Welcome to the InfoSci Platform
Deep-Auto Encoders for Detecting Credit Card Fraud

Deep-Auto Encoders for Detecting Credit Card Fraud

Sudarshana Kerenalli, Mylara C. Reddy, A. Usha Ruby
Copyright: © 2022 |Pages: 29
ISBN13: 9781799887546|ISBN10: 1799887545|ISBN13 Softcover: 9781799887553|EISBN13: 9781799887560
DOI: 10.4018/978-1-7998-8754-6.ch017
Cite Chapter Cite Chapter

MLA

Kerenalli, Sudarshana, et al. "Deep-Auto Encoders for Detecting Credit Card Fraud." Handbook of Research on the Significance of Forensic Accounting Techniques in Corporate Governance, edited by Suleman Sherali Kamwani, et al., IGI Global, 2022, pp. 330-358. https://doi.org/10.4018/978-1-7998-8754-6.ch017

APA

Kerenalli, S., Reddy, M. C., & Ruby, A. U. (2022). Deep-Auto Encoders for Detecting Credit Card Fraud. In S. Kamwani, E. Vieira, M. Madaleno, & G. Azevedo (Eds.), Handbook of Research on the Significance of Forensic Accounting Techniques in Corporate Governance (pp. 330-358). IGI Global. https://doi.org/10.4018/978-1-7998-8754-6.ch017

Chicago

Kerenalli, Sudarshana, Mylara C. Reddy, and A. Usha Ruby. "Deep-Auto Encoders for Detecting Credit Card Fraud." In Handbook of Research on the Significance of Forensic Accounting Techniques in Corporate Governance, edited by Suleman Sherali Kamwani, et al., 330-358. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8754-6.ch017

Export Reference

Mendeley
Favorite

Abstract

Internet-based payment methods in recent times are rapidly growing both in developing and developed economies. Credit card-based payment systems are among the prominent cashless payment methods in all economies. Credit card frauds by cyber-criminals are increasing in spite of several precautionary measures. Thus, fraud detection in real-time is a challenging task. Several machine learning tasks have attempted to solve the problem. This chapter proposes a two-step method to detect credit card fraud by coupling the deep learning-machine learning approaches. In the first stage, the dimensionality of the data set is reduced to 50% by a deep auto-encoder. A machine learning classifier classifies the instances in the second stage. Among the machine learning algorithms, the CatBoost and Random Forest achieved better performance. Their performance aligned with the state-of-the-art approaches. The proposed method is robust against the labor-intensive feature selection and imbalanced class problems.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.