Enhancing Fraud Detection in Real Time using DataOps on Elastic Platforms - Ankur Mahida; Pradeep Chintale; Hrushikesh Deshmukh (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Fraud detection has gained prominence as a measure of enhancing security and management of various organization’s networks. Moreover, having real time fraud detection enhances on-site handling of fraudulent activities and channels that could otherwise result in negative outcomes for companies. Real-time fraud detection can be conducted through DataOps practices, which enhance the capacity of data to have reliable information, and conducted on Elastic platforms which scale the amount of data to be provided and remarkable channel to achieve sustainable management of the data. Fraud detection on elastic platforms like elastic search have a capacity to detect real-time information through predetermined standards and approaches that provide alerts in case of any communication approach. Nonetheless, the fraud detection approach works with meaningful engagement to ensure sustainable compliance to privacy regulations such as anonymization, which ensures that personally identifiable data is not used for the wrong purpose. Hence, the act of fraud detection requires instrumental data handling mechanisms and appeal to scalability and flexibility of elastic platforms to achieve a scalable operation.