SỐ 189 | THÁNG 12/2021
Mô hình đánh giá tín dụng SMOTE- Lasso-Logistic
Bùi Thị Thiện Mỹ
Tóm tắt:
Đánh giá tín dụng (ĐGTD) nhằm phân nhóm khách hàng tốt - xấu là một trong những nhiệm vụ quan trọng của quản trị rủi ro tại các ngân hàng và tổ chức tín dụng. Một mô hình ĐGTD tin cậy phải phát hiện đúng nhóm khách hàng xấu. Điều này thường khó đạt được khi chênh lệch số phần tử hai nhóm khách hàng tốt - xấu là lớn. Bên cạnh đó, mô hình ĐGTD cần chỉ rõ những đặc điểm quan trọng của khách hàng để dự báo khả năng vỡ nợ. Bài viết đề xuất một mô hình ĐGTD, được gọi là SMOTE-Lasso-Logistic. Áp dụng kết hợp kỹ thuật tái chọn mẫu SMOTE và phương pháp Lasso trên mô hình hồi quy Logistic, mô hình SMOTE-Lasso-Logistic có thể giải quyết những vấn đề nói trên; đồng thời hiệu quả phân lớp cao hơn các cách tiếp cận truyền thống như mô hình hồi quy Logistic và mô hình Cây phân loại.
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A SMOTE-Lasso-Logistic Credit Scoring Model
Abstract:
Credit scoring to classify good – bad borrowers is one of the important tasks of risk management at banks and credit bureaus. A reliable credit scoring model must correctly discover the bad class. This does not usually succeed if the difference of the number of good and bad borrowers is large. Besides, credit scoring model should point out the significant characteristics of borrowers to predict the probability of default. The paper proposes a credit scoring model called SMOTE-Lasso-Logistic. Applying the combination of the resampling technique SMOTE and Lasso method on Logistic regression, SMOTE-Lasso-Logistic model can solve these issues and have higher classification performance than traditional approaches such as Logistic regression and Decision tree model.