Alternative Credit Decisions, the latest product from Berlin-based Fraugster, will enable buy now pay later (BNPL) and enterprise merchants to approve more customers without increasing credit risk.
The artificial intelligence (AI) payment company’s new product has set out to enrich BNPL credit scoring models and to give a more accurate picture of a buyer’s true credit risk.
The product consolidated over 100 attributes to assess credit risk, including a buyer’s positive transaction history, account history, purchase history and unpaid amounts. This insight has been facilitated by global network intelligence and real-time graph networks.
The launch of Fraugster’s product coincides with BNPLs reporting much higher bad debt impairment rates than credit cards. For every $1billion of processing volume, BNPLs write down $19.2 million of bad debt compared to $270,000 per billion for credit cards.
At the same time, good customers continue to experience service denials because BNPL providers and e-commerce merchants are unable to accurately determine their level of risk. This includes a significant proportion of returning shoppers who are treated as if they are buying something online for the first time. This is happening because credit decisions are missing important data.
“We want our customers to feel confident that they can trust the person they are approving to repay the amount they are borrowing,” said Fraugster CEO Christian Mangold whilst discussing the launch.
“The positive results we are already seeing with trial customers make me confident that we can help the e-commerce ecosystem approve more customers without increasing exposure to loan defaults.”
Currently, BNPLs and enterprise merchants broadly use credit bureau checks to increase their confidence in an approval decision, but they typically encounter a fee for each check made. Being one of the USPs of the service, Alternative Credit Decisions is able to reduce dependency on these checks, improving both the cost and accuracy of the process. The product also helps customers rationalise other third-party data vendor costs.