In recent years, artificial intelligence has emerged as one of the key technologies in the digital transformation of the financial sector. Leasing companies, as financial intermediaries active in securing long-term investments, face numerous challenges in the areas of credit assessment, financial risk forecasting, and operational optimization. This research, with a mixed approach (qualitative-quantitative) and using simulated data based on the Monte Carlo method (10,000 iterations), examined the role of artificial intelligence in improving operational performance and risk management in leasing companies. Qualitative findings from interviews with 10 experts revealed three main themes: structural challenges in performance, the need for intelligent risk management systems, and the prominence of artificial intelligence technology. Accordingly, simulated quantitative data showed that AI deployment reduced decision-making time by 64% (from 14 days to 5 days), operational costs by 18%, and default prediction accuracy by 72% to 89%. Fraud risk was also reduced by 66%. Statistical tests confirmed that all differences were statistically significant. Regression analysis also showed that “real-time coordination” and “data integration” were the strongest predictors of operational performance and risk management, respectively.