Gillmore Centre Network

Although online lending enjoyed explosive growth in the past decade, its market size remains small compared to other financial assets. The risk of losing money, stringent government regulations, and low awareness of the benefits have hampered the realization of the full potential of the online lending market. Because online loans are an emerging asset class, investors may not be aware of the investment performance of online loans compared to other assets, and it remains an open question whether online loans offer sufficiently attractive returns to warrant inclusion in an asset allocation decision. To attract lenders, platforms must provide an appealing investment opportunity which entails construction of portfolios of loans that investors find attractive. We propose general characteristics-based portfolio policies (GCPP), a novel framework to overcome the difficulties associated with portfolio construction of loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive dataset spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. We then address the question of whether online loans can earn competitive rates of return compared to traditional investment vehicles through six market indices covering stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes. Furthermore, the IRRs of the loan portfolios have small correlations with the benchmark index IRRs, pointing toward significant diversification benefits. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.

Our work naturally leads to the question of how interest rates of unsecured personal loans be set. Much of the literature advocates risk-based pricing: A loan with a high degree of risk should be assigned a high interest rate, and a loan with low risk should be assigned a low interest rate. Risk-based pricing has also been widely applied to the loan market in practice. However, the existing literature does not consider the economic rationale that interest rates should provide the same risk-return tradeoff, which results in the mispricing that risky borrowers pay a much larger premium compared to less risky borrowers. Employing GCPP, we find that the interest rates on the platform are not always set appropriately. Our results show that the price distortion leads to a significant gap in the probability of being funded between subprime (660<FICO<700) and prime (FICO>700) borrowers. We also provide evidence consistent with a bias against African Americans in setting interest rates.

We provide an alternative set of interest rates aimed at alleviating mismatches in risk and return. These interest rates lead to greater credit allocation to underserved subprime borrowers, narrowing the gap between subprime and prime borrowers. The new interest rates assigned by our method also reduce biases against minority groups, such that two borrowers with similar characteristics but different races are assigned similar interest rates. Finally, when interest rates are appropriately set, the platform benefits. LendingClub derives its revenue from the number of users. Maximizing revenue is equivalent to maximizing the volume of loans. Under the new interest rates, the loan volume is expected to increase by 15.5% compared to the volume under the original interest rates. Hence, online lending platforms can do well by doing good – maximizing revenue while providing more attractive loan pricing for both borrowers and lenders.