• Dr.A.Naresh
• Dr. P.Radhika
• M. Jyothi Sai Priya
• N. Abhigna
• K. Ashritha Sai , G. Venkata Sindhu ,Vasantha Triveni

Fraud has been rather pervasive and very costly in this interwoven world, posing a threat to the stability and reliability of various industries. With such a worrying surge of sophisticated fraud techniques and ever-evolving fraudulent patterns in behaviors, it goes without saying that some new, adaptive solution for fraud detection is in order. Artificial intelligence can prove really powerful in fighting fraud and can turn out to be very promising in raising the efficiency and accuracy of the detection systems. This paper presents new ways of dealing with fraud and introduces techniques of AI-powered fraud detection. It describes the current landscape of fraud detection approaches, covering traditional rule-based techniques and more recent methodologies, first based on statistical methods and then on machine learning techniques. It, therefore, outlines the limitations of such methods and emphasizes the need for AI-driven solutions to help overcome the potential barriers that dynamism in fraud poses. Here, it presents three AI-based techniques for fraud detection: Graph Neural Networks, Generative Adversarial Networks, and Temporal Convolutional Networks. All of them aim to make good use of the strengths found in various AI techniques.

Ensemble,Fraud detection,Temporal Convolution,Adversarial Networks,Digital Systems

"RULE-BASED SYSTEMS AND ENSEMBLE LEARNING TECHNIQUES FOR DIGITAL FRAUD DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.388-396, October-2024, Available :http://www.jetir.org/papers/JETIRGN06043.pdf

"RULE-BASED SYSTEMS AND ENSEMBLE LEARNING TECHNIQUES FOR DIGITAL FRAUD DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp388-396, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06043.pdf