To prevent fraud, Byrider chooses PointPredictive for machine learning AI
PointPredictive says machine learning in its Auto Fraud Manager with Auto Fraud Alert Reporting mines historical data from applications across the industry “to pinpoint where fraud is happening.”
PointPredictive launched Auto Fraud Manager with Auto Fraud Alert Reporting to help address what it says is a $6 billion-dollar annual problem of misrepresentation and fraud that negatively affects the auto lending industry.
On Tuesday, PointPredictive Inc. said buy-here pay-here dealership network Byrider has selected the company’s risk scoring products to help better segment high- and low-risk applications and dealers. PointPredictive said Byrider is doing that to improve profitability, expand loan availability and improve the lending experience for consumers and dealers.
PointPredictive said Byrider will use the products to identify misrepresentation and prevent default on high-risk applications.
It will also streamline the approval process of low-risk applications, according to PointPredictive, which said that will improve and expedite the consumer and dealer loan funding experience.
That will ultimately expand Byrider’s loan portfolio profitably, PointPredictive said.
Byrider performed testing of the products evaluated retrospective results before selecting PointPredictive’s machine learning AI scoring.
“In our retrospective test with PointPredictive, we saw a significant lift in identifying defaults tied to misrepresentation and fraud,” Byrider chief risk officer Gary Harmon said in a news release.
PointPredictive said Auto Fraud Manager with Auto Fraud Alert Reporting’s machine learning AI system has evaluated more than 60 million applications. The company said the system is continuously learning new patterns as they emerge.
“PointPredictive is excited to partner with Byrider to help them achieve better relationships with their borrowers and their dealer network,” said PointPredictive chief executive officer Tim Grace.
Grace added, “Our solutions have proven to help lenders reduce their risk of early defaulted loans and, in the process, help them streamline loans for reduced stipulations and friction in the lending process. By better targeting risk, the end beneficiaries are their dealers and borrowers who can see a reduction in the time it takes to fund loans.”