Applying in machine learning and deep learning in finance industry: A case study on repayment prediction
In the current era marked by the proliferation of peer-to-peer lending platforms, the imperative of ascertaining borrowers’ capacity to honor their financial obligations has assumed paramount significance. This endeavor transcends mere risk mitigation for individual investors, extending to the ident...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | Vietnamese |
| Published: |
TẠP CHÍ KHOA HỌC ĐẠI HỌC MỞ THÀNH PHỐ HỒ CHÍ MINH
2024-12-01
|
| Series: | Tạp chí Khoa học Đại học Mở Thành phố Hồ Chí Minh - Kinh tế và Quản trị kinh doanh |
| Subjects: | |
| Online Access: | https://journalofscience.ou.edu.vn/index.php/econ-vi/article/view/3828 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861251880517632 |
|---|---|
| author | Nguyễn Phát Đạt Hồ Mai Minh Nhật Trương Công Vinh Lê Quang Chấn Phong Lê Hoành Sử |
| author_facet | Nguyễn Phát Đạt Hồ Mai Minh Nhật Trương Công Vinh Lê Quang Chấn Phong Lê Hoành Sử |
| author_sort | Nguyễn Phát Đạt |
| collection | DOAJ |
| description | In the current era marked by the proliferation of peer-to-peer lending platforms, the imperative of ascertaining borrowers’ capacity to honor their financial obligations has assumed paramount significance. This endeavor transcends mere risk mitigation for individual investors, extending to the identification of judicious investment prospects. The present inquiry advocates for the adoption of sophisticated computational methodologies, including machine learning and deep learning, to analyze borrowers’ behavioral patterns, demographic profiles, and credit histories, thus facilitating the prognostication of loan repayment likelihood. Employed techniques encompass Logistic Regression (LR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), in conjunction with deep learning architectures such as Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN). Following methodological refinement, it becomes apparent that ensemble learning approaches, exemplified by XGB and LGBM, exhibit markedly superior predictive performance, surpassing conventional models with an accuracy rate exceeding 85%. Salient predictors include interest rates, credit ratings, and loan amounts. It is anticipated that the findings of this investigation will furnish investors with a potent analytical toolset for discerning and selecting loan portfolios, thereby fostering greater transparency and efficiency within the peer-to-peer lending ecosystem. |
| format | Article |
| id | doaj-art-54e87c82f7ba4becb85472db6a0aa02e |
| institution | Kabale University |
| issn | 2734-9306 2734-9578 |
| language | Vietnamese |
| publishDate | 2024-12-01 |
| publisher | TẠP CHÍ KHOA HỌC ĐẠI HỌC MỞ THÀNH PHỐ HỒ CHÍ MINH |
| record_format | Article |
| series | Tạp chí Khoa học Đại học Mở Thành phố Hồ Chí Minh - Kinh tế và Quản trị kinh doanh |
| spelling | doaj-art-54e87c82f7ba4becb85472db6a0aa02e2025-02-10T04:17:41ZvieTẠP CHÍ KHOA HỌC ĐẠI HỌC MỞ THÀNH PHỐ HỒ CHÍ MINHTạp chí Khoa học Đại học Mở Thành phố Hồ Chí Minh - Kinh tế và Quản trị kinh doanh2734-93062734-95782024-12-01201355310.46223/HCMCOUJS.econ.vi.20.1.3828.20252321Applying in machine learning and deep learning in finance industry: A case study on repayment predictionNguyễn Phát Đạt0Hồ Mai Minh Nhật1Trương Công Vinh2Lê Quang Chấn Phong3Lê Hoành Sử4Trường Đại Học Kinh tế - Luật, Thành phố Hồ Chí Minh Đại học Quốc Gia Thành Phố Hồ Chí Minh, Thành phố Hồ Chí MinhTrường Đại Học Kinh tế - Luật, Thành phố Hồ Chí Minh Đại học Quốc Gia Thành Phố Hồ Chí Minh, Thành phố Hồ Chí MinhTrường Đại Học Kinh tế - Luật, Thành phố Hồ Chí Minh Đại học Quốc Gia Thành Phố Hồ Chí Minh, Thành phố Hồ Chí MinhTrường Đại Học Kinh tế - Luật, Thành phố Hồ Chí Minh Đại học Quốc Gia Thành Phố Hồ Chí Minh, Thành phố Hồ Chí MinhTrường Đại Học Kinh tế - Luật, Thành phố Hồ Chí Minh Đại học Quốc Gia Thành Phố Hồ Chí Minh, Thành phố Hồ Chí MinhIn the current era marked by the proliferation of peer-to-peer lending platforms, the imperative of ascertaining borrowers’ capacity to honor their financial obligations has assumed paramount significance. This endeavor transcends mere risk mitigation for individual investors, extending to the identification of judicious investment prospects. The present inquiry advocates for the adoption of sophisticated computational methodologies, including machine learning and deep learning, to analyze borrowers’ behavioral patterns, demographic profiles, and credit histories, thus facilitating the prognostication of loan repayment likelihood. Employed techniques encompass Logistic Regression (LR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), in conjunction with deep learning architectures such as Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN). Following methodological refinement, it becomes apparent that ensemble learning approaches, exemplified by XGB and LGBM, exhibit markedly superior predictive performance, surpassing conventional models with an accuracy rate exceeding 85%. Salient predictors include interest rates, credit ratings, and loan amounts. It is anticipated that the findings of this investigation will furnish investors with a potent analytical toolset for discerning and selecting loan portfolios, thereby fostering greater transparency and efficiency within the peer-to-peer lending ecosystem.https://journalofscience.ou.edu.vn/index.php/econ-vi/article/view/3828dự báo khả năng hoàn trả khoản vayđánh giá rủi rohọc máyhọc sâuvay ngang hàng |
| spellingShingle | Nguyễn Phát Đạt Hồ Mai Minh Nhật Trương Công Vinh Lê Quang Chấn Phong Lê Hoành Sử Applying in machine learning and deep learning in finance industry: A case study on repayment prediction Tạp chí Khoa học Đại học Mở Thành phố Hồ Chí Minh - Kinh tế và Quản trị kinh doanh dự báo khả năng hoàn trả khoản vay đánh giá rủi ro học máy học sâu vay ngang hàng |
| title | Applying in machine learning and deep learning in finance industry: A case study on repayment prediction |
| title_full | Applying in machine learning and deep learning in finance industry: A case study on repayment prediction |
| title_fullStr | Applying in machine learning and deep learning in finance industry: A case study on repayment prediction |
| title_full_unstemmed | Applying in machine learning and deep learning in finance industry: A case study on repayment prediction |
| title_short | Applying in machine learning and deep learning in finance industry: A case study on repayment prediction |
| title_sort | applying in machine learning and deep learning in finance industry a case study on repayment prediction |
| topic | dự báo khả năng hoàn trả khoản vay đánh giá rủi ro học máy học sâu vay ngang hàng |
| url | https://journalofscience.ou.edu.vn/index.php/econ-vi/article/view/3828 |
| work_keys_str_mv | AT nguyenphatđat applyinginmachinelearninganddeeplearninginfinanceindustryacasestudyonrepaymentprediction AT homaiminhnhat applyinginmachinelearninganddeeplearninginfinanceindustryacasestudyonrepaymentprediction AT truongcongvinh applyinginmachinelearninganddeeplearninginfinanceindustryacasestudyonrepaymentprediction AT lequangchanphong applyinginmachinelearninganddeeplearninginfinanceindustryacasestudyonrepaymentprediction AT lehoanhsu applyinginmachinelearninganddeeplearninginfinanceindustryacasestudyonrepaymentprediction |