International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 07 | July 2024
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p-ISSN: 2395-0072
Anomaly Detection at the Intersection of Retail, Finance, and Compliance Bhupendrasinh Thakre Walmart, USA ----------------------------------------------------------------------------***-----------------------------------------------------------------------
Abstract: Finding anomalies in fraud cases is very hard because the data is so complicated and is often locked away behind strict access controls. Compliance and financial limits make things even more difficult. This study looks into how hard it is to do anomaly detection in places that are so tightly controlled. It focuses on the retail sector because following the rules is not only very important but also carries heavy penalties. Not much has been written about finding strange things at the intersection of law, finance, and technology, so this study looks at new ways to do it using data mining, machine learning, and artificial intelligence. These technologies are built into complex compliance frameworks that make it easier to look at financial activities. This paper comes up with a new way to find and deal with problems in high-stakes situations that take into account the difficulties of limited data access, following the rules, and the financial details of trades. The results add a new angle to what is already known and make it possible to create fraud detection systems that are more strong and flexible in industries with a lot of regulations.
Keywords: Anomaly detection, Retail fraud, Compliance framework, Machine learning, Financial transactions 1. Introduction Fraud and other strange behavior can be hard to spot in the retail sector because of the complicated interactions between money transfers, following the rules, and limited access to data [1]. Fraud cost the global retail industry an estimated $62 billion in 2020 alone [2]. Online payment fraud was responsible for an amazing 45% of these losses. In these kinds of situations, traditional methods for finding anomalies don't always work, so new methods need to be created that can get around these extra problems [3].
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