Credit Risk Modeling using R Programming

Every time an institution extends a loan, it faces credit risk. It is the risk of economic loss that every financial institution faces when an obligor does not fulfill the terms and conditions of his contracts. Measuring and managing the credit risk and developing, implementing strategies to help lowering the risk of defaults by borrowers becomes the core of any risk management activities.

Financial institutions make use of vast amounts of data on borrowers and loans and apply these predictive and statistical models to aid banks in quantifying, aggregating and managing credit risk across geographies and product lines.

In this course, our objective is to learn how to build these credit risk models step by step from scratch using a real life dataset.

The course comprises of two sections: 1) Developing a credit risk scorecard and 2) Developing a Probability of Default (PD) model. We will build a predictive model that takes as input the various aspects of the loan applicant and outputs the probability of default of the loan applicant. PD is also the primary parameter used in calculating credit risk as per the internal ratings-based approach (under Basel guidelines) used by banks.

In this course, we will perform all the steps involved in model building and along the way, we will also understand the entire spectrum of the predictive modeling landscape.