درخت حوزه‌های تخصصی

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

This study aimed to examine the predictive role of hope and emotion regulation in perceived discrimination among adults with physical disabilities. The study employed a correlational descriptive design with a sample of 390 adults with physical disabilities, selected based on the Morgan and Krejcie table. Participants completed validated measures assessing perceived discrimination, hope, and emotion regulation, including the Perceived Discrimination Scale, the Adult Hope Scale, and the Emotion Regulation Questionnaire. Data analysis was conducted using Pearson correlation to examine associations between variables and multiple linear regression to determine the predictive effect of hope and emotion regulation on perceived discrimination. Statistical analyses were performed using SPSS-27, with significance set at p < 0.01. The results indicated a significant negative correlation between hope and perceived discrimination (r = -0.45, p < 0.01) and between cognitive reappraisal and perceived discrimination (r = -0.38, p < 0.01), while expressive suppression was positively correlated with perceived discrimination (r = 0.41, p < 0.01). The regression model was statistically significant (F(2, 387) = 53.04, p < 0.01) with an R² value of 0.27, indicating that hope and emotion regulation explained 27% of the variance in perceived discrimination. Hope (B = -0.39, p < 0.01) and cognitive reappraisal (B = -0.28, p < 0.01) negatively predicted perceived discrimination, whereas expressive suppression (B = 0.31, p < 0.01) was a positive predictor. The findings suggest that hope and adaptive emotion regulation strategies, particularly cognitive reappraisal, serve as protective factors against perceived discrimination, whereas expressive suppression exacerbates discriminatory perceptions. These results highlight the importance of psychological interventions aimed at fostering hope and teaching adaptive emotion regulation strategies to mitigate the impact of discrimination in individuals with physical disabilities.