AI-Driven Fraud Detection in Financial Systems: A CRM-Centric Approach

  • Authors

    • Satyendra Kumar Vanapalli Consultant, Technical Solutions at Visa Inc, USA. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022PII4A5X

    Published 11-08-2022

  • AI in Finance, Fraud Detection, CRM Systems, Machine Learning, Financial Security, Customer Analytics, Risk Management, Digital Banking

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    S. K. Vanapalli, “AI-Driven Fraud Detection in Financial Systems: A CRM-Centric Approach”, IJMLPA, vol. 5, no. 2, pp. 01–14, Nov. 2022, doi: 10.67228/3142788X/IJMLPA-2022PII4A5X.

  • Abstract

    Financial fraud has aggressively transformed along with the digital era, this change being largely driven by the growing reach of internet banking and mobile payments as well as cyber threats, which have been getting more advanced and which exploit not only technical weaknesses but also people's behavior. Old-fashioned detection systems that run on rules still do have their merits; however, they mostly cannot cope with the amount and intricacy of fraud patterns that we see nowadays. That is why we have seen the rise of artificial intelligence (AI), which is a very potent means of spotting irregularities, understanding how people behave, and facilitating instant decisions in fraud detection. On the other hand, Customer Relationship Management (CRM) systems have become indispensable to financial environments through the gathering of customer information, records of contacts, and insights on behavior. This paper looks at how these two fields overlap and points out ways in which the blending of AI-based analytics in CRM systems can lead to a remarkable rise in proactive fraud prevention. Using customer-focused data, such as their record of purchases, modes of communication, and levels of engagement, AI programs are capable of spotting very subtle changes that could signal an attempt at fraud and at the same time, they are able to significantly reduce the number of false alarms. The technique suggested here describes a system for the detection of fraud, which is both dynamic and adaptable and which is obtained through the synthesis of machine learning techniques and the CRM data streams. Through a case-based study, it is shown how this unified method can lead to an increase in detection performance, shortening of the time for the response, and higher customer confidence in comparison to the existing systems.

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