Hack Session: Building an End-to-End Credit Risk Model

Nov 14, 2019


Auditorium 3

60 minutes

Machine Learning

An applicant’s demographic and socio-economic profiles are considered by credit managers before a decision is made regarding his/her loan application. The manager’s goal is to minimize risk and maximize profits for the bank’s loan portfolio.

The manager shares the information that, for the type of loans the model would predict, if the borrower pays back the loan, the bank makes a profit of X% the value of the loan. On the other hand, if the borrower defaults, the bank’s loss is 100%. The bank does not lose money for applicants who are rejected.

We have to develop a model that maximizes a profit-cost function given the provided data.


Key Takeaways:

  • Develop a Machine Learning model that determines which loan applicants are credible, i.e. most likely to repay their loans
  • Develop Monitoring framework and feedback loop for the Credit Risk Model


Check out the below video to know more about the session.

  • Arihant Jain

    Staff Data Scientist

    General Electric

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