SIDIBE Fatoumata2025-10-102025-10-102025-10-03https://hdl.handle.net/20.500.11951/1915This study examines the impact of credit risk management (CRM) practices specifically risk identification, risk assessment, and risk control on the financial performance of commercial banks in Mali. Anchored in Asymmetric Information Theory and Agency Theory, the research investigates how these practices influence core financial indicators: Return on Assets (ROA), Return on Equity (ROE), and Non-Performing Loan (NPL) ratios. A mixed-methods sequential explanatory design was employed: quantitative data were collected via structured questionnaires from 111 banking professionals across 11 major commercial banks including ECOBANK, BAM, BMS, and BNDA followed by in-depth qualitative interviews with credit officers and branch managers to contextualize and explain statistical patterns. The study addressed three objectives: (i) to examine the effect of credit risk identification practices on financial performance, (ii) to assess the influence of credit risk assessment procedures, and (iii) to evaluate the impact of credit risk control strategies. Descriptive statistics, correlation analysis, and multiple regression were used to analyze the data. Results revealed that while risk identification and risk control are positively correlated (r = 0.279), risk assessment showed weak and negative associations with both. Crucially, regression analysis confirmed that the three CRM practices combined explain 76.4% of the variance in financial performance (R² = 0.764, F = 115.695, p < 0.001). Risk control emerged as the strongest predictor, followed by risk identification both statistically significant. Risk assessment, however, exerted a negligible and statistically insignificant effect when isolated, suggesting that procedural rigor in evaluation does not automatically translate into financial gains without effective implementation and oversight. Qualitative insights corroborate these findings. While Malian banks have institutionalized borrower profiling, sectoral risk screening, and early warning systems, their effectiveness is undermined by fragmented data, inconsistent scoring models, and limited staff capacity. Risk control mechanisms such as loan restructuring, covenant monitoring, and dynamic provisioning show promise but remain constrained by slow legal enforcement, manual tracking systems, and poor interdepartmental coordination. In light of these findings, the study recommends: (1) investing in digital credit infrastructure to improve data reliability and enable real-time risk triggers; (2) standardizing and simplifying risk assessment models to enhance consistency and reduce agent discretion; and (3) prioritizing staff training and legal reform to strengthen execution of control mechanisms. These interventions are not merely operational they are financial imperatives.