This paper explores the development and practical application of a strategic fraud risk mitigation framework tailored for corporate finance, emphasizing its integration with cost optimization strategies. As organizations face increasing fraud risks, including financial statement and procurement fraud, the need for comprehensive, proactive measures to safeguard financial integrity becomes critical. The proposed framework combines advanced technologies such as AI, machine learning, and data analytics with traditional risk management methods to identify, assess, and prevent fraud while enhancing operational efficiency and reducing costs. Through real-world case studies, including examples from Siemens, JPMorgan Chase, and Target, the paper demonstrates how such frameworks have been successfully implemented to improve financial performance and organizational resilience. The paper also outlines a structured implementation strategy, addressing challenges such as organizational resistance, technological integration, and resource constraints. In conclusion, the research emphasizes the broader implications for corporate finance, highlighting the potential for enhanced financial integrity, cost reduction, and improved investor confidence. The paper also suggests avenues for future research, particularly in adapting the framework to the evolving landscape of digital finance and cybersecurity threats.