Abstract: The general objective of this study was to examine influence of corporate risk management practices on commercial bank financial performance in Rwanda. The study sought to achieve the following specific objectives: to evaluate the influence of Risk Identification, Risk Assessment and Measurement, Risk Monitoring and Control and Risk Mitigation Strategies on bank performance at Bank of Kigali. The data were analyzed using statistical methods, primarily through the calculation of frequencies and percentages, to identify patterns and effectively summarize the findings. For this study, the sample comprised 133 employees from Bank of Kigali. The study relied on secondary data for measurements and scaling, which were applied during the data analysis process. The researcher employed quantitative methods for this study, specifically utilizing a questionnaire to collect data. The collected data were systematically analyzed using the Statistical Package for the Social Sciences (SPSS) software. The ANOVA results assessed the overall significance of the regression model in explaining variability in bank performance. The resulting F-statistic of 91.136 tests whether the model fits significantly better than one without predictors. The p-value of .000, well below the 0.05 threshold, indicates the model is highly statistically significant. Overall, the findings confirm a statistically robust, cohesive risk management system that critically supports operational efficiency and financial stability, with Risk Identification and Risk Mitigation Strategies identified as the most impactful factors.

Abstract: This study aimed to assess the effect of project risk management on performance of Rwanda Urban Development Project (RUDP) in Rwanda. A descriptive and correlational research design was applied, and data analysis was conducted using SPSS to examine the relationship between project risk management practices and project performance. The R-value of 0.862 signifies a strong relationship between the independent variables and project performance, as values above 0.7 indicate a strong effect. The R-Square value of 0.742 indicates that 74.2% of the variations in project performance are explained through project risk identification, risk analysis, response planning, monitoring and control. A unit increase in project risk identification results in a 0.249 increase in project performance (β = 0.249, t = 4.195, p = 0.000), confirming its significance. A unit increase in project risk analysis leads to a 0.217 increase in project performance (β = 0.217, t = 2.771, p = 0.006), reinforcing its role in reducing project uncertainties. Project risk response planning contributes positively, with a unit increase leading to a 0.272 rise in project performance (β = 0.272, t = 3.151, p = 0.002), demonstrating its importance. Likewise, project risk monitoring and control positively impacts project performance. With a unit increase resulting in a 0.204 increase (β = 0.204, t = 3.029, p = 0.003). The study recommended that project managers establish a detailed scope definition framework and ensure strict adherence to guidelines throughout execution to prevent scope creep and maintain project focus.