Machine Learning for AML Scoring Increases Audit Effectiveness

Case Study

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

Challenges

  • 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

Solution Methodology 

  • Stitched together customer 360 data from a variety of data sources within the bank
  • Prepared training dataset capturing 1000+ behavioral features across different banking products and channels
  • Created velocity features for transactions across different channels
  • Ran supervised and unsupervised learning approaches on past investigations to generate a SAR ‘signal.’
  • Fine-tuned model performance to reduce false alerts, without compromising on effectiveness in capturing maximum suspicious events
  • Validated model performance in terms of AUC, precision and recall at optimized thresholds across train, test and out-of-time validation set

Business Impact

  • High precision of the model optimized investigation activities for high-risk customers
  • The model was also used to reduce audit effort by 80% for very low-risk customers
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