International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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The Role of Ai and Machine Learning in Financial Fraud Detection

Author(s) Ms. Rupali Yadav Yadav, Mr. Ranvijay Maurya
Country India
Abstract This research deeply examines novel methods for fighting financial fraud with an emphasis on the efficiency of Machine Learning (ML) and Artificial Intelligence (AI). Considering the limitations of traditional methods, the analysis intends to evaluate the existing state of affairs, thoroughly studying the effectiveness and shortcomings of ML and AI methods while tracing out complex lines of future inquiry. We explore the complex history of financial fraud, revealing the inherent limitations inherent in traditional rule-based and manual detection methods. Machine learning (ML) and artificial intelligence (AI) are then presented, emphasizing key research and successful applications that have revolutionized the area of fraud detection. In examining the evaluation metrics, we employ different measures including accuracy, precision, recall, F1 score, and the mysterious ROC-AUC. Then, various ML and AI algorithms are presented, ranging from enigmatic Random Forest, to stalwart Support Vector Machines (SVM), and to convoluted neural networks
The increasing digitalization of financial services has led to a rise in fraudulent activities, posing significant challenges to banks, fintech companies, and regulators. Traditional fraud detection systems, typically rule-based, struggle to detect emerging and sophisticated fraud techniques, resulting in delays and high false positives. This research investigates how Artificial Intelligence (AI) and Machine Learning (ML) technologies, through advanced data analytics, can enhance the accuracy and speed of fraud detection systems. By leveraging real-time data processing, anomaly detection, and predictive models, this study aims to demonstrate the efficacy of AI in preventing financial fraud, minimizing risks, and ensuring regulatory compliance in the banking and fintech sectors.
This research explores the role of AI and ML in financial fraud detection, assessing how these technologies improve efficiency, accuracy, and scalability in identifying fraudulent activities. The study examines various AI-driven approaches, including supervised and unsupervised learning models, deep learning techniques, and anomaly detection methods
Through case studies of leading financial institutions, the research demonstrates how AI-based fraud detection systems can reduce false positives, enhance real-time monitoring, and adapt to evolving fraud tactics. The study further evaluates model performance using accuracy, precision, recall, and F1-score metrics. The findings aim to provide insights into the effectiveness of AI in financial fraud prevention, offering recommendations for banks and fintech companies to strengthen their fraud detection frameworks and minimize risks in an increasingly digital financial ecosystem.
Keywords Financial fraud, Machine Learning, Artificial Intelligence, Fraud detection, Supervised learning, Unsupervised learning, Algorithmic approaches.
Field Business Administration
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-01
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.46023
Short DOI https://doi.org/g9m2f5
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