The increasing complexity of financial markets has necessitated the adoption of advanced risk assessment models to enhance predictive accuracy and mitigate fraudulent activities. Traditional risk assessment frameworks, which rely on historical financial data and rule-based algorithms, often struggle to adapt to evolving market conditions and emerging threats. In response, artificial intelligence (AI)-driven risk assessment models have gained prominence, leveraging machine learning and deep learning techniques to analyze vast datasets, detect patterns, and improve decision-making processes. These models offer superior predictive capabilities by integrating alternative data sources, real-time market trends, and behavioral analytics to assess financial risk with greater precision. Despite their advantages, AI-driven risk assessment models present several challenges, including algorithmic bias, model interpretability, and regulatory compliance. Bias in training data can lead to unfair credit allocations and systemic risks, necessitating robust bias mitigation strategies such as fairness-aware machine learning and adversarial debiasing. Additionally, the opacity of black-box AI models raises concerns about transparency and accountability, prompting the need for Explainable AI (XAI) frameworks to enhance interpretability and regulatory adherence. Moreover, AI has revolutionized fraud detection by identifying anomalies in financial transactions, leveraging predictive analytics to detect suspicious activities before they escalate. Financial institutions must balance innovation with ethical considerations, ensuring responsible AI deployment in risk assessment. This paper explores the evolution of AI-driven risk assessment models, their impact on predictive accuracy and fraud detection, and strategies to mitigate inherent challenges. Future research should focus on improving model transparency, refining bias reduction techniques, and strengthening regulatory oversight to ensure fairness and reliability in AI-driven financial risk assessments.