{"id":7828,"date":"2018-04-09T10:53:00","date_gmt":"2018-04-09T14:53:00","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/lenddo-using-data-analytics-to-boost-financial-inclusion\/"},"modified":"2018-04-09T10:53:57","modified_gmt":"2018-04-09T14:53:57","slug":"lenddo-using-data-analytics-to-boost-financial-inclusion","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/lenddo-using-data-analytics-to-boost-financial-inclusion\/","title":{"rendered":"Lenddo: using data analytics to boost financial inclusion"},"content":{"rendered":"

The challenge<\/strong><\/p>\n

Access to financial services is a key element for reducing poverty and boosting economic prosperity. There are approximately 2 billion people and 200 million small and micro enterprises in emerging economies that lack access to basic financial services such as savings and credit services. Even in developing nations with high degrees of financial inclusion such as Malaysia where 92% of adults have bank accounts, there is a mismatch between the financing needs of the population and the financial services available to them. The result is that individuals and SMEs often end up paying extremely high fees for services that do not adequately address their needs.<\/p>\n

Even though there has been some progress over the past few years, millions of lower and middle-class citizens in emerging economies are affected given the chicken and egg dynamic of the traditional financial system. Individuals have the steady income to take on a loan but they lack the credit history to get approval from financial institutions. Structural issues such as lack of infrastructure and information, weak credit score bureaus, inefficient institutions, among others complicate the situation further.<\/p>\n

Lenddo<\/strong><\/p>\n

The traditional credit assessment process relies on evaluating the borrower\u2019s capacity to pay. This is burdensome and costly for both the lender and the borrower, especially in the case of smaller loan sizes. Lenddo is taking a different approach by collecting both traditional and non-traditional data and using artificial intelligence to predict an individual\u2019s likelihood to pay.<\/p>\n

Lenddo does not actually provide financial services directly to individuals. It enables financial institutions to do predictive analytics to service new client segments in a profitable and scalable manner.<\/p>\n

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How does the process work?<\/strong><\/p>\n

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  1. Applicants submit their applications<\/em> \u2013 the application consists of a simple form with basic personal information (name, date of birth, email address, mobile number, employer and education), a psychometric test and granting data access permission to Lenddo.<\/li>\n
  2. Data collection<\/em> – Lenddo collects applicant data from traditional sources such as credit bureau data and financial transactions (if available) and non-traditional data from the following sources:\n