Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

Huei-Wen Teng

25 citations0/0 files storedReview of Pacific Basin Financial Markets and Policies

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

Abstract

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

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References

2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients 2016Deep Learning for Finance: Deep Portfolios 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 2001Bankruptcy prediction for credit risk using neural networks: A survey and new results

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