How to Identify and Combat the Most Prevalent Types of Fintech Lending Fraud
With the growth of fintech lending comes an increased risk of fintech fraud. Fraudsters nowadays are sophisticated, leveraging intricate and well-thought strategies to scam lenders and borrowers, resulting in financial losses and damaged reputations. This blog will take you through the most common types of fintech lending fraud and provide tips on how lenders can identify and prevent falling prey to it.
This is perhaps the most apparent type of fraud. With identity theft, fraudsters steal personal information, such as a social security number, driver’s license, or passport, and use it to apply for loans. They might also create fake identities or use stolen ones to apply for several loans at once.
To prevent identity theft, lenders should verify the identity of each borrower by checking their personal information against official records and conducting background checks.
Synthetic identity fraud
It sounds like what it is. Synthetic identity is when fraudsters create artificial or false identities using a combination of real and fake information. They may use a legitimate social security number but create a phony name and address. Once the synthetic identity is established, the fraudster can use it to apply for loans or credit cards.
Synthetic identity fraud can be challenging to detect, but lenders can minimize risk by verifying the identity of each borrower and monitoring their behavior for signs of fraud.
With loan stacking, borrowers apply for multiple loans from different lenders simultaneously using the same or a different identity. This type of fraud can result in financial losses for lenders and borrowers because the borrower might be unable to repay all the loans they’ve taken out.
Lenders can prevent loan stacking by checking each borrower’s credit report and limiting the number of loans they can apply for.
Income Fraud is one of the most common types of fraud that fintech companies have to grapple with. Income fraud occurs when borrowers inflate their income to qualify for a loan, an account, or a credit card.
Employment fraud is one of the fastest-growing types of fraud for Fintech companies. Employment fraud involves fabricating the employment on the application using a completely fake employer, or using a real employer and faking their employment. Fake employers are used to provide falsified verification of employment to help consumers qualify for loans or accounts with financial institutions.
There are over 9,500 fake employers in circulation that Point Predictive tracks.
Forgery of paystubs, bank statements, drivers licenses, utility bills and other supporting documentation is a significant problem for Fintechs.
Point Predictive’s analysis with companies indicate that as many as 1 in 10 documents that Fintech’s receive are forged or altered in an attempt to support fake employment, identity or income.
Point Predictive’s role
Fintech lending fraud is a growing problem that can result in significant financial losses for lenders and borrowers. By understanding the different types of fraud and taking steps to prevent them, lenders can protect themselves and their customers from financial harm. Point Predictive makes the job for lenders easier by offering AI-powered solutions that verify employment and proof of income with greater accuracy and speed. To learn more, schedule a demo with our expert team.