Statistical Credit Scorecards: A Predictive Modeling Tool for Risk Estimation
The increasing competition and massive push towards digitalization and automation has shifted credit underwriting from traditional human judgement-based approach to more advanced & scientific methods. In light of this, scorecards have become absolutely vital to achieve effective underwriting process. Credit scorecards rank borrowers by their credit worthiness and quantify borrower’s probability to default. These scorecards are deployed not only by banks but also by other organizations such as telecom players, insurance companies, government departments etc. as these scorecards provide a wide spectrum of benefits including reduced exposure to high-risk accounts, decreased bad accounts, fact-based underwriting process and increased automation.
Scorecard development is a statistical process which involves multiple steps starting from defining business objectives and requirement, defining the right good and bad customer definitions and combining multiple datasets of customers internal data like behaviour & demographic data, financial and external bureau data for scorecards development. It’s a step-by-step and iterative process which requires both statistical analytics and business knowledge. Therefore, it is essential that organization rigorously develop and frequently monitor & validate underlying statistical models for scorecards to consistently achieve high predictability and realize their benefits.
To gain more insight into the statistical credit scorecards, please download our detailed whitepaper.