Harnessing Big Data with AI-Driven BI Systems for Real-Time Fraud Detection in the U.S. Banking Sector - Ghimire, Ashok (BULLET : Jurnal Multidisiplin Ilmu, 2024)
AI BI systems with integration to Big Data is going to help change the face of the detect fraud for banking sector in United States. This paper examines how these technologies make it possible to detect fraudulent activities in real-time: the novel being that mega yards of transactional data may have to be ingested and analyzed in near real-time to make way for machine learning, predictive models, and/or AI. Banks are on the receiving end of those more advanced techniques and with the use of AI and Big Data there is capacity to analyze of those fraud patterns, improve accuracy and eventually diminish the made losses. However, the actual application of these systems has its drawbacks: concerns for data protection, having algorithms with certain biases, a history of the corresponding system being meddled with and needing to be updated. The present work aims to consider a few examples of applying the AI solutions in practice to investigate actual and pilot cases of frauds in the big US banks, such as JPMorgan Chase, Bank of America, and Wells Forgot. It also includes an emergence of fraud detection systems which in form of block chain technology, enhanced biometric science, quantum technology and shared fraud detection platform. However, all these technologies are seen to offer a great potential for enhancing the security level of the banking sector, especially as regards the prevention of fraud activities in the field. These are the goal posts which financial institutions have to clear while adopting change, controlling frauds to combat new techniques in an environment that moves towards an online financial services consumer’s environment.