Compensating for the Risks and Weaknesses of AI/ML Models in Finance

Artificial Intelligence (AI) is transforming financial risk management by enhancing predictive accuracy, automating processes, and mitigating risks. This paper explores the challenges such as ethical concerns, data privacy, and systemic risks. Drawing on recent literature, we analyze the benefits and limitations of AI adoption in finance and propose recommendations for future research and policy frameworks. This paper explores the applications, benefits, risks, and ethical considerations associated with AI in finance. The findings highlight the potential of AI to enhance efficiency while underscoring challenges related to systemic risks, data privacy, and governance. We delve into the benefits of AI, including improved accuracy, automation, and real-time insights, while also addressing the inherent risks and ethical considerations, such as algorithmic bias, data privacy, and systemic risk. Furthermore, we discuss the evolving regulatory landscape and the challenges financial institutions face in effectively managing AI-related risks. Through a systematic review of academic literature, industry reports, and regulatory documents, we identify three core dimensions of AI's impact: (1) operational enhancements including 15-40\% improvements in risk detection and \$1.2B annual fraud prevention savings; (2) systemic risks such as 20\% increased market volatility from model homogeneity; and (3) ethical concerns including 30\% bias rates in credit scoring models. The study develops a lifecycle risk framework spanning development (data biases, adversarial vulnerabilities), deployment (compliance failures, overreliance), and monitoring phases (model drift, cybersecurity threats). We propose a tripartite control matrix—remedial (algorithmic audits, human oversight), curative (explainable AI, diverse data sourcing), and compensative (insurance products, hybrid systems)—to address these challenges. The analysis reveals significant research gaps, including longitudinal performance studies (absent in 80\% of literature) and quantum AI integration (addressed by only 2 papers). Regulatory fragmentation between EU and US approaches emerges as a key governance challenge. The paper concludes with actionable recommendations for financial institutions, including continuous model auditing protocols, stress-testing standards for AI systems, and ethical AI certification frameworks. These findings contribute to both academic discourse and industry practice by providing evidence-based strategies for responsible AI adoption in finance.