- Development of loss forecasting model for CCAR submission using State Transition Matrix modeling methodology- Exploring opportunities to incorporate CCAR models for BAU usage- Led team of analysts for development of PD, EADF and recovery rate models for CCAR 2019 submission

- Developed EAD model - a component of the suite of loss forecasting models for CCAR submission. Highlights: 1. used discrete time survival analysis methodology; 2. incorporated macroeconomic factors in the model to take into account various stress scenarios- Led the development of recovery rate prediction model for CCAR submission- Led team of analysts to develop price sensitive model to predict the dollar loss rate, using discrete time survival methodology in PD-EAD-LGD framework, for credit card acquisition portfolio- Led team of analysts to develop loss forecasting model for balance transfer campaign offer optimization

- Developed a standardized diagnostic tool to identify the reasons for any breaking trends in the weekly performance of a vertical or a category- Created standard methodology to identify lapping effect by analyzing historical analyses

- Validated credit risk models spanning Acquisition, Collections & Recoveries, Fraud, Line Assignment and Customer Management- Developed a price sensitive loss forecasting model and created detailed process document and data repository for future reference- Recalibrated existing credit risk models with latest customer data

- Analyzed the energy consumption patterns of business consumers of a large UK based energy firm to identify smart consumption opportunities- Analyzed the contact pattern of digital consumers to identify the reasons of contact and subsequently provide opportunities to reduce the same- Analyzed the online behavior of digital consumers to identify the areas of improvement in the online portal to reduce customer contacts- Validated credit risk models spanning Acquisiton, Collections & Recoveries, Fraud, Line Assignment and Customer Management