Validation of "Neural Networks based Wire Payment Fraud Detection Model" for strategic and regulatory submission for a large US based Financial Institution
Client : Large US Financial Institution
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Objective
To validate a neural-network-based wire payment fraud detection model for regulatory submission and enhanced strategic decision-making. The goal was to validate the bank’s model for two large US portfolios and provide a decision on the model's use.
CRISIL's Solution
CRISIL GR&RS performed validation of the wire payment fraud detection model. This involved:
Reviewing all documents submitted for validation and assessing whether more information was required from stakeholders to complete the thorough validation process;
Holding regular discussions with stakeholders to ensure all ambiguities in their reports were removed before performing additional tests - at CRISIL’s suggestion - to evaluate the model's performance.
Validation Methodology
Thorough study of the model development document and other documents and research articles relevant to understanding of the model;
Review all tests done by the developers for evaluating the neural networks model performance;
Prescribe and review results of performance tests other than those done by the model developer.
Validation Highlights
The neural network model was found to be not performing well
Client Impact
Model validation completed in short timeframe
Significant shortcomings in model performance identified
CRISIL provided insights and suggestions, including new performance metrics to evaluate fraud model and enhancements to the quality of model documentation
Created a thorough validation document to be used by the client for CCAR submission
Provided specific recommendations about future use of the model in the two target portfolios
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