Callcredit has turned to artificial intelligence to tackle fraudulent loan applications as well as potential loan defaulters by using machine learning to identify behavioural patterns.
Having initially implemented an unnamed third-party fraud-detection platform for its free Noodle service, the firm created a model internally, on Microsoft Azure Machine Learning, which it claims outperformed the third-party product.
The company is also using Azure Machine Learning to spot people who will fail to pay back loans – either intentionally or because of legitimate financial problems that occur after taking out the loan.
A trial that was run by the company using Microsoft’s technology cut the level of default in a portfolio of 60,000 credit cards by more than £1m because it was better at spotting people who would default.
Callcredit chief data officer Mark Davison said: “It helps lenders make more accurate decisions, more quickly. The loan applications a lender will turn down and the ones they will approve are easy to spot. The grey area in the middle, which are the ones they have to manually refer, are much trickier.
“As a result, the end consumer goes through a far more onerous process where an underwriter has to review the case. That’s not a great experience for the consumer or the lender. When you decide you need a credit card or a secured loan for a car, for example, you want a decision quickly. Being able to give more accurate predictions to lenders enables them to make that decision really quickly.”
It has also freed up Callcredit’s fraud detection specialists to focus on more sophisticated patterns of criminal activity. If lenders are able to be more accurate when predicting who will default on loans, “that has a beneficial effect for the economy, too”, Davison claimed.
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