Online lenders experience about twice as much fraud as banks do. For fintechs, 6% of loans are made to fraudulent business borrowers. Online lenders are targets for fraud by borrowers who claim to be legitimate businesses. Compared to banks, which generally make loans to known customers, the online application process is remote and decisions are made quickly. Several online lenders don’t do thorough background checks, and rely on basic information such as business and owner name, address, business IP address, and business creation date to make lending decisions.
Fintechs need more relevant, alternative data to better assess if a business is real. By enriching internal data with external data, lenders can generate more accurate fraud scoring models which can dynamically identify fraud by:
By leveraging alternative data for lending, fintechs can improve their ability to immediately pinpoint loan applicants faking their identities by flagging non-existent or fictitious businesses. A more nuanced predictive model can take into account online evidence of past fraud, including any alarm-bell-raising Yelp reviews or complaints about the business on social media. It can incorporate information from multiple regions, sources, and past vendor relationships to assess the validity of the applicant’s identity. With more accurate fraud detection models, one of our customers was able to boost their fraud detection rate by 92% on applications before they progressed from the initial form submission. This cut their lending costs and charge-offs by 15%.