Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{199777,
        author = {Pranay Mukesh Katare and Prof. Pratiksha Meshram},
        title = {AI-Enabled Fraud Detection and Its Impact on Operational Efficiency of Indian Banks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1824-1836},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199777},
        abstract = {The Indian banking industry's quick digital transformation, driven by the expansion of online banking services, Interface for Unified Payments (UPI), mobile wallets, and instantaneous payment systems, has raised the volume considerably, speed, and intricacy of financial exchanges. While this transformation has improved operational efficiency, customer convenience, and financial inclusion, it has also intensified the risk and incidence of banking fraud. Conventional fraud detection systems, largely according to rule-based mechanisms as well as manual verification, have shown limited capability in identifying complex and evolving fraud patterns, emphasizing the need for more sophisticated and flexible solutions. This review paper examines Artificial Intelligence's (AI) function–enabled fraud detection systems in the Indian banking sector and assesses their impact on operational efficiency. It reviews prevailing fraud trends, discusses limitations of traditional fraud detection approaches, and analyzes the application of AI techniques such as supervised and unsupervised machine learning, natural language interpretation, behavioural analytics, and monitoring of transactions in real time. The paper also explores the adoption of AI across Indian banks in both the public and private sectors, the role of fintech partnerships, and regulatory initiatives undertaken through the Reserve Bank of India to promote secure and ethical AI deployment. The review finds that AI-enabled fraud detection enhances operational efficiency by reducing fraud losses, improving detection accuracy and speed, lowering operational costs, and improving customer experience. However, challenges related to data quality, legacy system integration, implementation costs, data privacy, ethical concerns, and skill gaps continue to affect large-scale adoption. The paper concludes by highlighting future trends and research directions in AI-driven fraud detection.},
        keywords = {},
        month = {May},
        }