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Gopinathan Rathinavelu is a Data Architect Professional specialising in data architecture within the banking and financial services sector in Chandler, Arizona, USA. He has over 20 years of experience in AI, machine learning, data modelling, data governance, data quality, and modern data architecture, as detailed in his editor biography. His contribution addresses AI applications in financial forecasting, fraud detection, and risk management.
Artificial intelligence (AI) is revolutionising the financial industry through enhanced forecasting, sophisticated fraud detection, automated risk evaluation, and analytics-based decision-making. The growing accessibility of financial transactions, market information, consumer profiles, digital payment histories, and economic metrics has enabled machine learning and deep learning models to discern intricate patterns that conventional statistical methods could overlook. This section explores the principles, structures, algorithms, applications, and assessment techniques related to AI-driven financial intelligence. It examines traditional machine learning, recurrent neural networks, transformers, graph neural networks, anomaly detection, and explainable AI methodologies. It further investigates issues associated with skewed datasets, model degradation, data confidentiality, adversarial threats, equity, interpretability, and regulatory oversight. A cohesive structure that amalgamates forecasting, fraud analysis, and risk evaluation is suggested to enhance dependable financial decision-making. The section underscores that AI solutions need to enhance existing financial controls, human proficiency, and transparent governance instead of supplanting them without sufficient validation.