AI Returns in Banking Threatened by Data and Skills Gaps

Banks are increasing their investment in artificial intelligence, but data quality, fragmented infrastructure and skills shortages are limiting the returns they can achieve from AI deployment. Industry research cited by FinTech Global indicates that financial institutions are moving beyond experimentation, yet many still lack the data foundations and technical capabilities needed to integrate AI effectively into core banking and risk processes.

The challenge extends beyond the availability of AI models. Banks need reliable and accessible data, modern technology architecture and employees capable of developing, validating, monitoring and governing AI systems. Fragmented data can weaken model performance, while legacy infrastructure can make integration more complex. Skills gaps can also increase dependence on external providers and make it harder for banks to understand model behaviour, manage AI risks and maintain systems after deployment.

For banks, the findings underline that AI investment needs to be accompanied by data governance, workforce development and strong model-risk controls. AI can support areas such as fraud detection, compliance, customer service and risk assessment, but its effectiveness depends on the quality of the underlying data and the institution’s ability to govern the technology. Building these foundations will be important if banks are to convert AI adoption into measurable and sustainable returns.

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