AI-Driven Fraud Detection in Digital Banking: Ml Approach for Secure and Transparent Financial Transactions
DOI:
https://doi.org/10.54536/ajfti.v3i1.5168Keywords:
Artificial Intelligence (AI), Digital Banking, Fraud Detection, Machine Learning (ML)Abstract
The convenience of digital banking services has transformed the global financial industry and is now available to consumers all over the world. As with any advancement, there’s an increase in associated risk. In this case, we have an upsurge in fraudulent activity, the mobility of cybercriminals, and their more advanced technologies to breach vulnerabilities within digital infrastructures. Indeed, financial crimes are constantly evolving like the rest of technology and society. Those who monitor and manually analyse systems are no match for the speed at which criminals can devise new rule-of-thumb schemes. This article examines how artificial intelligence and machine learning can reform the detection of fraud within digital banking systems. The research analyses different techniques of AI and ML, supervised learning, unsupervised learning, ensemble, and deep learning approaches, while also observing their uses in practical fraud detection systems. The paper also analyses the ethical and legal concerns involving the use of AI within banking, considering data issues, algorithmic discrimination, and other contentious aspects of legal compliance, including quasi-legal frameworks like GDPR and PCI-DSS. It also explores some of the newer directions in AI, like quantum computing, explainable AI (XAI), and federated learning, and their potential implications to improving fraud detection systems performance. Finally, the focus of this paper has been on a continuing effort and partnership across sectors in building resilient, secure, transparent financial systems. AI, ML, and blockchain technologies enhance the capability to prevent fraud in digital banking, while ensuring and maintaining customer trust and security in financial transactions.
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