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AI-based Customer Risk Rating Models for Efficient AML Compliance

Shubham Jain

Functional Consultant, Banking, Financial Services, and Insurance, TCS

Sukirt Singh

Business Intelligence Analyst, Analytics and Insights, TCS

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Enhancing customer risk assessment processes to combat financial crime

With the rapid spread of digital banking and expansion of risk horizons, money-laundering has emerged as a leading crime in the financial services industry. To prevent financial crime and improve anti-money laundering (AML) compliance, banks must improve customer risk assessment through accurate risk ratings. However, banks face challenges in the customer risk rating process. Manual data collection and infrequent customer information updates result in inadequate weightages assigned to risk factors leading to inaccurate risk ratings.

Banks must employ AI-backed risk detection models to automatically detect deviations in customer behavior that point to changes in customer information, proactively update the information in the AI model, and revise the risk rating. By adopting an AI-backed risk detection model, banks can:

  • Update customer risk ratings in real-time 

  • Enhance customer risk assessment

  • Boost fraud detection 

  • Improve AML compliance

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