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Dr. Prachee Sharma is an Assistant Professor in the Faculty of Arts and Humanities at The ICFAI University Raipur, Kumhari, Durg, Chhattisgarh, India. Her academic role is associated with teaching and higher education. Her contribution to this volume focuses on AI-based 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.