A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique

Credit risk is the risk of financial loss when a borrower fails to meet the financial commitment. While there are many factors that constitute credit risk, due diligence while giving loan (credit scoring), continuous monitoring of customer payments and other behaviour patterns could reduce the probability of accumulating non-performing assets (NPA) and frauds. In the past few years, the quantum of NPAs and frauds have gone up significantly, and therefore it has become imperative that banks and financial institutions use robust mechanisms to predict the performance of loans. The past two decades has seen an immense growth in the area of artificial intelligence, most notably machine learning (ML) with improved access to internet, data, and compute. Whilst there are credit rating agencies and credit scoring companies that provide their analysis of a customer to banks on a fee, the researchers continue to explore various ML techniques to improve the accuracy level of credit risk evaluation. In this survey paper, we performed a systematic literature review on existing research methods and ML techniques for credit risk evaluation. We reviewed a total of 136 papers on credit risk evaluation published between 1993 and March 2019. We studied the implications of hyper parameters on ML techniques being used to evaluate credit risk and, analyzed the limitations of the current studies and research trends. We observed that Ensemble and Hybrid models with neural networks and SVM are being more adopted for credit scoring, NPA prediction and fraud detection. We also realized that lack of comprehensive public datasets continue to be an area of concern for researchers.

Machine learning techniques for credit risk evaluation: a systematic literature review

There has been significant research into reject inference, with several statistical methods and machine learning techniques having been employed to infer the possible repayment behavior of rejected credit applicants. This study proposes a novel three-stage reject inference learning framework using unsupervised transfer learning and three-way decision theory that integrates: (1) the rejected credit sample selection using three-way decision theory, (2) higher-level representations to transfer learning from both accepted and selected rejected credit samples; and (3) credit scoring using the reconstructed accepted credit samples. This method was found to both perform well for reject inference and handle negative transfer learning problems. The numerical results were validated on Chinese credit data, the results from which demonstrated the superiority of the proposed reject inference method for credit risk management applications.

Three-stage reject inference learning framework for credit scoring using unsupervised transfer learning and three-way decision theory

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how human-computer interaction (HCI) and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 97 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, usability, and human-AI collaboration performance. Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.

pdf/towards-human-centered-explainable-ai-a-survey-of-user-1umcwub2yh.pdf

Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations.

A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment

Statistical and machine learning models in credit scoring: A systematic literature survey

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Statistical and machine learning models in credit scoring: A systematic literature survey