Credit scoring and approval rates changed substantially with the arrival of alternative lenders, mainly due to the adoption of new practices in collecting and analyzing potential borrower data. Alternative data[1] has played its role in expanding horizons for financial institutions and for creating an opportunity to enter the financial sector fir technology startups and data-rich international companies.

While social media, for example, as a source of data for creditworthiness assessment is still at a nascent stage, certain startups are already claiming to have incorporated information from social networks into their frameworks[2]. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable[3]), startups were found more imaginative than traditional institutions[4].

Alternative data requires alternative approach to data analytics, which wide adoption of machine learning and artificial intelligence brought.

“The use of alternative data sources, big data and machine learning technology, and other new artificial intelligence models could reduce the cost of making credit decisions and/or credit monitoring and lower operating costs for lenders. FinTech lenders could pass on the benefits of lower lending costs to their borrowers.” – Fintech Lending: Financial Inclusion, Risk Pricing, and Alternative Information[5]

Lenddo, for example, the company that enables financial institutions to do predictive analytics to service new client segments, in addition to collecting applicant data from traditional sources (credit bureau data and financial transactions, if available), also collects non-traditional data from such sources as psychometric tests, telecoms, browser history, mobile data (i.e., geolocation), social networks, and e-commerce transactions. HBS[6] shares that using advanced machine learning techniques, Lenddo’s predictive algorithms look at

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