Over the past decade, financial institutions have invested enormous resources in digital transformation: new channels, new technologies, new customer engagement tools. Customer data is now more abundant than ever. But, as Artsiom Liashanau notes, most banks and credit unions still struggle to translate this data into action. More data doesn’t mean more value. Many financial institutions today are sitting on a vast array of customer data, but a chasm still exists between accumulating information and making concrete decisions based on it, says Artsiom Liashanau. He cites an argument that explains this gap structurally, rather than as a lag in individual banks: The growth of data volumes has long outpaced the growth of operational processing capabilities; Decisions about the next step for a client are still largely made manually by analysts or marketers, rather than by a real-time system; While the cycle is measured in days or weeks, the window for a client to respond to an offer is often already closing. That’s why the winner isn’t the one who has collected more data, but the one who has learned to make better decisions faster than the competition. This is the difference between possessing information and the ability to act on it, the expert emphasizes. Why the conversation about AI in banks is often misplaced Artsiom Liashanau points out that much of the current discussion about artificial intelligence in the financial sector isn’t focusing on its most valuable application. The biggest opportunity artificial intelligence (AI) offers financial institutions isn’t content generation or the automation of routine processes. It’s what can be called decision intelligence, he explains. He justifies this priority with a specific ROI logic: Automating routine operations and text generation reduce costs but do not create new revenue. They do what the bank already does faster; Decisions made more accurately and ahead of competitors, for example, offering a product when the customer is truly ready to consider it, directly impact conversion and retention; The value of AI here is measured not by the number of automated tasks, but by the number of correct decisions made before the customer switches to another bank. Properly built AI does not replace the expertise of bank employees, but rather enhances it. It expands on what an analyst or manager physically cannot track manually for each client, adds Artsiom Liashanau. Why behavioral data is more important than demographics One of the key topics the expert raises is the shift in approach to customer segmentation. For decades, financial marketers relied primarily on demographic segmentation: age, income, family composition, geography. These are still important inputs, but they only tell part of the story. Imagine if a bank could recognize signs of financial stress in a client even before they seek help. Or notice a change in their life stage and proactively offer relevant financial advice. This is no longer just personalization, it’s understanding the client across their entire client base, says Artsiom Liashanau. Key takeaway for banks The task of banks and credit unions isn’t to become more like tech companies. The task is to use technology to enhance what has always been their strength: building trusting relationships with clients. The practical conclusion from this logic, according to him, boils down to a specific management priority: Invest not so much in the volume of collected data, but in the speed and accuracy of turning it into a solution tailored to the specific client; Embed analytical models into real operational processes as employee interfaces, automatic triggers, and communication scripts, rather than leaving them as separate reports; Parallel to the implementation of AI, build transparent control mechanisms and explainability of decisions to ensure speed is not achieved at the expense of customer trust. Technology is a tool. The decision on how to use it in the client’s best interests remains a management issue, not a technical one, concludes Artsiom Liashanau.
