Machine learning-driven credit risk: a systemic review

Neural Computing and Applications - Tập 34 - Trang 14327-14339 - 2022
Si Shi1, Rita Tse1,2, Wuman Luo1, Stefano D’Addona3, Giovanni Pau4,5
1Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
2Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education, Macao Polytechnic University, Macao SAR, China
3Department of Political Science, University of Roma Tre, Rome, Italy
4Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
5UCLA Samueli Computer Science, University of California, Los Angeles, USA

Tóm tắt

Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

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Neural Computing and Applications

Tập 34

14327-14339

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