Exploring Machine Learning Fraud Detection Solutions for Financial Transactions

Abstract:

Financial fraud remains a persistent challenge across various domains, particularly in public-sector financial operations, threatening the integrity and transparency of financial statements while eroding public trust. This highlights the need for continued advancement of fraud detection mechanisms to keep up with the ever-evolving fraud tactics. ML algorithms have proven to be one of the most successful methods for analysing large financial datasets to detect fraudulent patterns. This paper reviews the application of ML to detect fraud in financial transactions using ML-based algorithms, namely K-means, Support Vector Machine, Decision Trees, Naive Bayes, and Deep Learning, in fraud detection, analysing their use cases and effectiveness as reported in the literature. Additionally, the study experimentally compares the performance of a Convolutional Neural Network (CNN) model against a Logistic Regression model, with the CNN achieving an impressive 90% accuracy, outperforming Logistic Regression in fraud detection. The paper further investigates the financial features and indicators most relevant to fraud detection and explores the challenges and opportunities posed by large volumes of financial transactions. By addressing these areas, the study aims to provide insights into enhancing fraud detection mechanisms and strengthening the security and integrity of financial transactions in today's digital ecosystem, including government institutions.

• Periodical: Engineering Headway (Volume 28)
• Pages: 169-177
• DOI: https://doi.org/10.4028/p-887ExH
• Online since: November 2025
• Authors: Johannes P. Paavo*, Rafael Rodríguez-Puentes, Richard Maliwatu
• Keywords: Financial Fraud, Financial Transactions, Fraud Detection, Machine Learning, Model

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