{"id":5284,"date":"2017-04-05T22:15:08","date_gmt":"2017-04-06T02:15:08","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/is-fico-outdated-zestfinance-an-alternative-to-the-fico-credit-score\/"},"modified":"2017-04-05T22:15:08","modified_gmt":"2017-04-06T02:15:08","slug":"is-fico-outdated-zestfinance-an-alternative-to-the-fico-credit-score","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/is-fico-outdated-zestfinance-an-alternative-to-the-fico-credit-score\/","title":{"rendered":"Is FICO outdated?\u00a0 ZestFinance: An Alternative to the FICO Credit Score"},"content":{"rendered":"

In 2010, roughly 8.3% of US consumers, or 19 million people, were considered \u2018unscorable\u2019 by FICO, a credit rating service whose models serve as a key underpinning of the US system for assessing credit worthiness.[1]<\/a> \u00a0Included in this contingent of unscorable consumers are those lacking a credit history, read: millennials, who represent an untapped market opportunity for lenders.\u00a0 \u00a0Also excluded from FICO are the underbanked, those who lack bank accounts and primarily transact in cash.\u00a0 Not only are the underbanked denied access to loans based on typical FICO filters, but they must resort to predatory alternatives, such as payday loans, whose prohibitively high interest rates trap them in a vicious cycle of deep indebtedness that is difficult to dig out of. Has the FICO score become an obsolete filter?\u00a0 And can a lender step in to provide loans to these underserved segments where traditional banks have neglected to do so \u2013 and profitably?<\/p>\n

 <\/p>\n

ZestFinance believes so, and has leveraged machine learning to develop a credit scoring engine for borrowers who lack the available credit history necessary for a suitable FICO score, if they have one at all.\u00a0 By assessing whether an individual should receive a loan and directing the applicant to a bank partner, ZestFinance addresses a market failure where underserved segments couldn\u2019t access needed loans at interest rates they could afford \u2013 and that banks could afford to service.<\/p>\n

 <\/p>\n

ZestFinance offers two services to address this need.\u00a0 First, it licenses its credit-scoring services to subprime lenders with loans averaging around $600 with an APR of 390% — compare this to an average 521% for payday loans. [2]<\/a> \u00a0Second, the company has begun originating its own loans, called Basix, in the near prime space for consumers with FICO scores below 680.\u00a0 These loans aren\u2019t cheap either, with a fixed annual interest rate of 26 to 36% over 3 years for loan sizes ranging from $3,000 to $5,000. [3]<\/a><\/p>\n

 <\/p>\n

The company achieves this value creation through core assets of 1) an experienced data science team with niche expertise in feature engineering, specifically for credit scoring and 2) a rich dataset of alternative credit assessment variables and loan performance outcomes.\u00a0 The latest algorithmic techniques are applied to a dataset of some 70,000 variables derived in part from 10 alternative credit bureaus that payday lenders report to.[4]<\/a>\u00a0 Interestingly, a loan applicant\u2019s use of all capital letters on a loan application has been found to correlate with higher risk.[5]<\/a> ZestFinance\u2019s accumulated dataset of the loans it has underwritten and associated loan performance can then be used to continuously hone and retrain its models.<\/p>\n

 <\/p>\n

Just how good are its models? \u00a0Their subprime loans have achieved a 15% default rate, 50% the average figure for typical payday lenders.[6]<\/a> \u00a0The company\u2019s success has attracted the attention of Baidu and JD.com, which have enlisted its talents to develop a credit score for Chinese consumers based on alternate variables such as search history.[7]<\/a><\/p>\n

 <\/p>\n

ZestFinance captures value through license deals with lenders and loan margin on its near prime loans, but one could see how these revenue streams could erode as the space becomes increasingly crowded and as policy further develops to protect subprime customers from interest rates beyond a certain threshold.\u00a0 Additionally, we have yet to really stress test these loans.\u00a0 It will be interesting to see how loan performance shakes out in the event of a downturn, especially in the case of their Basix loans, which have yet to realize a full 3-year term.<\/p>\n

 <\/p>\n

So what\u2019s next?<\/em><\/p>\n

\u00a0<\/em><\/p>\n

ZestFinance competes in a crowded space, and key to its long-term defensibility will be its development of a truly proprietary dataset and ancillary services to support its data feedback loop.\u00a0 Joined by Prosper, Elevate Credit, Avant, and LendUp, just to name a few, ZestFinance\u2019s claim to sophisticated human judgment in model building and valuable data are core competencies cited by all competitors.\u00a0 Its access to China\u2019s wider diversity of model inputs positions it to develop a more unique dataset in the short term, but this could erode in the future.<\/p>\n

 <\/p>\n

Likely aware of this, ZestFinance has expanded beyond loan underwriting, and is now offering a new service called ZAML, which is a platform lenders can use to build their own credit-scoring models.[8]<\/a> \u00a0Clients can input their own data sources, train models, and understand model outputs in economic terms through ZAML.[9]<\/a><\/p>\n

 <\/p>\n

ZestFinance\u2019s value creation story now has changed to involve value sharing across various players in the ecosystem.\u00a0 Best practices with data sources and modeling techniques can be potentially leveraged across ZAML\u2019s entire client base to improve credit scoring practices \u2013 and with significantly less investment from each individual firm.\u00a0 ZAML could also be used to ensure model compliance with federal regulations, such as the CFPB\u2019s mandate that lenders may not discriminate against certain demographics in their loan approval procedures. [10]<\/a> \u00a0\u00a0Value capture now takes the form of recurring SaaS revenue for ZestFinance.\u00a0 Key questions going forward will be whether the company can sustain a recurring need for its ZAML platform \u2013 are models built once and rarely retrained\u2014and whether there is an opportunity to horizontally extend its services across industries.<\/p>\n

 <\/p>\n

 <\/p>\n

[1]<\/a> http:\/\/www.tradestreaming.com\/2016\/04\/19\/5-surprising-things-you-might-not-know-about-fico\/<\/p>\n

[2]<\/a> https:\/\/www.washingtonpost.com\/business\/zestfinance-issues-small-high-rate-loans-uses-big-data-to-weed-out-deadbeats\/2014\/10\/10\/e34986b6-4d71-11e4-aa5e-7153e466a02d_story.html?utm_term=.b45ddc768f13<\/p>\n

[3]<\/a> https:\/\/www.forbes.com\/sites\/laurashin\/2015\/10\/06\/alternative-lender-zestfinance-raises-150-million-in-funding-from-fortress\/#2207479c1f97<\/p>\n

[4]<\/a> https:\/\/www.americanbanker.com\/news\/zestfinance-aims-to-fix-underwriting-for-the-underbanked<\/p>\n

[5]<\/a> https:\/\/www.washingtonpost.com\/business\/zestfinance-issues-small-high-rate-loans-uses-big-data-to-weed-out-deadbeats\/2014\/10\/10\/e34986b6-4d71-11e4-aa5e-7153e466a02d_story.html?utm_term=.b45ddc768f13<\/p>\n

[6]<\/a> https:\/\/www.washingtonpost.com\/business\/zestfinance-issues-small-high-rate-loans-uses-big-data-to-weed-out-deadbeats\/2014\/10\/10\/e34986b6-4d71-11e4-aa5e-7153e466a02d_story.html?utm_term=.b45ddc768f13<\/p>\n

[7]<\/a> https:\/\/techcrunch.com\/2016\/07\/17\/baidu-invests-in-zestfinance-to-develop-search-powered-credit-scoring-for-china\/<\/p>\n

[8]<\/a> Website<\/p>\n

[9]<\/a> Website<\/p>\n

[10]<\/a> https:\/\/www.consumerfinance.gov\/fair-lending\/<\/p>\n","protected":false},"excerpt":{"rendered":"

In 2010, roughly 8.3% of US consumers, or 19 million people, were considered \u2018unscorable\u2019 by FICO, a credit rating service whose models serve as a key underpinning of the US system for assessing credit worthiness.[1] Included in this contingent of unscorable consumers are those lacking a credit history, read: millennials, who represent an untapped market opportunity for lenders. Also excluded from FICO are the underbanked, those who lack bank accounts and primarily transact in cash. Not only are the underbanked denied access to loans based on typical FICO filters, but they must resort to predatory alternatives, such as payday loans, whose prohibitively high interest rates trap them in a vicious cycle of deep indebtedness that is difficult to dig out of. Has the FICO score become an obsolete filter? And can a lender step in to provide loans to these underserved segments where traditional banks have neglected to do so \u2013 and profitably?<\/p>\n","protected":false},"author":972,"featured_media":5285,"comment_status":"open","ping_status":"closed","template":"","categories":[146,366],"class_list":["post-5284","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-fintech","category-machine-learning"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-and-analytics-as-digital-assets\/","yoast_head":"\nIs FICO outdated?\u00a0 ZestFinance: An Alternative to the FICO Credit Score - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/is-fico-outdated-zestfinance-an-alternative-to-the-fico-credit-score\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Is FICO outdated?\u00a0 ZestFinance: An Alternative to the FICO Credit Score - Digital Innovation and Transformation\" \/>\n<meta property=\"og:description\" content=\"In 2010, roughly 8.3% of US consumers, or 19 million people, were considered \u2018unscorable\u2019 by FICO, a credit rating service whose models serve as a key underpinning of the US system for assessing credit worthiness.[1] Included in this contingent of unscorable consumers are those lacking a credit history, read: millennials, who represent an untapped market opportunity for lenders. 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