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Content type: Examples
In October 2018, the Singapore-based startup LenddoEFL was one of a group of microfinance startups aimed at the developing world that used non-traditional types of data such as behavioural traits and smartphone habits for credit scoring. Lenddo's algorithm uses numerous data points, including the number of words a person uses in email subject lines, the percentage of photos in a smartphone's library that were taken with a front-facing camera, and whether they regularly use financial apps on…
Content type: Examples
Because banks often decline to give loans to those whose "thin" credit histories make it hard to assess the associated risk, in 2015 some financial technology startups began looking at the possibility of instead performing such assessments by using metadata collected by mobile phones or logged from internet activity. The algorithm under development by Brown University economist Daniel Björkegren for the credit-scoring company Enterpreneurial Finance Lab was built by examining the phone records…