By Aidan Roszak · On March 30, 2017

The Emergence of Online Marketplace Lenders

Advances in technology and an increased access to data are changing the way consumers and small businesses secure financing (Center, 2016). Online marketplace lending has emerged as a method of delivering faster credit for small businesses as well as consumers. These lenders function by offering a loan through an online platform that will continue to collect principal and interest payments from borrowers which are then sent to investors. Companies that offer these platforms charge fees for facilitating the process. Additionally, online marketplace lending platforms advertise both new loans and loans that can be used to refinance existing debt (“Understanding Online Marketplace,” 2015).

This method of lending has seen significant growth since its inception. According to data from the U.S. Treasury Department, from YE 2014 to YE 2015, venture capitalists injected $2.7 billion into online lending companies. By the end of 2015, total securitization volume reached more than $7.0 billion.As online lending companies enter and force established incumbents to innovate, consumers have been rewarded with lower interest rates and a better customer experience, while investors gained access to an asset class that had previously been off limits.However, the rise of online lending has generated warranted concern about renewed fair lending risks.

Traditional banks discern who to loan money to and in what amount by reverting to the risk profile that they have adopted. While traditional banks were unwilling to appeal to the lending industry’s down-market due to the risk profiles required by investors, online lending companies saw an opportunity to fulfill unmet demand. Part of the success seen by online lending companies can be attributed to their willingness to call upon unique ways of analyzing creditworthiness to attract potential borrowers who would have otherwise been left unfunded by Canada’s biggest banks.

Mogo and the Legal Response to Lenders

Online lenders regularly gather information from social media profiles about individuals, including their web surfing activity, online “friends,” and other data points. As stated in the Skadden Review on Emerging Fair Lending issues, they can apply that information to a myriad of situations, from the identification of individuals to receive funding, to the pricing and underwriting of loan applications. Everything and anything that’s available can be used to form a picture of who you are and whether you are a reliable borrower.

One player who has helped lead the development of Canadian online marketplace lending is Mogo. Mogo relies on a unique combination of data analysis to analyze loan applications and uses roughly 1,000 data points to gather a detailed snapshot of the borrower. This process has been adopted to minimize defaults and identify lending opportunities that add value to Mogo’s business. The application software delves into information received from credit bureaus, what phone the user is connected to, and IP addresses. Search history, Facebook friends, and how you interact with your social network are also all used in allowing Mogo to identify lending opportunities.

There is no regulation that specifically governs fair lending as it relates to the information that can be retrieved from social media. Rather, as often occurs with innovative technologies, laws written for a different time must be applied to the new technology. The appropriate role of financial regulators remains unclear in policing fintech companies that provide small amounts of business capital and personal lending. These new forms of lending present various difficulties, as regulators and governing bureaus are forced to apply current law in contexts that their authors never could have imagined (Raman, Barloon & Welch, 2012).

The Consumer Financial Protection Bureau (CFPB) is an organization that has made an effort to adopt an appropriate position on the matter. The organization has been granted significant power — as outlined by Dodd-Frank — to prohibit “abusive or unfair lending practices that promote disparities among consumers of equal credit worthiness but of different race, ethnicity, gender, or age” for lending. According to a recent statement by CFPB, online marketplace lenders could break the law if their software systems have more predictive algorithms than banks do in assessing a borrower’s creditworthiness. The bureau believes that relying too much on the information from the new algorithms opens the door to potential lending risks, and due to the power that has been granted to the bureau, infant online lenders are forced to operate within the framework that they are given. Online lenders such as Mogo have responded by highlighting the positive effects of these algorithms. They claim that their ability to fund small businesses and individuals has been driven by the algorithms that identify lending opportunities that align with their risk profile but would have been rejected by banks. Despite the claims made by young firms desperately seeking to gain a foothold in concentrated industries such as Canadian banking, the methods used by online lenders to identify borrowers has led to an increase in fair lending risks.

Issues Driven by Innovative Lending

While new forms of data and modeling have certainly been beneficial, they also pose new risks. Limited only by the imagination and creativity of their underwriters and third-party vendors, online lenders can use social media and other online data points as they see fit. Without any oversight, there is a very real risk that online lenders can make discriminatory lending decisions.

By using data aggregators to develop products that classify groups of individuals based on social media sources, online lenders are dangerously towing the line between market segmentation and discrimination. On the surface, it may appear as though the powerful scope of social media information ends at its ability to lend itself to advertising. A deeper look, however, reveals that digital footprints — including social media — actually provide information that can be translated into credit report metrics and used in credit underwriting. By capitalizing on this technique of data analytics, online lenders have developed a significant competitive advantage relative to traditional banks who are stuck relying on outdated and less holistic metrics to determine a customer’s creditworthiness. Despite their innovative platforms, critics worry that online lenders who rely on the information a consumer generates digitally are entering murky waters regarding discriminatory lending. For example, as found by Raman, Barloon & Welch (2012), a lender may scrutinize an applicant’s Facebook friend list to assess the applicant’s own creditworthiness or discriminate between applicants who possess a “.gov” or “.edu” e-mail address from those who do not. A lender may even use their predictive algorithms to identify the applicant’s neighbourhood of residence — which on average is skewed towards a specific race — in determining creditworthiness. While many online lenders have been opaque regarding the extent to which they rely on these strategies, most admit that they represent at least some portion of the data used in their loans and the risk that they can indirectly use place of work, Facebook friends, liked pages and race to determine whom to lend to generates warranted concern. Analyzing and relying upon social media information for certain applicants presents a disparate treatment risk, since any inconsistent treatment of applicants — as outlined by CFPB — is evidence of discrimination. Unfortunately, the likelihood of said discriminatory practices are high. As found by Rosa Golijan of NBC News, 1.6 million users “liked” a page pertaining to a racial or ethnic affiliation and 7.7 million Facebook users “liked” a Facebook page pertaining to a religious affiliation in the last year. If the underwriter were to consider this information into their decision in a way prohibited by law, the use of social media metrics could present significantly elevated fair lending risk. As consumers become even more reliant on social media, and as the prevalence of the Internet of Things continues to grow, the risks associated with online lending will intensify as people inadvertently provide information to lenders through their regular activities.

The world of social media offers a wealth of information that can be leveraged to enhance a lender’s competitiveness. Using the information that applicants freely choose to display to their network can, on aggregate, allow lenders to gain a more holistic understanding of an applicant’s creditworthiness. However, the algorithms created by online marketplace lenders can capture discriminatory correlations that lead to fair lending violations. Online lenders have correctly challenged traditional banks by providing credit to those who might not have otherwise had access. However, to develop a structure that enables access to transparent, affordable and fair credit products, it is imperative that regulation is introduced to mitigate the risk of discrimination.