Imagine being approved for a loan based on which college you attended, your online shopping patterns, or even the punctuation quality of your instant messages. While seemingly far-fetched, credit approval enabled by artificial intelligence-machine learning (AI-ML) is all but upon us, with digital-first lenders like BankMobile at the forefront. At a time when AI-ML is hotly touted but far from ubiquitous within credit lending, the question remains whether lenders can satisfy customers, shareholders and regulators to achieve sustained competitive advantage. <\/p>\n
With consumer debt in the US rising 5.1% to a record $3.83T in 2017 (and the mortgage crisis still ringing in the ears of creditors), finding ways to efficiently make sound lending decisions is key to success [1]. However, the traditional lending process leaves much to be desired. Lenders rely heavily on credit bureau data which systemically disadvantages so-called \u2018thin file\u2019 borrowers, such as young people, new immigrants and the self-employed. Other information is often left incomplete, incorrect, or even intentionally falsified by the borrower. Lending decisions require significant human judgement and can take upwards of weeks to process. And yet, default rates still exceed 9% in key categories such as student loans [2]. What\u2019s more, a recent study showed that while 83% of borrowers have never defaulted on a loan, only 45% receive access to prime lending rates [3].<\/p>\n
For BankMobile, an online-only retail bank whose median customer age is 28, the ability to serve a target market of borrowers with limited credit history has clear appeal [4]. Furthermore, with their parent company, Customers Bank, reporting a loan loss allowance of $38M on a net income of $79M in 2017, selecting the right candidates to lend to could drive significant impact on the bottom line [5]. <\/p>\n
Their solution? Automate the lending process using machine-learning algorithms and non-traditional, personal customer data.<\/p>\n
In 2017, BankMobile became the first bank to partner with AI-ML peer lending service, Upstart. The service leverages proprietary algorithms to determine creditworthiness and pricing based not only on an applicants\u2019 credit score, but other factors including their highest level of education, school name, area of study and even how they interact with the application [6]. The methodology, created in collaboration with the Consumer Financial Protection Bureau (CFPB) allows BankMobile to test changes to the algorithms, and analyze their ability to maintain fair lending standards as defined by the regulator [3].<\/p>\n
So far, it looks to be working.<\/p>\n
Presently, customers can receive personal loans in as little 24 hours, with more than half of all applications assessed fully automatically. Furthermore, a recent study showed that, for a given loan approval rate, the Upstart service experienced 75% fewer defaults versus a peer group of large US banks [3]. <\/p>\n
While BankMobile\u2019s medium term lending strategy remains largely undisclosed, plans do exist to expand the model beyond personal loans to credit cards, student loan refinancing, home equity and car loans in the coming years [6]. In parallel, partner Upstart continues to refine its underlying algorithms with the ultimate goal of achieving full loan process automation for 80-90% of deals [7]. <\/p>\n
While their FinTech partnership has enabled BankMobile to swiftly storm the market, addressing key risks in the near term is critical. Firstly, the bank should look to best practices to control possible algorithmic biases, namely by working to closely understand the limitations of the algorithms and assisting Upstart data scientists to shape data samples accordingly [8]. BankMobile employees should be trained in the lending process at a high-level and capable of facilitating warm handovers to the servicer as necessary. Further, at minimum, the bank must reduce its reliance on Upstart, implementing systems to back-up crediting arrangements in the event of partner issues [9]. The bank should seek to remove any variables with ‘gameable’ influence on the algorithms predictions, and closely monitor the system\u2019s performance, which is yet to be tested through a full economic cycle.<\/p>\n
Longer term, BankMobile may consider bringing or building its AM-ML lending capability in-house to enable rapid prototyping and to own the customer relationship end-to-end [10]. Such a move would likely command the creation of cross-functional teams anchored by top developer talent. Additionally, the bank may seek to build a more nuanced view of creditworthiness by incorporating other alternative data sources such as customer bill payment history or online shopping patterns. Lastly, given the sensitive nature of customer information and the fact that 1 in 3 focused cyber-attacks results in a security breach, investing in cyber security governance, cyber resilience and cyber response readiness is imperative [11]. <\/p>\n
Beyond these operational challenges, key questions still loom. For consumers, how palatable is a \u2018Netflix-esque\u2019 loan pricing and approval process, and what might lenders need to provide to earn their trust? For BankMobile specifically, should they insource their strategic AI-ML lending capability, and if so, when? <\/p>\n
(Word count: 782)<\/p>\n
Sources:
\n[1] Federal Reserve. \u201cConsumer credit increased at a seasonally adjusted annual rate of 5-1\/4 percent during the third quarter.”, press release, November 7, 2018. Federal Reserve website, https:\/\/www.federalreserve.gov\/releases\/g19\/current\/, accessed November 2018.<\/p>\n
[2] Vasily Souzdenkov, \u201cStates That Don\u2019t Pay Their Bills on Time\u201d, Credit Sesame Blog, May 31, 2018, https:\/\/www.creditsesame.com\/blog\/debt\/states-that-dont-pay-bill-on-time\/, accessed November 2018.<\/p>\n
[3] Upstart. \u201cResults to date\u201d https:\/\/www.upstart.com\/about#result-to-date-1b, accessed November 2018<\/p>\n
[4] Bank on It, \u201cEpisode 128 (2 of 4) Recorded live from Lendit USA 2018\u201d, April 24, 2018, podcast, http:\/\/bankonitpodcast.com\/episode-128-2-of-4-recorded-live-from-lendit-usa-2018, accessed November 2018.<\/p>\n
[5] Global Newswire. \u201cCustomers Bancorp Reports First Quarter 2018 Net Income of $20.5 Million; Diluted EPS of $0.64\u201d, press release, April 30, 2018. Global Newswire website, https:\/\/globenewswire.com\/news-release\/2018\/04\/30\/1489797\/0\/en\/Customers-Bancorp-Reports-First-Quarter-2018-Net-Income-of-20-5-Million-Diluted-EPS-of-0-64.html, accessed November 2018.<\/p>\n
[6] American Banker. \u201cBankMobile Deploys AI Alternative Data to Lend to FICO Poor Students\u201d, https:\/\/www.americanbanker.com\/news\/bankmobile-deploys-ai-alternative-data-to-lend-to-fico-poor-students, accessed November 2018.<\/p>\n
[7] Benzinga. \u201cUpstart, the brainchild of Google execs is using Fintech to make smarter loans\u201d, https:\/\/www.benzinga.com\/fintech\/17\/09\/10040990\/upstart-the-brainchild-of-google-execs-is-using-fintech-to-make-smarter-loans, accessed November 2018.<\/p>\n
[8] McKinsey. \u201cControlling machine learning algorithms and their biases\u201d, https:\/\/www.mckinsey.com\/business-functions\/risk\/our-insights\/controlling-machine-learning-algorithms-and-their-biases, accessed November 2018.<\/p>\n
[9] U.S. Department of the Treasury, \u201cOpportunities and Challenges in Online Marketplace Lending\u201d, Treasury Connect Blog, May 10, 2016, https:\/\/www.treasury.gov\/connect\/blog\/Documents\/Opportunities_and_Challenges_in_Online_Marketplace_Lending_white_paper.pdf, accessed November 2018.<\/p>\n
[10] The Boston Consulting Group. \u201cDigital Transformation\u201d, https:\/\/www.bcg.com\/en-us\/capabilities\/technology-digital\/digital.aspx, accessed November 2018.<\/p>\n
[11] Accenture. \u201cBuilding Confidence Facing the Cybersecurity Conundrum\u201d, https:\/\/www.accenture.com\/t20170406T052041Z__w__\/sa-en\/_acnmedia\/PDF-35\/Accenture-Building-Confidence-Facing-Cybersecurity-Conundrum-Transcript.pdf, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"
BankMobile looks to machine learning and alternative data sources to boost the bottom line.
\nCan consumers and regulators be convinced? <\/p>\n","protected":false},"author":11412,"featured_media":29228,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,298,4289,259,340,346,2684],"class_list":["post-29210","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artifical-intelligence","category-big-data","category-credit","category-fintech","category-lending","category-machine-learning","category-start-up-uncategorized","hck-taxonomy-organization-bankmobile","hck-taxonomy-industry-banking","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n