AI-Based Fraud Detection in Financial Transactions

Year : 2024 | Volume :10 | Issue : 01 | Page : 1-9
By

Pranjul Mehrotra

Bramah Hazela

Kamlesh Kumar Singh

Deependra Pandey

  1. Student Department of Computer Science Engineering,Amity School of Engineering & Technology, Lucknow, Amity University Uttar Pradesh India
  2. Assistant Professor Department of Computer Science Engineering, Amity School of Engineering & Technology, Lucknow, Amity University Uttar Pradesh India
  3. Assistant Professor Department of Electronics and Communication, Amity School of Engineering & Technology, Lucknow, Amity University Uttar Pradesh India
  4. Assistant Professor Department of Electronics and Communication, Amity School of Engineering & Technology, Lucknow, Amity University Uttar Pradesh India

Abstract

Financial fraud continues to pose a severe danger to individuals, businesses, and the global economy, necessitating the development of effective and innovative detection and prevention approaches. This research study examines the use of artificial intelligence (AI) techniques to detect fraudulent conduct in financial transactions. This paper investigates the advancements, challenges, and courses of AI-based fraud detection in financial transactions through a thorough examination of the research, case studies, and practical implementations. This research study seeks to provide readers with a thorough understanding of the role artificial intelligence (AI) plays in avoiding financial fraud, as well as key takeaways and recommendations for improving fraud detection systems. The method is based on a thorough investigation of peer-reviewed articles, industry reports, and case studies on AI-based fraud detection in financial transactions. This research aims to give a comprehensive overview of the present status of AI-based fraud detection, as well as insights into its efficacy, challenges, and potential future directions, by synthesizing and evaluating information from diverse sources. The study’s main findings on AI-based fraud detection indicate that there is a great deal of potential for enhancing the effectiveness and efficiency of programs for detecting financial crime. Various AI approaches, including supervised learning, unsupervised learning, and deep learning, have been successfully used in transactional data analysis to detect fraudulent patterns with high accuracy. As evidenced by case studies and practical applications, AI-based fraud detection systems can detect a wide variety of fraudulent activities, including identity theft, credit card fraud, and money laundering. However, the study also highlights several challenges and limitations associated with AI-based fraud detection. These include problems with data privacy, model interpretability, and algorithmic bias. When utilizing AI for fraud detection, a few ethical considerations that need to be carefully examined include transparency, fairness, and accountability. Additionally, the scalability and flexibility of AI-based fraud detection systems to evolve fraud strategies and legal limits present ongoing challenges for lawmakers and financial institutions. This study report has two implications. First, it provides financial institutions and lawmakers wishing to enhance their fraud detection abilities through AI technology with informative data. By being informed on the benefits, drawbacks, and moral ramifications of AI-based fraud detection systems, stakeholders may make well-informed decisions regarding their adoption and use. Second, it highlights how important it is to continue studying and collaborating to enhance AI-based fraud detection techniques, address important problems, and promote the moral and responsible use of AI in financial transactions.

Keywords: Financial fraud, AI techniques, detection, prevention, advancements, challenges, etc.

[This article belongs to International Journal of Embedded Systems and Emerging Technologies(ijeset)]

How to cite this article: Pranjul Mehrotra, Bramah Hazela, Kamlesh Kumar Singh, Deependra Pandey. AI-Based Fraud Detection in Financial Transactions. International Journal of Embedded Systems and Emerging Technologies. 2024; 10(01):1-9.
How to cite this URL: Pranjul Mehrotra, Bramah Hazela, Kamlesh Kumar Singh, Deependra Pandey. AI-Based Fraud Detection in Financial Transactions. International Journal of Embedded Systems and Emerging Technologies. 2024; 10(01):1-9. Available from: https://journals.stmjournals.com/ijeset/article=2024/view=146044

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Regular Issue Subscription Review Article
Volume 10
Issue 01
Received April 29, 2024
Accepted May 9, 2024
Published May 22, 2024
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