New Consortium of Online Lending Leaders Aims to Combat Fraud

online_lending_network

There have been several groups formed in the online lending industry but the latest one doesn’t focus on a voice in Washington but rather seeks to tackle another challenge facing online lenders: fraud. The new consortium called Online Lending Network was announced this week at Money 20/20 by ID Analytics, a firm focused on risk management.

Founding members include the leaders in online unsecured consumer lending: Lending Club, Prosper and Marlette Funding. Other lenders also part of the consortium include other online, marketplace, specialty finance and social lenders. What makes this newsworthy is that all of the major players have signed on, which is critical in combating fraud and gaining insight into a borrower’s intentions.

Al Pascual, senior vice president, research director and head of fraud & security at Javelin Strategy & Research shared just how serious fraud is in online lending:

New-account fraud more than doubled in the past year. Fraudsters are always looking for an easier way to make more money and are targeting this relatively new financing model by going after businesses that lend online.

These criminals are often taking advantage of what is often called “loan stacking”. It involves submitting multiple loan applications through various online lenders in a short period of time with no intention of paying back the loans. This type of fraud is made possible due to the lag time that data points such as inquiries for credit and outstanding credit are reported by credit bureaus. Since reporting isn’t instant, platforms like Lending Club, Marlette and Prosper are unable to determine whether the same borrower has already taken out a loan with one of their competitors. It’s important to keep in mind that loan stacking is not a new issue, but the fraud is made easier as loans are readily available online at faster speeds.

To combat this type of fraud, ID Analytics has created a near real-time repository for online loan activity. This will allow participating platforms to assess a borrower’s intentions and combat fraud not only with loan stacking but also with identity theft. ID Analytics explains the process in their press release:

Through the Online Lending Network, lenders report when a consumer requests an offer for a loan product, submits a loan application, or when a loan is funded. In return, the lender receives information on whether that consumer has either requested other loan offers or applied for loans elsewhere in the days, hours or minutes before. The near real-time nature of the response makes high-velocity fraud, like loan stacking, very difficult. It also has the potential to protect authentic consumers from overextending their credit capacity to facilitate responsible lending…The Online Lending Network will also provide access to tools to evaluate credit, including the detection of synthetic identities, and detection of potential identity theft, as online lenders are a target for fraudsters using stolen identities.

Conclusion

The key to this new consortium was getting buy-in from the major players, which ID Analytics has successfully done. This is the biggest step we’ve seen in the online lending industry in combating fraudulent activity. As technology companies, the platforms, along with innovative service providers like ID Analytics are well suited to deal with fraud in a way that hasn’t been done before. Although fraud will likely always exist on some level, this new consortium will go a long way in lowering the impact of fraud on online lending businesses.

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Willy05
Willy05
Nov. 17, 2016 12:58 pm

Fraud reduction needs to go much farther. Any loan that defaults in the first year of issuance is suggestive of fraud. The goals need to be much more ambitious. Verify borrowers and their income, Manually review obvious indicators like misspellings. Look for community ties and history. Encourage co borrowers and guarantors. Should always be looking to reduce the gap between nominal returns and actual investor returns.