Preprint Article Version 1 This version is not peer-reviewed

Which Attributes Matter the Most for Loan Origination? A Neural Attention Approach

Version 1 : Received: 12 February 2020 / Approved: 14 February 2020 / Online: 14 February 2020 (02:45:01 CET)

How to cite: Alexos, A.; Chatzis, S. Which Attributes Matter the Most for Loan Origination? A Neural Attention Approach. Preprints 2020, 2020020180 (doi: 10.20944/preprints202002.0180.v1). Alexos, A.; Chatzis, S. Which Attributes Matter the Most for Loan Origination? A Neural Attention Approach. Preprints 2020, 2020020180 (doi: 10.20944/preprints202002.0180.v1).

Abstract

In this paper we address the understanding of the problem, of why a deep learning model decides that an individual is eligible for a loan or not. Here we propose a novel approach for inferring, which attributes matter the most, for making a decision in each specific individual case. Specifically we leverage concepts from neural attention to devise a novel feature wise attention mechanism. As we show, using real world datasets, our approach offers unique insights into the importance of various features, by producing a decision explanation for each specific loan case. At the same time, we observe that our novel mechanism, generates decisions which are much closer to the decisions generated by human experts, compared to the existent competitors.

Subject Areas

deep learning; neural attention; loans; loan origination; machine learning

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