PointPredictive, Inc. a leading provider of machine learning fraud solutions, today announced the results of new study that determined high levels of fraud on early payment auto loan defaults. The study found scoring applications for auto loans with models that were built to detect fraud resulted in lenders finding 50% more early default than traditional credit scores.
In 2007, BasePoint Analytics™ found that between 30% and 70% of mortgage loans that defaulted within the first six months contained serious misrepresentations on the original application. These misrepresentations on borrowers income, employment, collateral or even intent to occupy had a material impact on the performance of the loan but were often considered “hidden fraud” since they were never detected in the application process.
“Our analysis and experience suggest that many auto loans that default within the first six months have fraud misrepresentation on the loan application, indicates Tim Grace, CEO at PointPredictive, “when we ran our fraud pattern recognition models on the application information provided on loans that defaulted early, the models were finding strong evidence of fraud. This is the same type of behavior we saw in mortgage prior to the mortgage meltdown”
PointPredictive Auto Fraud Models analyze each application and alert lenders when it appears that there might be misrepresentation on the application related to the income, employment, collateral, borrower or dealer. While built to detect fraud, scientist were surprised to find that it did extraordinarily well in the detection of early payment default (auto loans that default within the first 6 months). In lender tests covering 1.4 million applications and 22,500 deals, the PointPredictive Fraud Score was able to detect 14x more of the total fraud experienced by the lenders than previously used tools and processes. The early payment default (EPD) score identified 4x more first pay defaults and the Dealer Score identified 2x more suspicious dealers.
Auto lending fraud, like mortgage fraud occurs when information on an auto loan application is intentionally misrepresented either by the borrower themselves, a sophisticated fraud ring, or in some cases an unscrupulous dealer. When information is manipulated and the lender does not know about it
they may underwrite the application assuming the information is valid. Intentional fraud presents a problem to auto lenders since loans that have misrepresentation are more likely to result in – early payment default – a term lenders use to indicate when no payments are ever made on the loan.
PointPredictive Auto Fraud Manager uses pattern recognition; a technique that scientist have perfected to detect fraud based on historical data mining. The solution works by analyzing historical patterns of fraud, early payment default and risky dealer activity and then scores each application as it comes in from a dealer. Lenders are automatically alerted when an individual application has a significant number of application anomalies or fraud patterns. The lender can review the application and take action before it is approved. Over time, if a particular dealer submits many applications with similar fraud patterns, the solution will alert them to that as well so they can take the appropriate action.
As part of the study, PointPredictive has published a whitepaper on the subject titled, “ You Can’t Fight Fraud with Credit Risk Tools”. The whitepaper is available by emailing firstname.lastname@example.org.