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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 Artem Lyashanov notes, most banks and credit unions still struggle to translate this data into action.
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 Artem Lyashanov.
He cites an argument that explains this gap structurally, rather than as a lag in individual banks:
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.
Artem Lyashanov 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:
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 Artem Lyashanov.
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 Artem Lyashanov.
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:
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 Artem Lyashanov.
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Data quality management supplies clean, validated data while AIOS governs the meaning attached to it. Data quality management is necessary, but not sufficient on its own. Strong data quality management programs need all three working together.
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The article comprehensively explains Data Quality Management and explores its framework, implementation, and best practices. Data quality management is vital for making sense of your data, and ultimately improving the bottom-line. Data quality management (DQM) is a set of practices to detect, understand, prevent, address, and enhance data to support effective decision-making and governance in all business processes. In both cases, you’re applying the principles of data quality management in the real world. You can best observe data quality management in action through the lens of a modern day data problem.
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