Objective:

The objective of the project is to give a hands-on experience of Machine Learning and Artificial Intelligence to the Participant. The participant will learn to apply ML-AI algorithms suggesting scores of credit risk. They even stress test the scores and see for the stability of the scores.

To understand stress testing, considering the home loan buying example, the loan manager understands at a granular level whether a particular loans goes to default or not by tweaking the expected gdps and housing prices (basically macro economic conditions) and predict credit risk scores to understand the stability of the creditors commitment to repayment.

Methodology:

Participants will research and gather publicly available credit risk data from various sources. Perform a classification activity for credit risks. Eventually score their default probabilities and estimate the risk involved in individual loans.

Benefits:

This project will help participants to have first-hand experience of ML in finance  industry. The use cases are also extensive in tele communications , media and so on. It is a parallel application to churn prediction.

Duration: 3-6 Months (Online – Offline, Instructor based)