Expected Credit Loss Framework Implementation Case Study

This case study details the implementation of an Expected Credit Loss Framework by Tiger Analytics for a leading global Credit Bureau. The objective was to enhance forecasting accuracy and address regulatory scrutiny on existing loss forecasting models and stress testing. The client, a large multinational Consumer Credit Reporting Bureau, faced challenges due to limited documentation and compliance gaps, particularly concerning regulatory standards from the FRB, OCC, and FDIC. Tiger Analytics developed a robust framework that included detailed model documentation and adhered to relevant guidelines. The solution improved data quality and cleaning through feature extraction aligned with CCAR/DFAST frameworks. Additionally, it incorporated effective credit behavior segmentation and a Potential Default Probability Model. The case study outlines the technical architecture, including input data sources and variable transformation processes, leading to the deployment of a comprehensive Estimated Loss framework that met regulatory benchmarks and provided transparency in model documentation.