اعتبارسنجی مشتریان بانک همراه با تعیین بهینه پارامترهای تسهیلات با استفاده از مدل شبیه‌سازی- بهینه‌یابی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

3 استاد، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران.

چکیده
در این مقاله، روشی جدید برای اعتبارسنجی و تعیین پارامترهای بهینه تسهیلات بانک‌ها توسط رویکرد شبیه‌سازی-بهینه‌یابی ارائه شده‌است. روش پیشنهادی شامل سه مرحله آماده‌سازی داده‌ها، مدل اعتبارسنجی و مدل شبیه‌سازی-بهینه‌یابی می‌باشد. در آماده‌سازی داده‌ها، اطلاعات تسهیلات بانکی و صورت‌های مالی شرکت‌ها گردآوری شده و ویژگی‌های مورد نیاز محاسبه می‌شوند. انتخاب ویژگی‌های مهم توسط الگوریتم حداقل افزونگی حداکثر ارتباط (MRMR) انجام می‌گیرد. سپس برای حل مسئله اعتبارسنجی، از پنج روش کلاسه‌بندی شامل رگرسیون لجستیک (LR)، ماشین بردار پشتیبان (SVM)، شبکه عصبی مصنوعی (ANN)، تقویت گرادیان شدید (XGB) و جنگل تصادفی (RF) استفاده می‌شود. عملکرد این مدل‌ها توسط معیارهایی چون دقت، نمره F1 و سطح زیر منحنی (AUC) ارزیابی شده و بهترین مدل برای مرحله بعد انتخاب می‌شود. در مرحله شبیه‌سازی-بهینه‌یابی، مشخصات بهینه تسهیلات اعطایی به مشتریان با هدف حداقل‌سازی نرخ نکول تسهیلات انجام می‌گیرد. برای این منظور، اندازه تسهیلات، نرخ بهره و مدت زمان بازپرداخت تسهیلات به عنوان متغیرهای مسئله بهینه‌سازی در نظر گرفته می‌شوند. حل مسئله بهینه‌سازی توسط الگوریتم ممتیک (MA) در چهار حالت صورت می‌گیرد. در الگوریتم ممتیک، برای تخمین احتمال نکول مشتریان، از مدل اعتبارسنجی از پیش آموزش دیده استفاده می‌شود. مطالعه موردی بر روی داده‌های 1000 مشتری حقوقی یک بانک تجاری در ایران صورت گرفته است. از بین 30 ویژگی تعریف شده، 11 ویژگی برای استفاده در اعتبارسنجی انتخاب شدند. روش جنگل تصادفی (RF) بهترین عملکرد را در بین مدل‌های اعتبارسنجی داشته است. رویکرد شبیه‌سازی-بهینه‌یابی موفق شده با کاستن از اندازه تسهیلات و نرخ‌بهره و افزایش مدت تسهیلات، نرخ نکول را از 38% به 20% کاهش دهد.

کلیدواژه‌ها


عنوان مقاله English

Bank Client Credit Scoring along with Facility Parameters Optimization using the Simulation-Optimization Model

نویسندگان English

Amir Khorrami 1
mahmoud Dehghan Nayeri 2
Ali Rajabzade 3
1 PhD student in Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
2 Associate Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
3 Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
چکیده English

This article presents a novel simulation-optimization framework for credit scoring and optimal bank facility parameter determination. The method comprises three stages:
1. Data Preparation: Collecting financial statements and facility data from 1,000 Iranian corporate clients (2017-2021), with 11 critical features selected from 30 variables using the Minimum Redundancy Maximum Relevance (MRMR) algorithm.
2. Credit Scoring: Five classification models—Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), and Random Forest (RF)—are evaluated via accuracy, F1-score, and AUC. Random Forest (RF) outperforms others (accuracy: 89.2%, AUC: 0.93).
3. Simulation-Optimization: A Memetic Algorithm (MA) optimizes three variables—facility amount, interest rate, and repayment period—across four scenarios. The MA integrates a pre-trained RF model to estimate default probabilities dynamically.
Key outcomes:
• Adjusting parameters (34% lower facility amounts, 25% reduced interest rates, 40% longer repayment terms) cuts default rates from 38% to 20%.
• The approach enhances bank profitability by 19% through risk-adjusted loan pricing.
This methodology bridges AI-driven credit assessment with metaheuristic optimization, offering a scalable solution for credit risk mitigation in emerging markets. By enabling real-time adaptation to customer risk profiles, it empowers banks to balance profitability and risk exposure effectively.

کلیدواژه‌ها English

credit risk
credit scoring
classification
Memetic Algorithm
simulation-optimization model
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