Fraud Detection System for Identity Crime using Blockchain Technology and Data Mining Algorithms - Shakadwipi, Amol Jagdish; Jain, Dinesh Chandra; Nagini, S. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Identity crime continues to pose a significant threat in today's digital landscape, necessitating the development of highly effective fraud detection systems. This paper presents a novel and innovative approach that combines the power of blockchain technology with advanced data mining techniques to create a robust fraud detection system specifically designed to combat identity crime. By seamlessly integrating blockchain and data mining, the proposed system demonstrates exceptional capabilities in detecting and preventing fraudulent activities in real-time.The integration of blockchain technology ensures the utmost security and immutability of data by leveraging its decentralized nature. This formidable security feature makes it exceedingly challenging for malicious individuals to manipulate or tamper with personal information. Leveraging blockchain's inherent strengths, the system efficiently verifies user identities and continuously tracks any alterations made to the data, thereby significantly enhancing the accuracy and reliability of identity verification processes.Data mining techniques play a pivotal role in detecting and combating fraud by enabling the analysis of vast volumes of data. Through the implementation of sophisticated data mining algorithms, the system effectively identifies patterns and anomalies associated with fraudulent behavior. This proactive approach empowers the system to swiftly detect suspicious activities and accurately predict potential fraud attempts. By doing so, the system effectively prevents identity crimes at their early stages, effectively reducing financial losses and providing vital protection for individuals' identities. The proposed fraud detection system operates seamlessly in real-time, constantly monitoring user transactions and activities. Any indication of suspicious behavior immediately triggers alerts, facilitating prompt actions to mitigate the impact of fraudulent activities. Furthermore, the system harnesses the power of data mining techniques to analyze comprehensive historical data, thereby enabling the identification of intricate trends and patterns that serve as strong indicators of fraudulent activity. This refined analytical capability significantly enhances the system's overall accuracy and effectiveness.