Deep learning for text-based credit scoring for micro, small and medium enterprises

Stevenson, Matthew and Bravo, Cristian (2018) Deep learning for text-based credit scoring for micro, small and medium enterprises. 29th European Conference on Operational Research, , Valencia, Spain. 08 - 11 Jul 2018.

Abstract

Personal credit risk models are built upon a wealth of structured sociodemographic and behavioural data. It tends to be high in volume and low in cost and as a result, personal lending is a highly automated process. This, however, is not true for micro, medium and small business credit processing which is cumbersome and expensive for lenders.
Often, a lack of sufficient structured data and the bespoke nature a credit request requires expert judgement on the creditworthiness of an organisation. This occurs in the first instance by a financial analyst who generates a written report, which is then usually passed onto a further assessor who makes the final decision based on the written report combined with other sources of available data.

The purpose of this research is to eliminate the requirement for a second stage of assessment - where both the traditional variables and text-based evaluation by the credit agent are considered - by developing Deep Learning models that can capture the rich and dynamic information available from the written financial analyst reports, outputting a probability score that can then be used alongside other structured sources of information.

The results suggest that the implementation of a semi-automated process would allow for both a more accurate and cost-effective approach assessing credit risk for micro, small and medium enterprises.

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Contributors

Author: Matthew Stevenson

Author: Cristian Bravo

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