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Dr. P. Viswanathan is an Assistant Professor in the Department of Commerce at Pachaiyappa's College for Men, India. His research interests include commerce, financial management, digital banking, and business analytics.
The swift growth of digital banking, mobile payment systems, cloud computing, and e-commerce platforms has markedly elevated the quantity and intricacy of financial transactions. While these innovations have enhanced efficiency and accessibility, they have simultaneously created new avenues for financial fraud, cyberattacks, money laundering, and unauthorised transactions. Conventional rule-based fraud detection systems frequently struggle to recognise new and complex attack patterns because they cannot adjust to changing threats. Anomaly Detection has become an essential method for recognising atypical patterns and dubious behaviours in banking and payment systems. Utilising Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data Analytics, anomaly detection systems can recognise irregularities in standard transaction patterns and offer immediate fraud prevention solutions. This chapter examines the fundamentals, methodologies, frameworks, applications, obstacles, and prospective advancements of anomaly detection within contemporary banking and payment systems.
The swift advancement of digital technologies, cloud computing, the Internet of Things (IoT), artificial intelligence, and fintech has profoundly altered the worldwide financial environment. Nonetheless, these technological innovations have also elevated the complexity and prevalence of financial offences, encompassing fraud, money laundering, cyber-enabled schemes, identity theft, and crimes associated with cryptocurrency. Conventional fraud prevention strategies are progressively insufficient in tackling new threats because of their restricted flexibility and dependence on fixed regulations. Intelligent Financial Crime Prevention (IFCP) has arisen as a multifaceted strategy that combines Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Blockchain, Explainable AI, and Federated Learning to preemptively detect and alleviate financial threats. This chapter examines contemporary trends, innovations, and prospective trajectories in intelligent financial crime prevention systems, emphasising their significance in safeguarding cloud-based IoT and e-commerce environments.
The stock market represents a highly dynamic and intricate financial system, wherein investment choices are influenced by a multitude of economic, technical, political, and behavioural elements. Conventional forecasting techniques reliant on statistical evaluation and past patterns frequently fail to account for the nonlinear and erratic characteristics of financial markets. The advent of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Internet of Things (IoT), Cloud Computing, and Explainable Artificial Intelligence (XAI) has greatly enhanced the capacity to forecast stock market movements, assess financial risks, and facilitate informed investment choices. AI-powered predictive models evaluate past stock prices, trade volumes, technical metrics, macroeconomic factors, financial news, and investor sentiment to produce precise market forecasts. This chapter delineates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues related to AI-driven stock market trend forecasting and financial prediction.