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Dr. Shaista Parveen is an Assistant Professor in the Department of Computer Science and Engineering at Guru Nanak Dev Engineering College, India. Her research focuses on Artificial Intelligence, Machine Learning, and intelligent computing applications.
The swift digital transformation of financial services, cloud technology, Internet of Things (IoT), and e-commerce platforms has markedly heightened the intricacy and magnitude of financial risks and security vulnerabilities. Conventional risk assessment techniques, mostly reliant on human evaluation and fixed rule-based frameworks, frequently unable to detect evolving threats and fluid risk patterns instantaneously. Artificial Intelligence (AI) has surfaced as a revolutionary technology that improves risk evaluation and mitigation via predictive analytics, anomaly identification, behavioural modelling, and automated decision processes. AI-powered systems employ Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Big Data Analytics to assess financial risks, forecast fraudulent behaviours, and execute preemptive prevention measures. This chapter offers an extensive examination of AI applications in risk evaluation and mitigation, addressing key methodologies, frameworks, obstacles, and prospective research avenues within cloud-based IoT and e-commerce environments.
Artificial Intelligence (AI) has emerged as a fundamental element of contemporary financial frameworks, facilitating astute decision-making in banking, insurance, investment management, credit evaluation, fraud detection, anti-money laundering (AML), and risk analysis. Although sophisticated Machine Learning (ML) and Deep Learning (DL) models attain exceptional predicted precision, numerous work as "black-box" mechanisms, complicating stakeholders' comprehension of the rationale behind their choices. This absence of transparency generates apprehensions about trust, accountability, equity, adherence to regulations, and the ethical implementation of AI. Explainable Artificial Intelligence (XAI) tackles these issues by offering clear, transparent, and comprehensible elucidations for decisions made by AI. XAI bolsters user trust, aids in regulatory adherence, enhances model verification, and promotes ethical AI use within financial organisations. This chapter elucidates the principles, framework, methodologies, applications, obstacles, and prospective research avenues of Explainable AI within cloud-enabled, IoT-driven, and digital financial environments.