[793 words]<\/p>\n
The age-old issue faced by the lending industry is information asymmetry: will a borrower have the ability and the willingness to repay a loan? Traditional credit scoring methods are imperfect: good potential borrowers are shut out of the credit market because they do not \u201ctick the box\u201d, and approved borrowers continue to default. Today, fintechs such as LendingClub are seeking new ways to reduce the information asymmetries that cause these frictions in the market, and ultimately lead to inefficient capital allocation and higher borrowing costs.<\/p>\n
LendingClub is the largest online lending marketplace in the US, having issued $41.6bn in loans since inception in 2007[1]<\/a>. The platform is growing quickly, with loan origination up 18% year-over-year in the quarter ending September 2018[2]<\/a>. LendingClub aims to increase efficiency and affordability in the lending market, by using large datasets and machine-learning techniques that go beyond traditional credit scoring to reach new borrowers and improve risk detection. LendingClub\u2019s strategy is to pass these improvements onto consumers in the form of lower borrowing rates.<\/p>\n Built into LendingClub\u2019s model is a dataset based on over 10 years of underwriting and more than 2.5 million customers, with information such as transactional data, behavioral data and employment information. Techniques used by the underlying model include analyzing trended data, looking at a borrower\u2019s changing credit behavior over time, and taking a granular view at various credit balances across a borrower\u2019s portfolio. With a large number of datapoints per borrower and an ever-increasing borrower group, the company has been able to refine its model over time.[3]<\/a><\/p>\n Research undertaken by the Federal Reserve Bank of Philadelphia found that LendingClub has been able to undercut interest rate spreads offered by traditional lenders as a result of its methodologies, and that previously underserved borrowers are being given access to credit. Furthermore, the research founds that LendingClub\u2019s credit scoring algorithm has shown increasing divergence from traditional credit scoring metrics over time. Despite the low correlation with traditional credit scores, LendingClub ratings proved a good predictor for loan delinquency, indicating that the learning model is working.[4]<\/strong><\/a><\/em><\/p>\n Despite these positive signals, certain parts of LendingClub\u2019s process are not yet automated. One key area is honesty: the company\u2019s algorithms cannot assess whether potential borrowers are being truthful in their loan applications. LendingClub therefore verifies information manually, for example reaching out in person to HR departments in order to verify a borrower\u2019s employment status. According to a 2010 性视界 Case Study, 50% of pre-approved borrowers requiring verification did not make it past the manual review phase at LendingClub, because they did not respond to the verification request or provided insufficient information[5]<\/a>.<\/p>\n A potential solution to this remaining manual process lies with Ping An, the Chinese financial services conglomerate, which has created a system that detects whether potential borrowers are being truthful in their credit applications by analyzing facial movements made during an interview over a smartphone, using machine learning techniques. Ping An claims the technology has reduced credit losses by 60%[6]<\/a>. Such a system could potentially remove the need for verification of information, providing a significant further reduction in informational asymmetries and corporate expenses for LendingClub. The question arises: would US consumers permit such a method to be used from an ethical standpoint?<\/p>\n Usage of credit scoring algorithms and machine learning could also have huge potential in uses outside of the consumer lending space. 93% of LendingClub\u2019s loans as of September 2018 were personal loans, with a tiny percentage being small business loans[7]<\/a>. In the medium term, if success in the business lending space can be proven, there is potential for machine-learning techniques to be scaled up for use in the wider corporate space. There are inherent inefficiencies in banks\u2019 largely manual corporate credit underwriting and loan origination processes, feeding into substantial underwriting fees. Are machine learning technologies transferable from the consumer to the corporate lending space? This remains to be proven on a large scale, but LendingClub could benefit hugely if it can leverage the opportunity.<\/p>\n [1]<\/a> Lending Club, \u201cLending Club Statistics,\u201d https:\/\/www.lendingclub.com\/info\/statistics.action, accessed\u00a0\u00a0 November 2018.<\/p>\n [2]<\/a> Lending Club, \u201cPress Release: LendingClub Reports Third Quarter 2018 Results\u201d, https:\/\/ir.lendingclub.com\/file\/Index?KeyFile=395660550, accessed November 2018.<\/p>\n [3]<\/a> Lending Club, \u201cBlog: The Power of Data and the Next Generation Credit Model\u201d https:\/\/blog.lendingclub.com\/lendingclubs-next-generation-credit-model, accessed November 2018.<\/p>\n [4]<\/a> Julapa Jagtiani and Catharine Lemieux, \u201cThe Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform\u201d, Federal Reserve Bank of Philadelphia Working Paper WP 18-15, April 2018. https:\/\/www.philadelphiafed.org\/-\/media\/research-and-data\/publications\/working-papers\/2018\/wp18-15.pdf, Accessed November 2018.<\/p>\n [5]<\/a> Tufano P, Jackson H, Ryan A. \u201cLending Club\u201d. HBS No. 9-210-052. Boston: 性视界 Business School Publishing, 2010.<\/p>\n