Credit risk

This research investigates the relationship between Economic Policy Uncertainty (EPU), credit risk, and lending decisions using the Generalized Method of Moments (GMMs)over the period from2019 to 2023 for12banks listed on the Tehran Stock Exchange. The study employs three regression models to analyze the dynamics of Non-Performing Loans (NPLs), Loan-To-Deposit Ratios(LTDRs), and Return onAssets (ROAs)within the banking sector. Findings reveal a significant persistence in NPLs, indicating that banks with higher past NPLs face ongoing challenges that adversely affect their financial health. A notable negative relationship between Leverage(Lev) and Non-Performing Loan Ratio (NPLR)suggests that more leveraged banks may implement effective risk management strategies, reducing their exposure toNPLs. Additionally, capital adequacy emerges as a critical factor, with higher capital ratios correlating with lower NPLs. The analysis of LTDR indicates thatLevand capital adequacy significantly influence lending practices, while a marginally significant relationship between EPU and LTDR suggests external uncertainties may slightly impact lending decisions. Model results further demonstrate strong persistence in profitability, with historical ROA positively predicting current ROA. Overall, this study underscores the importance of effective risk management practices in banking and highlights ongoing challenges posedbyNPLs, particularly for larger institutions. Recommendations include prioritizing capital buffers and monitoring lending practices to mitigate risks while fostering sustainable profitability growth. Future research should explore additional variables to elucidate the complexities of banking performance metrics

The present study is aimed to Rating the loans granted to the real customers of Bank Melli Iran in accordance with the credit factors of the customers using the multi-objective meta-heuristic algorithm of genetics-adaptive neuro-fuzzy network system (GENETIC-ANFIS). This research is a qualitative-quantitative design and exploratory based on purpose in terms of purpose and descriptive in terms in terms of data collection and analysis method and survey. Qualitative data was collected via the research of Rezaei et al. (2022) and the decision making team of the banking field, and quantitative data was collected through 1178 real customers of Bank Melli of Mazandaran province during the years 2012 to 2021 based on 14 types of loans. According to the rating of granted loans, the risk of each loan was measured separately for 4 personal, environmental, economic and credit factors. In Mudharabah loans, Musyarakah, debt purchase, Istisna and salaf, the economic factor showed the highest sensitivity. Also, the behavior of the research meta-heuristic model has indicated 78% reliability in the accuracy and interpretability of the model compared to genetic algorithm, neural network, fuzzy logic and neural-fuzzy network models..

Objective : This paper introduces a groundbreaking optimization-simulation model, a novel approach that promises to revolutionize credit scoring and loan optimization for banks. Methods : The proposed approach follows a three-stage framework: data preparation, credit scoring, and optimization simulation. In the data preparation stage, corporate client data, including bank loan information and financial statements, has been collected and processed to define and calculate relevant features. The credit scoring stage involved meticulous feature selection using the correlation method, followed by the rigorous training and testing of five classification methods: logistic regression (LR), K-nearest neighbors (KNN), artificial neural network (ANN), adaptive boosting (AdaBoost), and random forest (RF). Model performance has been evaluated using accuracy, F1-score, and area under the curve (AUC) to identify the most effective classifier. In the optimization-simulation stage, the Memetic Algorithm (MA) has been utilized to optimize loan characteristics, including loan size, interest rate, and repayment period, while minimizing the rate of loan defaults. Additionally, this stage incorporated the pre-trained credit scoring model to estimate the impact of loan characteristics on default probabilities.  Results : A case study was conducted using data from 1,000 corporate clients of Bank Tejarat. The optimization-simulation approach has successfully reduced the loan default rate from 33% to below 5%, a significant achievement that underscores its potential to mitigate banks' credit risk. This shows the effectiveness of the proposed method in reducing credit risk for banks. Additionally, the AdaBoost technique achieved the best performance among the credit assessment models. Conclusion : The optimization-simulation approach combines determining the optimal loan specifications with the credit assessment process. This approach considers the impact of loan characteristics on the likelihood of customer default and utilizes this information to reduce banks' credit risk