Fraud Detection in Malaysian Financial Institutions using Data Mining and Machine Learning

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Information and Learning Technology Pub Date : 2023-06-13 DOI:10.53819/81018102t4152
Shih T. Cho
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Abstract

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management
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使用数据挖掘和机器学习的马来西亚金融机构欺诈检测
金融机构欺诈威胁不断升级是一个全球性问题,马来西亚金融业也不例外。本研究的重点是数据挖掘和机器学习方法在识别和减轻这些机构内欺诈活动方面的实施和有效性。本文批判性地回顾了现有文献,弥合了先进技术应用与欺诈管理之间的差距。金融部门的欺诈交易是动态和复杂的,需要先进的检测技术。传统的方法往往难以有效地管理这种复杂性,这表明需要更先进的自适应策略。这就是以预测和分析能力而闻名的数据挖掘和机器学习技术可以做出重大贡献的地方。数据挖掘是在大型数据集中发现模式和相关性的过程,是检测可能暗示欺诈的异常的有用工具。本研究评估了各种数据挖掘技术,如聚类、分类和关联,并探讨了它们在检测欺诈交易中的应用。研究结果表明,这些技术可以大大提高欺诈检测率,同时最大限度地减少误报。此外,人工智能子集机器学习在欺诈检测方面显示出巨大的潜力。它从数据中学习并根据数据做出决策的能力使其成为欺诈检测的可行解决方案。本文探讨了监督和无监督学习算法及其在马来西亚金融部门识别欺诈行为的功效。结果表明,机器学习模型在正确实施的情况下,可以显著提高欺诈检测的准确性。该审查强调了采用数据挖掘和机器学习等先进技术有效打击金融欺诈的重要性。它还提出了未来的研究方向,强调需要考虑到马来西亚独特的社会经济环境的具体情况,本地化模型。此外,结合数据挖掘和机器学习的混合模型的发展可以提供更好的结果。总之,本研究为进一步探索马来西亚金融部门欺诈检测中先进分析工具的应用开创了先例。这些技术为提高欺诈检测系统的准确性和适应性提供了巨大的潜力,值得进行彻底的调查。关键词:欺诈检测,马来西亚金融机构,数据挖掘,机器学习,金融欺诈管理
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来源期刊
International Journal of Information and Learning Technology
International Journal of Information and Learning Technology COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.10
自引率
3.30%
发文量
33
期刊介绍: International Journal of Information and Learning Technology (IJILT) provides a forum for the sharing of the latest theories, applications, and services related to planning, developing, managing, using, and evaluating information technologies in administrative, academic, and library computing, as well as other educational technologies. Submissions can include research: -Illustrating and critiquing educational technologies -New uses of technology in education -Issue-or results-focused case studies detailing examples of technology applications in higher education -In-depth analyses of the latest theories, applications and services in the field The journal provides wide-ranging and independent coverage of the management, use and integration of information resources and learning technologies.
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