Tạp chí đã xuất bản
2004
ISSN
ISSN 2615-9813
ISSN (số cũ) 1859-3682

SỐ 186 | THÁNG 9/2021

Lược khảo các nghiên cứu về ứng dụng công nghệ phân tích dữ liệu lớn và trí tuệ nhân tạo trong quản trị rủi ro tín dụng của ngân hàng

Nguyễn Minh Sáng, Nguyễn Thị Hồng Vinh

Tóm tắt: Các công nghệ đột phá như phân tích dữ liệu lớn và trí tuệ nhân tạo (TTNT) đã thay đổi cách thức hoạt động của ngân hàng. Nhà quản trị đang cố gắng tìm ra các giải pháp TTNT và dữ liệu lớn phù hợp với các chức năng quản trị rủi ro của ngân hàng. Việc tìm hiểu tài liệu về lĩnh vực này một cách hệ thống sẽ làm rõ hơn tầm quan trọng của việc ứng dụng TTNT và dữ liệu lớn trong quản trị rủi ro tín dụng (QTRRTD). Do đó, mục tiêu của bài viết này là cung cấp một đánh giá toàn diện về các nghiên cứu trong lĩnh vực ứng dụng TTNT và dữ liệu lớn trong QTRRTD thông qua phương pháp phân tích trắc lượng thư mục, phân tích hệ thống mạng lưới và phân tích khái niệm của các nghiên cứu đã công bố trong giai đoạn 1998–2021. Qua đó, kết quả nghiên cứu cho thấy trong giai đoạn trên có đến 192 nghiên cứu về ứng dụng TTNT và dữ liệu lớn trong QTRRTD. Các nghiên cứu đã sử dụng các phương pháp cũng như mô hình khác nhau để đánh giá rủi ro tín dụng. Trong đó, các phương pháp học nhóm, học sâu, học có giám sát, lựa chọn đặc trưng bằng cách sử dụng thuật toán di truyền cho thấy dự báo kết quả tốt hơn các phương pháp QTRRTD truyền thống.  

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Artificial Intelligence and Big Data Applications in Credit Risk Management: A Systematic Review

Abstract: Disruptive technologies such as artificial intelligence and big data analytics have transformed the way banks operate. Administrators are trying to find artificial intelligence and big data solutions that are suitable for the bank's risk credit management functions. A systematic study of the literature on this field will clarify the importance of artificial intelligence and big data in credit risk management. The aim of this paper is to provide a comprehensive review of research in the field of AI and big data applications in credit risk management using a bibliographic, intellectual network, and conceptual analysis of published studies during the period 1998 to 2021. The research results show that in the above period there are one hundred and ninety-two studies on the application of artificial intelligence and big data in credit risk management. Studies have used different methods as well as models to assess credit risk. In particular, group learning methods, deep learning, supervised training, feature or attribute selection using genetic algorithms show better predictive results than traditional methods.  

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