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

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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Credit Card Fraud Detection with Machine Learning Methods

2019 4th International Conference on Computer Science and Engineering (UBMK) ◽

10.1109/ubmk.2019.8906995 ◽

2019 ◽

Author(s):

Gokhan Goy ◽

Cengiz Gezer ◽

Vehbi Cagri Gungor

Keyword(s):

Machine Learning ◽

Credit Card ◽

Fraud Detection ◽

Learning Methods ◽

Credit Card Fraud ◽

Machine Learning Methods

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Machine Learning Methods for Analysis Fraud Credit Card Transaction

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1164.0886s19 ◽

2019 ◽

Vol 8 (6S) ◽

pp. 870-874

Keyword(s):

Machine Learning ◽

Credit Card ◽

Negative Impact ◽

Online Banking ◽

Data Sets ◽

Learning Methods ◽

Machine Learning Methods ◽

One Time Password ◽

Day By Day ◽

Isolation Forest

The use of online banking and credit card is increasing day by day. As the usage of credit/debit card or netbanking is increasing, the possibility of many fraud activities is also increasing. There are many incidents are happened in presently where because of lack of knowledge the credit card users are sharing their personal details, card details and one time password to a unknown fake call. And the result will be fraud happened with the account. Fraud is the problem that it is very difficult to trace the fraud person if he made call from a fake identity sim or call made by some internet services. So in this research some supervised methodologies and algorithms are used to detect fraud which gives approximate accurate results. The illegal or fraud activities put very negative impact on the business and customers loose trust on the company. It also affects the revenue and turnover of the company. In this research isolation forest algorithm is applied for classification to detect the fraud activities and the data sets are collected from the professional survey organizations.

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Performance Evaluation of Machine Learning Methods for Credit Card fraud Detection using SMOTE and AdaBoost

IEEE Access ◽

10.1109/access.2021.3134330 ◽

2021 ◽

pp. 1-1

Author(s):

Emmanuel Ileberi ◽

Sun Yanxia ◽

Zenghui Wang

Keyword(s):

Machine Learning ◽

Performance Evaluation ◽

Credit Card ◽

Fraud Detection ◽

Learning Methods ◽

Credit Card Fraud ◽

Machine Learning Methods

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Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings

10.4018/978-1-7998-8455-2.ch011 ◽

2022 ◽

pp. 285-305

Author(s):

Siddharth Vinod Jain ◽

Manoj Jayabalan

Keyword(s):

Machine Learning ◽

Credit Risk ◽

Financial Services ◽

Credit Card ◽

Large Scale ◽

Cost Savings ◽

Learning Models ◽

Learning Methods ◽

Machine Learning Methods ◽

Card Payment

The credit card has been one of the most successful and prevalent financial services being widely used across the globe. However, with the upsurge in credit card holders, banks are facing a challenge from equally increasing payment default cases causing substantial financial damage. This necessitates the importance of sound and effective credit risk management in the banking and financial services industry. Machine learning models are being employed by the industry at a large scale to effectively manage this credit risk. This chapter presents the application of the various machine learning methods like time series models and deep learning models experimented in predicting the credit card payment defaults along with identification of the significant features and the most effective evaluation criteria. This chapter also discusses the challenges and future considerations in predicting credit card payment defaults. The importance of factoring in a cost function to associate with misclassification by the models is also given.

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