Poster: A Scoping Review of Alternative Credit Scoring Literature

Abstract

This paper covers a scoping review to establish the breadth of alternative credit scoring literature. The field is nascent and gaining popularity due to the crucial role alternative data is playing to accelerate financial inclusion. Historically, evaluating creditworthiness required availability of past financial activity such as loan repayment. Such stringent requirements rendered people with little or no financial history ‘credit invisible’. Advancements in Artificial Intelligence and Machine Learning have enabled scoring algorithms to work with non-financial data such as digital footprints from mobile devices and psychometric data to compute credit scores. Although the largest portion of ‘credit invisibles’ are in developing economies, research in the area is predominantly originating from developed economies and most alternative credit scoring models are trained with data from developed economies. There is need for more research from developing contexts and utilization of alternative data from populations with a smaller digital footprint.

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