# Neural Learning of Online Consumer Credit Risk

## TL;DR

This paper introduces 'NeuCredit', a deep learning model that effectively predicts consumer credit risk by capturing complex temporal and cross-sectional data interactions, and provides interpretable risk components, outperforming traditional methods.

## Contribution

The paper presents a novel deep learning model that captures serial dependencies, nonlinear interactions, and interpretable risk components in consumer credit risk prediction.

## Key findings

- Deep learning significantly improves forecasting accuracy over traditional methods.
- Inclusion of behavioral data enhances risk prediction.
- Model provides interpretable risk decomposition.

## Abstract

This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase. The "NeuCredit" model can capture both serial dependences in multi-dimensional time series data when event frequencies in each dimension differ. It also captures nonlinear cross-sectional interactions among different time-evolving features. Also, the predicted default probability is designed to be interpretable such that risks can be decomposed into three components: the subjective risk indicating the consumers' willingness to repay, the objective risk indicating their ability to repay, and the behavioral risk indicating consumers' behavioral differences. Using a unique dataset from one of the largest global e-commerce platforms, we show that the inclusion of shopping behavioral data, besides conventional payment records, requires a deep learning approach to extract the information content of these data, which turns out significantly enhancing forecasting performance than the traditional machine learning methods.