Fraud Detection in IoT-Based Financial Transactions Using Anomaly Detection Techniques

In the course of this exploration study, the operation of anomaly-finding methods for fraud discovery in IoT-based financial transactions is investigated. As a result of the increasing prevalence of Internet of Things (IoT) bias in the financial services industry, there is an urgent requirement to establish effective procedures to decry fraudulent conditioning. The purpose of this study is to uncover unusual patterns and suspicious acts in the financial data generated by the Internet of Things (IoT) by utilizing anomaly discovery techniques. In this investigation, many methods of anomaly discovery are investigated. These methods include statistical styles, machine literacy algorithms, and deep literacy models. The goal of this investigation is to successfully differentiate between fraudulent trades and legitimate bones. The purpose of this study is to analyze the performance and efficacy of various methods in directly detecting fraudulent conditioning in IoT-founded financial transactions. This evaluation is accomplished through empirical analysis and testing. The results of this study contribute to the advancement of fraud discovery capabilities in Internet of Things (IoT) enabled fiscal systems, as well as to the enhancement of security and the mitigation of fiscal hazards.