{"id":35436,"date":"2018-11-13T19:13:17","date_gmt":"2018-11-14T00:13:17","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-at-capital-one\/"},"modified":"2018-11-13T19:13:17","modified_gmt":"2018-11-14T00:13:17","slug":"machine-learning-in-credit-assessment-at-capital-one","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-in-credit-assessment-at-capital-one\/","title":{"rendered":"Machine Learning in Credit Assessment at Capital One"},"content":{"rendered":"

Since its inception, Capital One has been recognized as a leading-edge financial institution when it comes to its use of technology in banking1<\/sup>. By leveraging big data, Capital One has attracted 45 million customers, making it a top 10 U.S. bank2<\/sup>. But there\u2019s certainly room to grow and new technologies to fuel this growth. One potential innovation for growth is machine learning for customer risk assessment, not only to make better lending decisions but also to expand financial inclusion to un- and under-banked Americans.<\/p>\n

Machine learning is on Capital One\u2019s radar. In 2017, the bank hired Nitzan Mekel to build and lead their Machine Learning organization3<\/sup>, which is growing with speed\u2014in the last month alone, nearly 30 \u201cMachine Learning\u201d jobs were posted on the bank\u2019s Glassdoor page4<\/sup>. Beyond being an area of human capital investment, machine learning is receiving the bank\u2019s research dollars, as well3<\/sup>; the research team\u2019s summarized findings are summarized in a dedicated Machine Learning section on the company\u2019s tech blog5<\/sup> and Domenic Puzio and Jennifer Van from Capital One spoke about \u201cHow to Become a Machine Learning Expert in Under an Hour\u201d at the South by Southwest conference6<\/sup>. Beyond research, the bank is incorporating machine learning into product development, citing Eno, one of the industry\u2019s first AI-powered customer service chatbots, and real-time fraud detection as customer-available applications of machine learning at Capital One3<\/sup>. Machine learning seems to be integrating into \u201calmost every facet of [the] business,\u201d says Mekel3<\/sup>.<\/p>\n

While exciting progress has been made, there\u2019s more the bank can do to expand machine learning into product development, specifically customer risk assessment. Most banks use few pieces of data, namely credit score, to make lending decisions7<\/sup>, but this may not provide a holistic picture of the customer\u2019s credit-worthiness. By incorporating wider data sets into machine learning models, Capital One could better predict a customer\u2019s fit for financial products. For example, during the Kabbage online application process, customers link their PayPal and Quickbooks accounts through APIs; Kabbage also considers social media data (e.g., Twitter followers, Facebook likes, customer reviews) when making small business loan decisions8<\/sup>.<\/p>\n

New and smarter data models would help banks identify potential customers from those automatically rejected because of a low or nonexistent credit score. This is particularly helpful for the 63 million un- or under-banked Americans, who sought financial services products outside of the banking system9<\/sup>. If banks, like Capital One, were to expand the data they use to make lending decision, and make smarter decisions powered by machine learning models, credit-worthy un-banked customers would gain access to capital and banks would gain new customers. It\u2019s a win-win proposition.<\/p>\n

Capital One has recognized the ability for machine learning to enhance its credit risk assessments, but development efforts are not ready for product-launch yet. Mekel points to \u201cexplainability\u201d as the reason for the delay: \u201cBeing in a heavily regulated environment, we want to make sure that we\u2019re not just meeting the regulatory requirements, but that we help set the standard for what fair and ethical machine learning deployment looks like,\u201d Mekel says3<\/sup>. As the models and algorithms become more advanced, it becomes harder to explain to customers and regulators alike, how the underlying models work and with complexity also comes an increased risk of harm from biased, unethical, or unfair outcomes3<\/sup>. Mekel and his team view these as challenges to be addressed before further machine learning applications are deployed at Capital One.<\/p>\n

As Capital One more deeply integrates machine learning into consumer product development, the team should consider many things, including data selection and training to eliminate biases; consumer sentiment towards granting broader access to personal data; and partnership with the regulators to make a commercially viable, safe models for lending decisions. To help tackle these challenges, Capital One should consider hiring data ethicists as it seeks to be the standard setter for fair and ethical machine learning. To address the \u201cexplainability\u201d issue, Capital One should also seek to proactively educate consumers and regulators alike on how broader data sources are being used to make better business decisions.<\/p>\n

There are key questions to be considered as Capital One continues to integrate machine learning into its business DNA. What are potential data sources Capital One should consider when assessing someone\u2019s credit worthiness, and what are the potential sources of biases? What are other ways that Capital One and other financial institutions can use machine learning to close the inclusion gap? I applaud Capital One for its machine learning efforts and its vision to use new innovations to benefit both consumers and the bank; I hope to see machine learning applied to address the issue of financial inclusion in the coming years. (775 words)<\/p>\n

 <\/p>\n

Sources<\/strong><\/p>\n

1<\/sup> Capital One Financial Corporation, \u201cAwards and Recognition\u201d, http:\/\/press.capitalone.com\/phoenix.zhtml?c=251626&p=irol-awards<\/a>, accessed November 2018.<\/p>\n

2<\/sup> Capital One Financial Corporation, \u201cAbout Capital One\u201d, https:\/\/www.capitalone.com\/about\/<\/a>, accessed November 2018.<\/p>\n

3<\/sup> Allison Toh, \u201cAI in Your Wallet: Capital One Banks on Machine Learning,\u201d AI Podcast <\/em>(blog), October 12, 2018, https:\/\/blogs.nvidia.com\/blog\/2018\/10\/12\/ai-in-your-wallet-capital-one-banks-on-machine-learning\/<\/a>, accessed November 2018.<\/p>\n

4<\/sup> Glassdoor, \u201cMachine Learning Engineer Jobs\u201d, https:\/\/www.glassdoor.com\/Jobs\/Capital-One-machine-learning-engineer-Jobs-EI_IE3736.0,11_KO12,37.htm<\/a>, accessed November 2018.<\/p>\n

5<\/sup> Capital One Tech, \u201cMachine Learning\u201d, https:\/\/medium.com\/capital-one-tech\/machine-learning\/home<\/a>, accessed November 2018.<\/p>\n

6<\/sup> Capital One Tech, \u201cBecome a Machine Learning Expert in Under an Hour\u201d, Capital One Tech <\/em>(blog), February 28, 2018, https:\/\/medium.com\/capital-one-tech\/become-a-machine-learning-expert-in-under-an-hour-8437939ae1e2<\/a>, accessed November 2018.<\/p>\n

7<\/sup> Experian, \u201cHow lenders make\u2014and monitor\u2014credit decisions\u201d, https:\/\/www.experian.com\/assets\/consumer-education-content\/brochures\/Reports_Issue_6.pdf<\/a>, accessed November 2018.<\/p>\n

8<\/sup> Darren Dahl, \u201cThe Six-Minute Loan: How Kabbage is Upending Small Business Lending and Building a Very Big Business\u201d, Forbes, May 6, 2015, https:\/\/www.forbes.com\/sites\/darrendahl\/2015\/05\/06\/the-six-minute-loan-how-kabbage-is-upending-small-business-lending-and-building-a-very-big-business\/#6b60ecfa9042<\/a>, accessed November 2018.<\/p>\n

9<\/sup> FDIC National Survey of Unbanked and Underbanked Households, https:\/\/www.fdic.gov\/householdsurvey\/2017\/2017execsumm.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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