The client needed to validate a fraud detection model containing 12 fraud detection rules, including an ML model, to ensure accuracy and effectiveness in mitigating fraud risks.

Comprehensive validation and optimization of fraud detection model.

1. Input Data Analysis: Validated data sources and ETL process, highlighted and resolved data issues, and verified data transformation rules.

2. Model/Rule Validation: Evaluated numerical thresholds and parameters, refined threshold values using statistical sampling and hypothesis testing, validated rule codes and logic.

3. Model Optimization: Performed rules and model optimization to minimize false positives, conducted statistical evaluations (CSI/PSI) to ensure model stability.4. Implementation and Monitoring: Validated model implementation code and package, ensuring comprehensive monitoring and performance assessment.

Enhanced fraud detection accuracy and reduced false positives.

The validation process ensured the fraud risk mitigation process was accurate, suggested various reusable tools for future validations, and reduced false positives, ensuring rules operated according to business intuition.