CASE STUDY September 17, 2023

Machine Learning for AML Scoring Increases Audit Effectiveness


Business Objective

Our client is a leading European financial institution, offering banking, wealth management, and payment solutions to their retail and business clientele.

The financial institution’s internal audit team responsible for money laundering wanted to:

  • Develop an independent data-driven system to leverage client transaction information
  • Intercept high-risk transactions and flag clients not yet reported as suspicious transactions
  • Increase the effectiveness of internal control currently based on random samples or selected by experiential triggers


  • Limited number of tagged cases corresponding to various money laundering scenarios
  • Disparate data sources within the bank
  • Limited visibility on suspicious operations among non-customers
  • No benchmark of model performance: Not all cases flagged by the model were investigated, making an evaluation of false/true positive cases complex
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