Role of Report Analysis on Asset Management and Policy Evaluation: A Case Study of Bank of Kigali (2017-2023)

The study assessed the effect of Report Analysis on asset management and policy evaluation in Rwanda: a case of Bank of Kigali. The specific objectives of this research were to assess the report analysis practices used in Bank of Kigali; to analyze the Asset Management and Policy Evaluation of Bank of Kigali; and to find out relationship between report analysis and Asset Management and Policy Evaluation of Bank of Kigali. The researcher explored a descriptive design and correlation research design. Target population was 108 employees including the staff from departments of operational and commercial activities, compliance and risk management, HR & management department; finance and accounting of Bank of Kigali-Rwanda. The research used the varieties of sampling techniques of purposive, random and stratified to select 85 respondents as sample size. The collected data were from questionnaire and documentary instruments which were subjected to quantitative analysis using computer software, specifically the Statistical Packages for Social Sciences (SPSS) and Excel. Descriptive statistics; correlation coefficient and multiple linear regression analysis methods were used to analyze data from BK. Findings revealed that financial analysis shows a strong positive correlation with asset management and policy evaluation (Pearson Correlation = 0.689, p < 0.01), emphasizing the importance of financial scrutiny in effective asset management. Demonstrates a robust positive correlation (Pearson Correlation = 0.765, p < 0.01), suggesting that a rigorous risk evaluation framework is essential for strategic asset management and policy formulation. Market evaluations also shows a strong positive correlation (Pearson Correlation = 0.759, p < 0.01), indicating that market insights significantly influence asset management decisions and policy evaluations. The regression model indicates that approximately 87.8% of the variance in asset management and policy evaluation can be explained by these three predictors, with an Adjusted R-squared value of 87.3%. This robust model fit suggests high predictive power and reliability. The significant F-statistic (F = 193.839, p < 0.05) confirms the overall statistical significance of the model in predicting the effectiveness of asset management and policy evaluation. Among the predictors, financial analysis, risk assessment, and market evaluations all significantly contribute to the model, with financial analysis showing the highest relative importance (Beta = 0.484), followed by market evaluations (Beta = 0.397) and risk assessment (Beta = 0.317). Hypothesis tests reinforced the regression findings. The significant p-values associated with financial analysis, risk assessment, and market evaluations (all p < 0.05) led to the rejection of the null hypotheses, confirming that these factors significantly affect the bank’s asset management and policy evaluation practices. The study conclusively shows that financial analysis, risk assessment, and market evaluations are crucial determinants of asset management and policy evaluation at the Bank of Kigali.