As AI becomes embedded in financial services and banking operations, there’s new pressure on something less glamorous than the AI model: the customer data behind it. AI readiness depends on accurate customer data – and not just as a deployment milestone. It’s an ongoing condition and warrants integrated data quality processes that keep customer identities current, connected, and usable.

It’s not a new problem for incorrect or outdated data to create inefficiency in financial services. What changes with AI is the scope and speed at which that information can influence (and damage) operations. An outdated address once meant only returned mail. Now it turns data quality from an abstract concept into a banking reality, with the potential to affect concrete operational processes like fraud assessment, customer authentication, personalized services, or automated interactions.

AI cannot on its own determine whether a specific customer record correctly reflects a real person or business – that’s wholly different than identifying patterns or processing enormous volumes of information. Its analysis may be sophisticated but will still be wrong if based on data that is incomplete, outdated, duplicated, or incorrectly connected. Worse, AI and all its efficiency gives this inaccurate data more opportunities to affect decisions, creating impact across operations. For financial institutions, AI readiness begins and ends with confidence in customer identity.

Vastly different from transaction records, which record an event that took place at a particular moment, customer data is fluid.

Melissa recently surveyed 150 enterprise technology decisionmakers across banking, fintech, and retail. Among the respondents, 77% said they were entirely or mostly confident in their transactional and business data.

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