Applications of Artificial Intelligence in Financial Risk Assessment and Fraud Prevention تطبيقات الذكاء الاصطناعي في تقييم المخاطر المالية ومنع الاحتيال | ||||
| منارة الاسكندرية للعلوم التجارية | ||||
| Volume 1, Issue 2, July 2025, Page 224-212 PDF (624.79 K) | ||||
| Document Type: المقالات العلمية | ||||
| DOI: 10.21608/mauta.2025.401785.1005 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Hatem A Khater | ||||
| Computer and Systems Engineering Department, Faculty of Engineering, Horus University Egypt, New Damietta | ||||
| Abstract | ||||
| This study investigates how artificial intelligence (AI) can improve financial risk assessment and fraud prevention. Traditional rule-based systems are failing to identify complex fraud patterns and manage dynamic risk profiles as a result of the growing volume and complexity of financial transactions. Machine learning (ML), deep learning (DL), and natural language processing (NLP) are three examples of AI technologies that provide scalable, real-time solutions that can analyze large datasets, spot anomalies, and generate precise predictions. The study examines recent approaches, case studies, and uses of AI in financial institutions, such as insider trading detection, credit scoring, and anti-money laundering. It also draws attention to the expanding application of explainable AI (XAI) to resolve issues with regulatory compliance and model transparency. Even with the notable gains in accuracy and efficiency, issues with algorithmic bias, data privacy, and ethical responsibility still exist. The study comes to the conclusion that although AI has the potential to completely transform financial systems, its effective integration necessitates strong governance frameworks, interdisciplinary cooperation, and ongoing innovation to guarantee security, fairness, and confidence. | ||||
| Keywords | ||||
| Artificial Intelligence; Financial; Risk Management; Fraud Detection | ||||
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