طراحی مدل هوشمند اعتباریابی واردکنندگان کالا با رویکرد یادگیری ماشین

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
تفاوت نرخ ارز رسمی با نرخ ارز در بازار آزاد زمینه سوء‌استفاده اخلال‌گران ارزی را فراهم نموده است. از سویی دیگر واردکنندگان کالا بدون توجه به سوابق عملکرد ارزی و ریالی، مورد ارزیابی شبکه بانکی کشور قرار گرفته و نسبت به اخذ وثایق ریالی از ایشان اقدام می‌شود. هدف اصلی این پژوهش طراحی مدل هوشمند عمیق به منظور ارزیابی ریسک و اخذ وثایق از واردکنندگان کالا به نحوی است که مدل نهایی با بالاترین میزان صحت و دقت، توانایی تحلیل داده‌های بزرگ عملکردی را داشته باشد. بدین منظور، در ابتدا داده‌های عملکردی با استفاده از روش کی‌مینز خوشه‌بندی شده و سپس نتایج مدل خوشه‌بندی به عنوان ورودی مدل‌های طبقه‌بندی شامل روش‌های جنگل تصادفی ، اکس جی بوست و شبکه عصبی کراس ترتیبی مورد استفاده قرار گرفته و نتایج هر مدل با استفاده از شاخص F مورد بررسی قرار گرفته شده است. در نهایت، مدل ترکیبی خوشه‌بندی کی‌مینز- شبکه عصبی کراس ترتیبی به عنوان مدلی که بالاترین میزان شاخص صحت و دقت را داشته انتخاب و با استفاده از مدل یاد شده مشتریان در 3 طبقه قرار گرفته و ترکیب وثایق نقد و غیر نقد برای ایشان تعیین شده است. نتایج بیانگر آن است که مدل به خوبی توانایی طبقه‌بندی مشتریان را با توجه به سوابق عملکردی ایشان، داشته است.

کلیدواژه‌ها


عنوان مقاله English

Title: Designing an Intelligent Credit Model for Goods Importers with a Machine Learning Approach

نویسندگان English

seyed sina madani 1
mahmoud dehghan nayeri 2
Ali Rajabzadeh Ghatari 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

The disparity between the official exchange rate and the market rate has created opportunities for currency manipulators to exploit the system. On the other hand, importers of goods are evaluated by the national banking network regardless of their past foreign exchange and financial performance, and collateral is required from them in the form of domestic currency. The main objective of this study is to design an intelligent deep learning model for risk assessment and collateral determination for importers, in such a way that the final model, with the highest level of accuracy and precision, is capable of analyzing large-scale performance data. For this purpose, operational data were first clustered using the K-means method, and the results of the clustering were then used as input for classification models including Random Forest, XGBoost, and Keras Sequential neural networks. The performance of each model was evaluated using the F-measure index. Finally, the combined K-means–RNN model was selected as the best-performing model with the highest accuracy and precision. Using this model, customers were classified into three risk categories, and an optimal mix of cash and non-cash collateral was determined for each group.
The findings indicate that the proposed model is capable of effectively classifying customers based on their performance history and can serve as a robust tool for credit and foreign exchange risk management in the banking sector.

کلیدواژه‌ها English

Deep Neural Network
Risk Assessment
Artificial Intelligence
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