{"id":34633,"date":"2018-11-13T23:32:24","date_gmt":"2018-11-14T04:32:24","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/using-machine-learning-to-improve-lending-in-the-emerging-markets\/"},"modified":"2018-11-13T23:32:24","modified_gmt":"2018-11-14T04:32:24","slug":"using-machine-learning-to-improve-lending-in-the-emerging-markets","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/using-machine-learning-to-improve-lending-in-the-emerging-markets\/","title":{"rendered":"Using machine learning to improve lending in the emerging markets"},"content":{"rendered":"

At the heart of most Western banking systems is an integrated and trusted credit scoring system accessible to all market participants. Because of this system, participants can confidently make lending decisions (both whether to issue a loan and at what price) based on risk models that have been refined over time. In many emerging market nations such credit scoring infrastructure does not exist. As a result, banks in such markets are often unwilling to lend to most consumers and businesses. This trend has led to a funding gap of $2.1-$2.6 trillion dollars for SMEs (Small and Medium-sized Enterprises)[1]<\/a>. Several firms have stepped up to fill that void with dynamic credit scoring solutions built on machine learning. A notable player is Mines.io.<\/p>\n

Why Apply Machine Learning to Credit Scoring?<\/p>\n

Machine-learning augmented credit risk modeling allows for a level of nuance not easily attained in traditional models. In traditional credit risk modeling, customers are tagged with easily observed identifiers (new customer, old customer, high earner, etc.) and the credit behavior of these groups is analyzed to discern key trends; with these trends being incorporated into a composite \u201ccredit score\u201d.[2]<\/a> The issue with this approach is that these broader categories don\u2019t necessary offer the level of granularity needed to make the most optimal lending decisions. Consequently, under this approach companies are likely to \u201cleave money on the table\u201d by opting not to lend to entire swathes of the economy or not adequately pricing-in the risk of a specific individual or entity whose activities would distinguish it from its broader peer set. Using machine learning, companies like Mines.io can execute micro-segmentation based on customer behaviors rather that non-behavior identifiers.[3]<\/a> Additionally, machine learning techniques are also able to \u201ctrain\u201d models based on additional data sources to improve the predictive power of credit models.[4]<\/a><\/p>\n

Does the Technology Have any limitations in an Emerging Markets Context?<\/p>\n

Regulatory limitations as well as potential data issues limit the ability of lenders to solely rely on machine learning derived-decisions. From a regulatory perspective, lenders are often required to explain very clearly the methodology\/rationale for their lending decisions. As such, lenders cannot have a \u201cblack box\u201d AI process. Separately, because of potential data quality issues companies must be careful that the source data feeding into their models cannot be easily manipulated. Lastly, in the emerging market context in which companies like Mines.io operate, Companies must be vigilant for backward-looking bias in the data used to generate insights in their models. In regions with rising middle classes many of whom are making money in novel ways (across many small business ventures, etc.), it can be hard to ascertain what aspects of previously successful borrowers led to success and whether those aspects will be salient for new applicants.<\/p>\n

What is Mines.io and what is their go-to-market strategy?<\/p>\n

Founded in 2014, Mines.io was started by silicon-valley based data scientists keen to build the infrastructure needed to allow financial institutions to more confidently lend to SMEs and individuals, track the credit history of those entities across their entire credit life, and integrate that credit history into an iterative credit algorithm to improve go-forward lending decisions.[5]<\/a> Having built its system, the key question was how to go to market. From a regulatory perspective, direct lending would likely require banking licenses and compliance\/regulatory complexities as well as a more complicated sales process that would take the company farther away from its core data science competencies.<\/p>\n

In the short-term, to counter compliance and regulatory complexities associated with directly offering lending products, the Company has instead opted to follow a SaaS model. As part of this strategy, the company has launched a four-fronted product suite selling lending software-as-a-service products to incumbent banks, mobile operators, retailers, and payment processors. To each customer profile, the company offers a white-label service that allows the client to offer lending products to its customers that make lending and rate decisions based on an algorithmic credit engine.[6]<\/a> To date, the Company has focused on small unsecured consumer loans. In order to monetize, the company charges a specified percentage of interest income from loans originated \/ assessed by the Mines.io platform.<\/p>\n

In the long-term, there are legitimate questions about how much of their underlying capital lenders will be willing to risk on lending decisions made by a 3rd<\/sup> party that does not bare any cost if the loan fails. Second, the Company will also have to answer the question of whether data collected for the purpose of assessing small consumer loan creditworthiness will be applicable for larger loans and other financial products (mortgages, insurance, car loans, etc.) as it looks for additional sources of revenue.<\/p>\n

(769 words)<\/p>\n

 <\/p>\n

[1]<\/a> Owens, J. and Wilhelm, L. (2018). Alternative Data Transforming SME Finance. [online] Gpfi.org. Available at: https:\/\/www.gpfi.org\/sites\/default\/files\/documents\/GPFI%20Report%20Alternative%20Data%20Transforming%20SME%20Finance.pdf [Accessed 13 Nov. 2018].<\/p>\n

[2]<\/a> Zoldi, S. (2018). How to Build Credit Risk Models Using AI and Machine Learning. [online] FICO. Available at: https:\/\/www.fico.com\/blogs\/analytics-optimization\/how-to-build-credit-risk-models-using-ai-and-machine-learning\/ [Accessed 13 Nov. 2018].<\/p>\n

[3]<\/a> Zoldi, S. (2018). How to Build Credit Risk Models Using AI and Machine Learning. [online] FICO. Available at: https:\/\/www.fico.com\/blogs\/analytics-optimization\/how-to-build-credit-risk-models-using-ai-and-machine-learning\/ [Accessed 13 Nov. 2018].<\/p>\n

[4]<\/a> Zoldi, S. (2018). How to Build Credit Risk Models Using AI and Machine Learning. [online] FICO. Available at: https:\/\/www.fico.com\/blogs\/analytics-optimization\/how-to-build-credit-risk-models-using-ai-and-machine-learning\/ [Accessed 13 Nov. 2018].<\/p>\n

[5]<\/a> MINES – Digital Credit For Emerging Markets. (2018). MINES – Digital Credit For Emerging Markets. [online] Available at: https:\/\/www.mines.io\/ [Accessed 13 Nov. 2018].<\/p>\n

[6]<\/a> MINES – Digital Credit For Emerging Markets. (2018). MINES – Digital Credit For Emerging Markets. [online] Available at: https:\/\/www.mines.io\/ [Accessed 13 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"

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