Enhancing Fraud Detection in Banking using Advanced Machine Learning Techniques

Aug 3, 2026

This study evaluates advanced machine learning techniques for detecting fraudulent activities in the banking industry using a comprehensive dataset of banking transactions. Multiple models are tested, including LightGBM, XGBoost, CatBoost, vote classifiers, and neural networks, with CatBoost achieving the highest accuracy for identifying fraudulent cases. Sampling and scaling strategies further improve detection performance, while the CatBoost ensemble method enhances efficiency of fraud identification. Results support the use of these methods to reduce financial losses and secure transactions, strengthening banking trust and security.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Digital transformation and rising fraud

Limits of rule-based detection

Motivation for advanced machine learning

Aberystwyth University Enhancing Fraud Detection in Banking using Advanced Machine Learning Techniques Detthamrong, Umawadee; Chansanam, Wirapong; Boongoen, Tossapon; Iam-On, Natthakan Published in: International Journal of Economics and Financial Issues DOI: 10.32479/ijefi.16613 Publication date: 2024 Citation for published version (APA): Detthamrong, U. , Chansanam, W. , Boongoen, T. , & Iam-On, N. (2024) . Enhancing Fraud Detection in Banking using Advanced Machine Learning Techniques. International Journal of Economics and Financial Issues, 14(5), 177-184. [https://doi.org/10.32479/ijefi.16613](https://doi.org/10.32479/ijefi.16613) Document License CC BY-NC-ND General rights Copyright and moral rights for the publications made accessible in the Aberystwyth Research Portal (the Institutional Repository) are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the Aberystwyth Research Portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the Aberystwyth Research Portal Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. tel: +44 1970 62 2400 email: [is@aber.ac.uk](is@aber.ac.uk) Download date: 02. Aug. 2026 International Journal of Economics and Financial Issues ISSN: 2146-4138 available at [http: www.econjournals.com](http: www.econjournals.com) International Journal of Economics and Financial Issues, 2024, 14(5), 177-184. Enhancing Fraud Detection in Banking using Advanced Machine Learning Techniques Umawadee Detthamrong1, Wirapong Chansanam2*, Tossapon Boongoen3, Natthakan Iam-On3 1College of Local Administration, Khon Kaen University, Khon Kaen, Thailand, 2Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen, Thailand, 3Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom,*[Email: wirach@kku.ac.th](Email: wirach@kku.ac.th) Received: 01 April 2024 Accepted: 19 July 2024 DOI: [https://doi.org/10.32479/ijefi.16613](https://doi.org/10.32479/ijefi.16613) | ABSTRACT
This study demonstrates the effectiveness of advanced machine learning techniques in detecting fraudulent activities within the banking industry. We evaluated the performance of various models, including LightGBM, XGBoost, CatBoost, vote classifiers, and neural networks, on a comprehensive dataset of banking transactions. The CatBoost model exhibited the highest accuracy in identifying fraudulent instances, showcasing its superior performance. The application of diverse sampling and scaling techniques significantly improved fraud detection accuracy, emphasizing their crucial role in the process. Furthermore, the incorporation of the CatBoost ensemble method substantially enhanced the efficiency of fraud identification. Our findings underscore the potential of these advanced machine-learning approaches in mitigating financial losses and ensuring secure transactions, ultimately bolstering trust and security in the banking sector. Future research directions include refining the CatBoost model’s hyper parameters, adapting to evolving fraud patterns, and integrating real-time data for enhanced responsiveness. Additionally, efforts will be made to improve the interpretability of the model’s decision-making process, providing valuable insights into its trust-building capabilities and enhancing the transparency of fraud detection methodologies.
Keywords: Fraud Detection, Machine Learning, CatBoost, Banking Security, Ensemble Methods
JEL Classifications:

FAQs

Which machine learning models were evaluated for fraud detection?

The study evaluates LightGBM, XGBoost, CatBoost, vote classifiers, and neural networks for fraudulent activity detection.

Why does CatBoost perform best in this study?

CatBoost shows the highest accuracy in identifying fraudulent instances, and its ensemble approach further improves the efficiency of fraud identification.

What techniques help improve fraud detection accuracy?

Diverse sampling and scaling techniques significantly improve accuracy, highlighting their crucial role in the modeling process.

Aug 3, 2026

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