Artificial Intelligence (AI) is rapidly transforming risk management practices in the banking and financial services sector by enabling predictive analytics, real-time monitoring, fraud detection, and intelligent decision-support systems. Traditional risk management frameworks in banking relied heavily on historical data, manual assessment, and rule-based models, which often proved insufficient in dynamic financial environments characterized by cyber threats, credit volatility, and regulatory complexity. The integration of AI technologies—including machine learning, deep learning, and big-data analytics—has significantly improved the accuracy, speed, and reliability of financial risk assessment. This empirical study investigates the impact of AI-based risk management practices on risk mitigation effectiveness and financial performance within banking and financial institutions. Primary data were collected from 100 banking professionals using a structured Likert-scale questionnaire measuring AI Risk Analytics Adoption (AIRA), Risk Management Efficiency (RME), and Financial Performance Stability (FPS). Statistical techniques such as descriptive analysis, reliability testing, correlation, regression, and ANOVA were applied. The results reveal a strong positive relationship between AI adoption and both risk management efficiency and financial performance stability. Regression analysis indicates that AI-based risk analytics significantly explain variations in institutional stability and operational resilience. However, concerns regarding cybersecurity, data governance, regulatory compliance, and ethical AI usage remain critical challenges. The study contributes empirical evidence to the literature on digital financial transformation and intelligent risk governance, offering actionable implications for banks, regulators, and policymakers seeking resilient and technology-enabled financial systems.