Credit scoring is a statistical analysis performed by lenders and financial institutions to assess a person’s creditworthiness for mortgages, credit cards, and private loans. Credit scoring is used by lenders to decide whether to extend or deny credit.
Traditionally, credit bureaus rated a person’s credit score with a number between 300 and 850. As new types of lenders and insurers emerge, however, the traditional credit score becomes just one parameter among many that determine a person’s creditworthiness.
Good models let you rapidly explore data to build stand-alone predictive credit scoring models within business rules flows.
According to FICO, machine learning allows you to build an algorithm to map credit risk with fewer resource hours. For example, a single analyst took 40 hours to build an ML score showing slightly improved risk prediction over the FICO® Score 9 models (without ML) that took five analysts one month to build.
The following five categories influence credit scores:
Essential external data for a good B2C Credit Risk Model are the credit score of a spouse and the state of the economy. In a recession, for example, the likelihood that it will be more difficult to repay loans increases.
Other useful information for a B2C Credit Risk model is information from an application for a financial product or account as well as internal data on current or past customers.
Artificial intelligence is still new in the field of detecting credit risk, so the best models are still being built up.
Further, B2C credit scoring does not asses a borrower’s chance of default nor does does it take current fluctuating economic conditions into account. For example, a borrower who has an excellent credit score during an economic upturn may still default if the economy enters a recession. AI cannot predict that default unless it has already seen signs of the borrower struggling to pay bills on time.
Machine learning could allow banks and other lenders to increase revenue by approving more credit invisible applicants and more applicants whose credit scores paint an incomplete portrait of their creditworthiness. ZestFinance, for example, claims to have helped Prestige Financial Services increase loan approvals by 14% with an ML-based credit model.
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