The Role of Bank Credit in Assessing Financial Risk and Predicting Financial Failure Using the Z-Score Model: Evidence from Iraqi Commercial Banks
DOI:
https://doi.org/10.53819/81018102t5403Abstract
This paper examined how bank credit can be used to measure financial risk and predict financial failure using the Z-score, drawing on data from Iraqi commercial banks. The research used panel data regression to estimate a sample of banks across the study period, with bank credit (loans) as the primary explanatory variable and total assets and total liabilities as control variables. The experimental findings revealed that bank credit significantly and positively influences financial stability, as indicated by the Z-score (β1 = 0.0100, p = 0.043). In particular, a rise in bank loans will have a quantifiable positive effect on the Z-score, thereby reducing the likelihood of bankruptcy. Total assets also showed a positive and significant correlation with financial stability (2 = 0.01), indicating that larger banks with diversified asset bases are more resilient to financial risk. Conversely, total liabilities have an adverse, statistically significant effect on Z-Score (β3 is negative and p < 0.05), indicating that greater leverage implies greater financial risk and undermines banks' solvency. The general model has strong explanatory power, with the adjusted R2 well above levels deemed acceptable in panel data studies, and the outcomes are also robust across different model specifications. These results confirm that the Z-Score model is effective at forecasting financial failure. That sound credit control is a key factor in improving the financial stability of Iraqi commercial banks. The study recommends calibrate the credit growth in accordance with the internal risk appetite and the macro-financial environment. Set bank-specific ranges of loan growth percentiles, like percentile ranges based on historical volatility, and ensure that executives must approve any variation, and that it is clearly connected to capital planning and liquidity buffers. Increase and strengthen underwriting requirements when cyclical conditions are favourable, thereby eliminating potential weaknesses. Integrate the Z-score into regular risk management, track its patterns on board risk committees, set tolerance limits aligned with stress-test results, and associate violations with automated reactions such as tightening credit conditions, changing charges, or increasing capital.
Keywords: Bank Credit, Financial Risk, Financial Failure, Z-Score, Iraqi Commercial Banks, and Panel Data.
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