Credit Risk Analytics and Credit Scoring

Build practical skills in credit risk analytics, credit scoring, model governance, and responsible model deployment for modern banking, BFSI, fintech, risk, analytics, and lending environments. You’ll learn how credit-risk analytics supports lending and portfolio decisions, from defining business and risk objectives to preparing regulatory-ready customer data. You’ll explore data quality controls, privacy, data lineage, default definitions, dataset partitioning, model inventories, vendor solutions, predictive variables, and governance across the model lifecycle. As you progress, you’ll evaluate model validation and calibration, interpret score distributions and population shifts, and communicate portfolio-risk insights to management. You’ll also apply score cutoffs, manage overrides, assess vendor scorecards, benchmark models, and examine deployment controls, parameter drift, performance monitoring, and prediction lineage. By the end of the course, you’ll be able to evaluate credit-risk models and data, interpret model performance, support consistent lending decisions, and monitor deployed models against portfolio needs and risk appetite. The course uniquely connects credit scoring with governance, responsible data practices, validation, deployment, and ongoing monitoring.