THE IMPACT OF ARTIFICIAL INTELLIGENCE ON RISK ASSESSMENT AND FRAUD DETECTION
DOI:
https://doi.org/10.5281/zenodo.17068345Keywords:
Artificial Intelligence (AI), Risk Assessment, Fraud Detection, Machine Learning, Predictive Modelling, Audit Automation, Internal Controls, Forensic Accounting.Abstract
This thesis examines the transformative influence of Artificial Intelligence (AI) on risk assessment and fraud detection in both financial and non-financial sectors. It argues that AI has become a pivotal enabler of predictive analytics, anomaly detection, and strategic decision-making in risk management. Drawing on academic literature, case studies, and industry evidence, the study identifies how machine learning, natural language processing, and neural networks have enhanced fraud detection accuracy, reduced operational costs, and accelerated response times. However, it also recognises the ethical, regulatory, and technological challenges of adopting AI, such as algorithmic bias, the black-box problem, and data privacy risks. The research concludes that while AI improves efficiency, its success ultimately depends on human oversight, transparent governance, and alignment with regulatory frameworks.
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