Introducing MLOps

Chapter 9. MLOps in Practice: Consumer Credit Risk Management

In the final chapters of this book, we explore three examples of how MLOps might look in practice. We explicitly chose these three examples because they represent fundamentally different use cases for machine learning and illustrate how MLOps methodology might differ to suit the needs of the business and its ML model life cycle practices.

Background: The Business Use Case

When a consumer asks for a loan, the credit institution has to make a decision on whether or not to grant it. Depending on the case, the amount of automation in the process may vary. However, it is very likely that the decision will be informed by scores that estimate the probability that the loan will or will not be repaid as expected.

Scores are routinely used at different stages of the process:

• At the prescreen stage, a score computed with a small number of features allows the institution to quickly discard some applications.

• At the underwriting stage, a score computed with all the required information gives a more precise basis for the decision.

• After the underwriting stage, scores can be used to assess the risk associated with loans in the portfolio.

Analytics methods have been used for decades to compute these probabilities. For example, the FICO score has been used since 1995 in the United States. Given the direct impact they have on the institutions’ revenues and on customers’ lives, these predictive models have always been under great ...