Big Data Analytics in Banking Risk Management: AI-Powered Decision Support Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P104Keywords:
Big Data Analytics, AI-Powered Decision Support Systems, Banking Risk Management, Fraud Detection, Credit Risk Assessment, Machine Learning, Deep Learning, Regulatory Compliance, Explainable AI, Federated LearningAbstract
Technology revolutions such as Big Data Analytics and Artificial Intelligence have prevailed in risk management in the banking sector. AI technologies are widely adopted in financial institutions for Decision Support Systems (DSS) to improve the operational decision process, assess risks, detect fraud, and comply with regulations. The application of machine learning, deep learning, and real-time analytics have decisively helped in the announcement analysis of structured and unstructured data and, therefore, improved the decision-making and effectiveness of banks. The sources of Big Data in banking include transactions, credit scores, social media, and markets, among others. Bright data is then used for various purposes using AI algorithms, including checking for fraudulent activities, credit risk assessment, compliance with financial rules such as anti-money laundering, and knowing your customer. Also, AI-based DSSs play a role in credit decisions, portfolio operations, and improving the security system. However, its use brings about the following challenges: Data privacy, ethical concerns that arise when it comes to decision-making by using such technology, and AI models that need to be explained, especially to clients. In addition, the ever-changing regulatory policies require the development of better governance techniques for AI. Explainable AI (XAI), Federated Learning, and Quantum Computing come out as the next characteristics of banking risk analytics since it is essential to have improved explainability and security, as well as the integration of learners that allow for distributed learning. This paper aims to discuss the role of big data analytics in risk management within the banking sector with a focus on the decision support systems enabled by Artificial Intelligence, key advantages, and restrictions of utilizing the latter, as well as future research areas in the field
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