{"id":33936,"date":"2018-11-13T17:28:26","date_gmt":"2018-11-13T22:28:26","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-credit-in-uganda\/"},"modified":"2018-11-13T17:33:49","modified_gmt":"2018-11-13T22:33:49","slug":"predicting-loan-repayment-rates-for-unsecured-loans-in-uganda","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/","title":{"rendered":"Predicting loan repayment rates for unsecured loans in Uganda"},"content":{"rendered":"

Importance of machine learning for Numida<\/strong><\/p>\n

Numida is a financial technology start-up in Uganda providing unsecured credit to small business owners.[1]<\/a>\u00a0 Users enter their sales and expenses into the app in order to apply for unsecured loans. Numida\u2019s hypothesis is that a user\u2019s app usage can predict the probability of loan repayment.\u00a0 As such, users must use the app for at least 3 days before they can receive a loan in order to provide Numida with sufficient data to gauge a user\u2019s credit worthiness.\u00a0 Numida\u2019s use of data to make predictions is at the core of its product offering and is one of the most common applications of machine learning.[2]<\/a><\/p>\n

 <\/p>\n

Numida\u2019s activities in machine learning<\/strong><\/p>\n

Accuracy of predictive models depends on data \u2013 there needs to be a large enough sample size and variation in the data for machine learning to be useful.\u00a0 Numida thus needs to a large number of users who have different usage patterns on the app (e.g. frequency of data input, type of data entered, whether or not they apply for a loan, and the loan size they take out).\u00a0 There also needs to be a variation in outcomes, i.e. some users will need to repay the loan and some will need to default, in order for machine learning to be useful in predicting repayment probabilities.<\/p>\n

 <\/p>\n

For the past year, Numida has been struggling with getting people to use the app and apply for loans.\u00a0 This means that the sample size has not been large enough for Numida to use statistics to identify app usage behaviors that are correlated with higher probabilities of repayment, since app usage has been low and the number of borrowers even lower.\u00a0 As such, management has focused on making the app more user-friendly to increase user retention and experimenting with the loan eligibility criteria to broaden the borrower base.\u00a0 Only with more users and borrowers will Numida be able to use machine learning for predictive analysis.<\/p>\n

 <\/p>\n

Given that it is an early stage start up that has yet to find product-market fit, Numida is more focused on short term strategy and does not yet have a 2+ year strategy.<\/p>\n

 <\/p>\n

Recommendations<\/strong><\/p>\n

The biggest challenge Numida faced as of August 2018 was user retention and getting loan applicants, an issue ill-suited for machine learning given the low user base (<150 weekly active users).\u00a0 With this small sample size, a simple before vs. after analysis is sufficient to determine whether a change in the user interface or the loan product resulted in any uptake.\u00a0 Some analysis has been conducted in this vein and Numida needs to continue to build a culture of monitoring the effectiveness of their client acquisition strategies.<\/p>\n

 <\/p>\n

Once Numida finds product-market fit and acquires a steady stream of users and borrowers, it can use machine learning to streamline its loan application process.\u00a0 Currently Numida relies on humans to check each loan application submitted on the app before transferring money to the user.\u00a0 As part of the due diligence, sometimes loan officers need to contact users to request them to reupload documents because of poor photo quality, causing delays in loan disbursement.\u00a0 This friction in the customer journey causes users to wonder during the delay if Numida\u2019s value proposition of unsecured credit is a scam and discourages users from persevering in building up a habit of entering sales and expenses into the app.\u00a0 An ability to leverage machine learning to conduct basic due diligence of photo quality when users upload photos of their documents and provide immediate feedback on whether it needs to be retaken could streamline the customer journey and result in higher user retention.[3]<\/a>\u00a0 However, this will likely be worth the investment once Numida has sufficient application volumes.<\/p>\n

 <\/p>\n

Open questions<\/strong><\/p>\n

There are several open questions regarding the applicability of machine learning within the developing world.\u00a0 Ugandan\u2019s digital footprint is still sparse compared to those residing in the developed world due to Uganda\u2019s cash-based economy and the careful rationing of internet use due to the cost of internet data.\u00a0 Numida\u2019s hypothesis of using data to predict a user\u2019s creditworthiness is reliant on inputs that are hard to collect \u2013 users are reluctant to build a habit of inputting their sales and expenses into the app, yet the opportunity to collect user data is limited since internet use is still fairly limited.\u00a0 In such a situation, how can Numida get vast amounts of data cheaply and in a more convenient way for users that would enable Numida to properly predict probabilities for loan repayment? (787)<\/p>\n

[1]<\/a> Numida. (2018).\u00a0About<\/i>. [online] Available at: http:\/\/www.numida.co\/about [Accessed 13 Nov. 2018].<\/p>\n

[2]<\/a> Yeomans, M. (2015). \u201cWhat every manager should know about machine learning.\u201d 性视界 Business<\/em><\/p>\n

Review Digital Articles<\/em>, 7 Jul. 2015.<\/p>\n

[3]<\/a> Higginbotham, S. (2018).\u00a0http:\/\/fortune.com<\/i>. [online] Fortune. Available at: http:\/\/fortune.com\/2015\/06\/15\/facebook-ai-moments\/ [Accessed 13 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"

Using machine learning to gauge small business owner's credit worthiness to provide unsecured loans. Real application limited by small sample size. Basic statistical analysis could be more useful than complicated predictive analysis for small data sets.<\/p>\n","protected":false},"author":11046,"featured_media":34022,"comment_status":"open","ping_status":"closed","template":"","categories":[4289,3627,3492,1245,346],"class_list":["post-33936","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-credit","category-developing-countries","category-disruptive-startups","category-east-africa","category-machine-learning","hck-taxonomy-organization-numida","hck-taxonomy-industry-technology","hck-taxonomy-country-uganda"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nPredicting loan repayment rates for unsecured loans in Uganda - Technology and Operations Management<\/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-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting loan repayment rates for unsecured loans in Uganda - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Using machine learning to gauge small business owner's credit worthiness to provide unsecured loans. Real application limited by small sample size. Basic statistical analysis could be more useful than complicated predictive analysis for small data sets.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"article:modified_time\" content=\"2018-11-13T22:33:49+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Picture1-24.png\" \/>\n\t<meta property=\"og:image:width\" content=\"418\" \/>\n\t<meta property=\"og:image:height\" content=\"80\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/\",\"name\":\"Predicting loan repayment rates for unsecured loans in Uganda - Technology and Operations Management\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/Picture1-24.png\",\"datePublished\":\"2018-11-13T22:28:26+00:00\",\"dateModified\":\"2018-11-13T22:33:49+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/#primaryimage\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/Picture1-24.png\",\"contentUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/Picture1-24.png\",\"width\":418,\"height\":80},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Submissions\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Predicting loan repayment rates for unsecured loans in Uganda\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\",\"name\":\"Technology and Operations Management\",\"description\":\"MBA Student Perspectives\",\"potentialAction\":[{\"@type\":\"性视界Action\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Predicting loan repayment rates for unsecured loans in Uganda - Technology and Operations Management","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/","og_locale":"en_US","og_type":"article","og_title":"Predicting loan repayment rates for unsecured loans in Uganda - Technology and Operations Management","og_description":"Using machine learning to gauge small business owner's credit worthiness to provide unsecured loans. Real application limited by small sample size. Basic statistical analysis could be more useful than complicated predictive analysis for small data sets.","og_url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/","og_site_name":"Technology and Operations Management","article_modified_time":"2018-11-13T22:33:49+00:00","og_image":[{"width":418,"height":80,"url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Picture1-24.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/","url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/","name":"Predicting loan repayment rates for unsecured loans in Uganda - Technology and Operations Management","isPartOf":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website"},"primaryImageOfPage":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/#primaryimage"},"image":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/#primaryimage"},"thumbnailUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Picture1-24.png","datePublished":"2018-11-13T22:28:26+00:00","dateModified":"2018-11-13T22:33:49+00:00","breadcrumb":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/#primaryimage","url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Picture1-24.png","contentUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Picture1-24.png","width":418,"height":80},{"@type":"BreadcrumbList","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-loan-repayment-rates-for-unsecured-loans-in-uganda\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/d3.harvard.edu\/platform-rctom\/"},{"@type":"ListItem","position":2,"name":"Submissions","item":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/"},{"@type":"ListItem","position":3,"name":"Predicting loan repayment rates for unsecured loans in Uganda"}]},{"@type":"WebSite","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website","url":"https:\/\/d3.harvard.edu\/platform-rctom\/","name":"Technology and Operations Management","description":"MBA Student Perspectives","potentialAction":[{"@type":"性视界Action","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/33936","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission"}],"about":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/types\/hck-submission"}],"author":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/users\/11046"}],"replies":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/comments?post=33936"}],"version-history":[{"count":0,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/33936\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media\/34022"}],"wp:attachment":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media?parent=33936"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/categories?post=33936"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}