Surprising Fraud Risk Insights from the Online Lending Market

April 12, 2016
By Garient Evans

Consumer demand is driving tremendous growth and change in the online lending industry. Online lenders are also a popular target for fraudsters of all kinds, with two to four times the number of fraud attacks as other account types, according to an analysis of fraud rate by industry we conducted at ID Analytics with data from our ID Network.1 The surprise is that these lenders are able to prevent the vast majority of fraud, a requirement because some marketplace lenders offer investors a 100 percent money-back guarantee as part of their Terms & Conditions for identity fraud cases.2  How can marketplace lenders achieve such low perpetrated fraud rates in the face of such high attack rates? How are these lenders responding to consumer demand for more convenient borrowing experiences all while mitigating risk?

To better understand the threats facing online lenders (composed of both marketplace and direct lending enterprises), ID Analytics recently reviewed our ID Network data and found that the online lending industry had more than double the pre-booked fraud rate of the bankcard and retail credit card industries over a nine month period, and three to four times the fraud attempts of wireless carriers. In spite of the volume of attempts, successful fraud in this space remains negligible for most lenders.

The research found another surprise as well. Specifically, first party fraud, where an individual opens an account in their own name with no intention of repayment, and synthetic identity fraud, in which the fraudster uses a combination of authentic and fictitious information, are growing vectors of attack and should be a major concern for risk managers. Both of these approaches can be easily missed by the traditional fraud detection methods used at many institutions.

Fraud Rate by Industry_041216

This data clearly shows that traditional methods for mitigating fraud are no longer sufficient. For example, many lenders rely on credit bureau solutions that do not address the “credit invisible population” (consumers lacking a credit history). Lack of data limits the effectiveness of these fraud risk scores. This population is a prime demographic for many online lenders and requires accurate treatment. Manual review processes have proven very effective to mitigate fraud in this space, but they are expensive, introduce discouraging customer friction, and do not scale well as businesses grow.

We are seeing innovative lenders moving beyond the legacy approach to reliably automate their underwriting platforms while keeping fraud at bay and staying within regulatory constraints. How are they doing it? By using alternative data and predictive solutions to evaluate and automatically clear more applicants while remaining compliant. Effective alternative data incorporates insights from wireless, banking, peer-to-peer lending, checking and savings accounts. Combined with information like address change histories, it delivers a rich view into a consumer’s risk.

First party fraud and synthetic identity fraud can be identified in both the automated and manual processes, though an automated approach is substantially less expensive. In other industries, where minimal fraud might be accepted as a cost of business, these newer fraud vectors represent a greater threat because they might be mistaken for credit losses.  Online lenders who have not taken a fresh look at the new types of fraud and the role of alternative data and predictive solutions will fall behind their competitors who are scaling more quickly and efficiently.

ID Analytics has helped the online industry scale as a premier provider of risk management services and our real-time, cross-industry identity network currently screens more than 50 percent of the applications in this fast-growing sector. To learn more about how ID Analytics can help your company assess applicants while mitigating fraud risk visit:


Garient Evans is the Vice President of Client Services at ID Analytics


1 Based on research into published loan volumes for various online lenders and the volume ID Analytics see from them