International Journal For Multidisciplinary Research

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

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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AI-Powered Fraud Detection Through CRM Platforms in Financial Services

Author(s) Geetha Krishna Sangam
Country United States
Abstract The rise of AI technologies in financial services has introduced powerful capabilities for fraud detection, especially when integrated with CRM platforms such as Salesforce. This paper explores how CRM data, combined with AI-driven analytics, enables real-time identification and mitigation of fraudulent activity. By examining architectural frameworks, machine learning models, and integration best practices, this study presents a comprehensive guide to leveraging CRM platforms for fraud intelligence. Emphasis is placed on risk signals, behavioral patterns, and customer lifecycle data as foundational inputs for fraud algorithms.
Fraud remains one of the most persistent and costly challenges in the financial services industry, driven by increasing digital transactions, omnichannel customer interactions, and sophisticated attack vectors. Traditional rule-based fraud detection systems are often reactive, siloed, and unable to adapt to evolving fraud patterns in real time. This paper explores the role of Artificial Intelligence (AI)–powered fraud detection when embedded directly within Customer Relationship Management (CRM) platforms. By leveraging machine learning, behavioral analytics, and real-time data orchestration, CRM-centric fraud detection enables proactive risk identification, contextual decision-making, and seamless operational response. The study presents architectural patterns, AI models, integration strategies, and governance considerations, demonstrating how CRM platforms can evolve into intelligent fraud prevention hubs within modern financial ecosystems.
Keywords AI, fraud detection, CRM, data analytics, Salesforce, anomaly detection, AI-Driven Fraud Detection, CRM Platforms, Financial Services, Machine Learning, Behavioral Analytics, Salesforce, Risk Management, Financial Crime.
Field Engineering
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-17
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.66318
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E-ISSN 2582-2160
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