Fraudulent activities in bank reconciliation and financial transactions pose significant threats to the integrity and operational efficiency of financial institutions. Existing methods for detecting and preventing fraud often rely on reactive approaches, leaving organizations vulnerable to sophisticated schemes. This study proposes an AI-Enhanced Fraud Detection and Prevention Model (AI-FDPM), a real-time framework designed to monitor, detect, and prevent financial irregularities in bank reconciliation and transaction oversight processes. By leveraging advanced artificial intelligence techniques, including machine learning (ML), natural language processing (NLP), and anomaly detection algorithms, the model ensures heightened accuracy and responsiveness in identifying fraudulent activities. The AI-FDPM employs a layered architecture that integrates data preprocessing, pattern recognition, and predictive analytics to identify anomalies in transaction data. Machine learning algorithms are trained on historical transaction datasets to recognize fraudulent patterns while continuously adapting to emerging threats. Additionally, the framework incorporates NLP for processing unstructured financial data, enabling the detection of inconsistencies in transaction narratives and supporting documentation. Real-time monitoring and alert systems further enhance the model's capabilities by providing proactive fraud prevention measures. Key findings demonstrate that the AI-FDPM significantly reduces financial discrepancies and improves reconciliation accuracy by up to 85%, while enabling timely intervention in high-risk scenarios. The model also supports scalability and adaptability, allowing financial institutions to handle increasing transaction volumes without compromising oversight quality. A case study analysis highlights successful implementations in mitigating fraud within banking systems, emphasizing the model's effectiveness and cost-efficiency. This research provides a transformative approach to fraud detection and prevention, addressing critical gaps in traditional methods. The AI-FDPM framework offers financial institutions a robust, scalable, and intelligent solution to enhance financial security and operational reliability. Policymakers, financial analysts, and technology developers will find this model instrumental in advancing fraud management strategies in the evolving financial landscape.