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.

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1. Liu, Y., Jiang, Z., Luo, C., & Chen, L. (2022). A Deep Learning-Based Model for Fraud Detection in Financial Transactions. IEEE Access, 10, 15521-15530.
2. Aggarwal, A., & Yadav, D. (2021). Fraud Detection in Financial Transactions Using Machine Learning and Deep Learning Techniques. International Journal of Scientific Research in Computer Science, Engineering, and Information Technology, 6(4), 49-54.
3. Feng, L., Liu, X., & Huang, G. (2020). Research on Financial Transaction Fraud Detection Algorithm Based on Artificial Intelligence. In 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE) (pp. 1-4). IEEE.
4. Huang, Y., Wang, X., & Wang, C. (2019). A Comparative Study of Machine Learning Algorithms for Financial Transaction Fraud Detection. In 2019 International Conference on Artificial Intelligence and Computer Engineering (AICE) (pp. 45-50). IEEE.
5. Lin, Z., Wang, L., & Zhang, W. (2018). Financial Transaction Fraud Detection Based on Machine Learning Methods. In 2018 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) (pp. 645-649). IEEE.
6. Gupta, A., & Nagar, S. (2023). Artificial Intelligence Techniques for Fraud Detection in Financial Transactions: A Review. Journal of Financial Crimes, 30(4), 897-914.
7. Smith, J., & Johnson, M. (2022). Advanced Machine Learning Algorithms for Real-Time Fraud Detection in Banking Transactions. International Journal of Banking, Risk and Fraud Prevention, 9(2), 45-58.
8. Chen, Y., Wang, H., & Liu, Z. (2021). Deep Learning-Based Fraud Detection System for Online Financial Transactions. IEEE Transactions on Dependable and Secure Computing, 18(5), 3156-3169.
9. Patel, R., & Shah, D. (2020). Ensemble Learning Approach for Fraud Detection in Financial Transactions using AI. Expert Systems with Applications, 158, 113508.
10. Li, X., Zhang, Y., & Zhang, J. (2019). Anomaly Detection in Financial Transactions Using Deep Autoencoder Neural Networks. Future Generation Computer Systems, 91, 187-197.
11. Kumar, A., & Sharma, S. (2018). Fraud Detection in Financial Transactions using Machine Learning Techniques. International Journal of Computer Applications, 178(2), 12-19.
12. Wang, Q., & Dong, Z. (2017). Real-Time Fraud Detection in Financial Transactions using Spark Streaming. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 3456-3465). IEEE.
13. Tan, J., & Liu, C. (2016). Fraud Detection in Online Financial Transactions using Neural Networks. Neurocomputing, 205, 350-359.
14. Gupta, R., & Jain, R. (2015). Hybrid Approach for Detecting Fraudulent Transactions in Financial Data using Genetic Algorithms and Neural Networks. Procedia Computer Science, 60, 1046-1055.
15. Zhang, L., & Li, Y. (2014). Improved Fraud Detection in Financial Transactions using Fuzzy Logic and Data Mining Techniques. Applied Soft Computing, 24, 1018-1027.

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 is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Liu, Y., Jiang, Z., Luo, C., & Chen, L. (2022). A Deep Learning-Based Model for Fraud Detection in Financial Transactions. IEEE Access, 10, 15521-15530.
2. Aggarwal, A., & Yadav, D. (2021). Fraud Detection in Financial Transactions Using Machine Learning and Deep Learning Techniques. International Journal of Scientific Research in Computer Science, Engineering, and Information Technology, 6(4), 49-54.
3. Feng, L., Liu, X., & Huang, G. (2020). Research on Financial Transaction Fraud Detection Algorithm Based on Artificial Intelligence. In 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE) (pp. 1-4). IEEE.
4. Huang, Y., Wang, X., & Wang, C. (2019). A Comparative Study of Machine Learning Algorithms for Financial Transaction Fraud Detection. In 2019 International Conference on Artificial Intelligence and Computer Engineering (AICE) (pp. 45-50). IEEE.
5. Lin, Z., Wang, L., & Zhang, W. (2018). Financial Transaction Fraud Detection Based on Machine Learning Methods. In 2018 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) (pp. 645-649). IEEE.
6. Gupta, A., & Nagar, S. (2023). Artificial Intelligence Techniques for Fraud Detection in Financial Transactions: A Review. Journal of Financial Crimes, 30(4), 897-914.
7. Smith, J., & Johnson, M. (2022). Advanced Machine Learning Algorithms for Real-Time Fraud Detection in Banking Transactions. International Journal of Banking, Risk and Fraud Prevention, 9(2), 45-58.
8. Chen, Y., Wang, H., & Liu, Z. (2021). Deep Learning-Based Fraud Detection System for Online Financial Transactions. IEEE Transactions on Dependable and Secure Computing, 18(5), 3156-3169.
9. Patel, R., & Shah, D. (2020). Ensemble Learning Approach for Fraud Detection in Financial Transactions using AI. Expert Systems with Applications, 158, 113508.
10. Li, X., Zhang, Y., & Zhang, J. (2019). Anomaly Detection in Financial Transactions Using Deep Autoencoder Neural Networks. Future Generation Computer Systems, 91, 187-197.
11. Kumar, A., & Sharma, S. (2018). Fraud Detection in Financial Transactions using Machine Learning Techniques. International Journal of Computer Applications, 178(2), 12-19.
12. Wang, Q., & Dong, Z. (2017). Real-Time Fraud Detection in Financial Transactions using Spark Streaming. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 3456-3465). IEEE.
13. Tan, J., & Liu, C. (2016). Fraud Detection in Online Financial Transactions using Neural Networks. Neurocomputing, 205, 350-359.
14. Gupta, R., & Jain, R. (2015). Hybrid Approach for Detecting Fraudulent Transactions in Financial Data using Genetic Algorithms and Neural Networks. Procedia Computer Science, 60, 1046-1055.
15. Zhang, L., & Li, Y. (2014). Improved Fraud Detection in Financial Transactions using Fuzzy Logic and Data Mining Techniques. Applied Soft Computing, 24, 1018-1027.