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How Machine Learning Closes the Loop on Hidden Auto Fraud

  • Frank McKenna
  • October 12, 2017
  • 0

An unscrupulous finance manager can manipulate a loan application to disguise a fraud scheme and fool even the most diligent lender. To address this, lenders are increasingly enlisting the help of machines to find hidden patterns of fraud. The technique is new in the fight against automotive lending fraud, but it has been used for 25 years to control credit card and mortgage fraud.

Machine learning software works by analyzing thousands of factors about a loan and pinpoints when something doesn’t look right. It might be issues with the income. It could be issues with the employer. It can even detect issues with a car and the way it is priced. It doesn’t matter. If any information looks fabricated, machines can spot it.

Machine learning software works because of how the models are trained to spot fraud. Machines are trained, just like underwriters are trained, but on a mass scale. Machines are shown millions of good loan examples, and tens and thousands of fraudulent loans. Overtime they learn subtle differences so when they see the pattern again, they identify it immediately.

Machine learning closes the loop on auto fraud by automatically learning every new pattern of fraud as it presents itself.

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