microsoft’s core mission: we understand that transformation can be challenging, but we also believe that it holds incredible promise. Our approach is to leverage technology in novel ways—enabling business agility with the tools that will define the future of customer interaction.

As your trusted technology partner, we offer both industry know-how and enterprise-grade solutions. And we can help you wherever you are on your digital transformation roadmap.