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NTIS 바로가기지능정보연구 = Journal of intelligence and information systems, v.29 no.3, 2023년, pp.419 - 437
김성수 (연세대학교 정보대학원) , 배준호 (연세대학교 정보대학원) , 이주현 (연세대학교 정보대학원) , 정희주 (하나금융TI 클라우드센터 MSP셀) , 김희웅 (연세대학교 정보대학원)
As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning...
권영탁. (2021, 08.17). [권영탁의 핀테크 이야기]?대안신용평가로 씬파일러들 구제해야. 한국금융신문. https://m.fntimes.com/html/view.php?ud202108140937274397dd55077bc2_18
김명종. (2012). 회사채 신용등급 예측을 위한 SVM?앙상블학습. 지능정보연구, 18(2), 29-45.
김성진, & 안현철. (2016). 기업신용등급 예측을?위한 랜덤 포레스트의 응용. 산업혁신연구,?32(1), 187-211.
김연정. (2021, 09.27). '금융이력부족자' 대부분?신용점수 700점대...은행 대출에 불리. 연합뉴스. https://www.yna.co.kr/view/AKR20210926043100002
김종윤, 장원중, & 김광용. (2019). 온라인 상거래?데이터를 반영한 개인신용평가모형 (커머스?스코어) 개발. 정보기술아키텍처 연구, 16(1),?45-55.
김정산. (2023, 05.09). 금융소비자 양극화..."저신용자 더 빌리고 고신용자 더 모았다". 메트로신문. https://www.metroseoul.co.kr/article/20230509500001
김하영, 허정윤, & 권호창. (2022). 인공지능 기반?금융서비스의 공정성 확보를 위한 체크리스트 제안: 인공지능 기반 개인신용평가를?중심으로. 지능정보연구, 28(3), 259-278.
엄하늘, 김재성, & 최상옥. (2020). 머신러닝 기반?기업부도위험 예측모델 검증 및 정책적 제언:?스태킹 앙상블 모델을 통한 개선을 중심으로.?지능정보연구, 26(2), 105-129.
오윤설. (2020). 신용등급 재평가를 통한 대출 상환 여부 예측. 한국정보과학회 학술발표논문집,?580-582.
유성준, & 박나리. (2020). CART 기법을 이용한?개인신용정보 재현자료 생성 기법. 통계연구,?25(1), 1-30.
이군희, 유영범, & 하승인. (2017). 개인신용평가 모형을 위한 딥러닝 활용에 대한 연구. 대한산업공학회 춘계공동학술대회 논문집, 4042-4047.
이우형. (2018). 딥러닝 학습의 판별 성능 증대를?위한 부스팅 활용에 대한 연구 : 신용평가?모형을 중심으로. 서강대학교.
김유진. (2022, 01.03). [신년기획] 가계대출 총량관리 시작... 은행, 35조 중금리 대출시장서?기회 노린다. 이투데이, https://www.etoday.co.kr/news/view/2092457
김나경 & 김예지. (2023, 03.15). [단독] '無이력의?악순환' 씬파일러 99.9%가 신용 800점 이하..."거래이력이 없지, 빚 안 갚는다 했나". 파이낸셜뉴스, https://www.fnnews.com/news/202303151401284618
홍종선, & 김지훈. (2009). 신용평가모형에서 두?분포함수의 동일성 검정을 위한 비모수적인?검정방법. 한국데이터정보과학회지, 20(2),?261-272.?
Abrahams, C. R., & Zhang, M. (2008). Fair lending?compliance: Intelligence and implications for?credit risk management (Vol. 13). John Wiley?& Sons.
Agosto, A., Giudici, P., & Leach, T. (2019). Spatial?regression models to improve P2P credit risk?management. Frontiers in artificial intelligence,?2, 6.
Baesens, B., Setiono, R., Mues, C., & Vanthienen, J.?(2003). Using neural network rule extraction?and decision tables for credit-risk evaluation.?Management science, 49(3), 312-329.
Benediktsson, J. A., Swain, P. H., & Ersoy, O. K.?(1990). Neural network approaches versus?statistical methods in classification of multisource?remote sensing data. Vancouver, Canada, July?10-14, 1989) IEEE Transactions on Geoscience?and Remote Sensing.
Breiman, L. (2001). Random forests. Machine?learning, 45, 5-32.
Calabrese, R., Elkink, J. A., & Giudici, P. S. (2017).?Measuring bank contagion in Europe using?binary spatial regression models. Journal of the?Operational Research Society, 68, 1503-1511.
Cerda, P., Varoquaux, G., & Kegl, B. (2018).?Similarity encoding for learning with dirty?categorical variables. Machine Learning, 107?(8-10), 1477-1494.
Chen, T., & Guestrin, C. (2016, August). Xgboost: A?scalable tree boosting system. In Proceedings of?the 22nd acm sigkdd international conference?on knowledge discovery and data mining (pp.?785-794).
Chollet, F. (2021). Deep learning with Python. Simon?and Schuster.
Dahouda, M. K., & Joe, I. (2021). A deep-learned?embedding technique for categorical features?encoding. IEEE Access, 9, 114381-114391.
Duarte, F., Martins, B., Pinto, C. S., & Silva, M.?J. (2018). Deep neural models for ICD-10?coding of death certificates and autopsy reports?in free-text. Journal of biomedical informatics,?80, 64-77.
Fu, X., Ouyang, T., Chen, J., & Luo, X. (2020).?Listening to the investors: A novel framework?for online lending default prediction using?deep learning neural networks. Information?Processing & Management, 57(4), 102236.
Guo, C., & Berkhahn, F. (2016). Entity embeddings?of categorical variables. arXiv preprint arXiv:1604.06737.
He, H., & Fan, Y. (2021). A novel hybrid ensemble?model based on tree-based method and deep?learning method for default prediction. Expert?Systems with Applications, 176, 114899.
Hendrycks, D., & Gimpel, K. (2016). Gaussian?error linear units (gelus). arXiv preprint arXiv:1606.08415.
Huang, X., Khetan, A., Cvitkovic, M., & Karnin, Z.?(2020). Tabtransformer: Tabular data modeling?using contextual embeddings. arXiv preprint?arXiv:2012.06678.
Kipf, T. N., & Welling, M. (2016). Semi-supervised?classification with graph convolutional networks.?arXiv preprint arXiv:1609.02907.
Lagasio, V., Pampurini, F., Pezzola, A., & Quaranta,?A. G. (2022). Assessing bank default determinants?via machine learning. Information Sciences,?618, 87-97.
Lee, J. W., & Sohn, S. Y. (2021). Evaluating?borrowers' default risk with a spatial probit?model reflecting the distance in their relational?network. PloS one, 16(12), e0261737.
Lee, J. W., Lee, W. K., & Sohn, S. Y. (2021). Graph?convolutional network-based credit default?prediction utilizing three types of virtual?distances among borrowers. Expert Systems?with Applications, 168, 114411.
Li, Z., Tian, Y., Li, K., Zhou, F., & Yang, W.?(2017). Reject inference in credit scoring using?semi-supervised support vector machines. Expert?Systems with Applications, 74, 105-114.
Li, Z., Wang, X., Yao, L., Chen, Y., Xu, G., &?Lim, E. P. (2022, November). Graph Neural?Network with Self-attention and Multi-task?Learning for Credit Default Risk Prediction.?In Web Information Systems Engineering-WISE?2022: 23rd International Conference, Biarritz,?France, November 1-3, 2022, Proceedings?(pp. 616-629). Cham: Springer International?Publishing.
Liu, X., Li, Y., Jiang, C., Wang, Z., Zhao, F., &?Wang, J. (2022, May). Attentive feature fusion?for credit default prediction. In 2022 IEEE?25th International Conference on Computer?Supported Cooperative Work in Design (CSCWD)?(pp. 816-821). IEEE.
Luo, C., Wu, D., & Wu, D. (2017). A deep learning?approach for credit scoring using credit default?swaps. Engineering Applications of Artificial?Intelligence, 65, 465-470.
Moscato, V., Picariello, A., & Sperli, G. (2021). A?benchmark of machine learning approaches for?credit score prediction. Expert Systems with?Applications, 165, 113986.
Munoz-Cancino, R., Bravo, C., Rios, S. A., & Grana,?M. (2023). On the combination of graph data for?assessing thin-file borrowers' creditworthiness.?Expert Systems with Applications, 213, 118809.
Seger, C. (2018). An investigation of categorical?variable encoding techniques in machine learning:?binary versus one-hot and feature hashing.
Shumovskaia, V., Fedyanin, K., Sukharev, I., Berestnev,?D., & Panov, M. (2021). Linking bank clients?using graph neural networks powered by rich?transactional data. International Journal of?Data Science and Analytics, 12, 135-145.
Vong, W. K., Hendrickson, A. T., Navarro, D. J.,?& Perfors, A. (2019). Do additional features?help or hurt category learning? The curse of?dimensionality in human learners. Cognitive?science, 43(3), e12724.
Woo, H., & Sohn, S. Y. (2022). A credit scoring model?based on the Myers-Briggs type indicator in?online peer-to-peer lending. Financial Innovation,?8(1), 1-19.
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi,?K. (2018). Convolutional neural networks: an?overview and application in radiology. Insights?into imaging, 9, 611-629.
Zhang, L., Wang, J., & Liu, Z. (2023). What should?lenders be more concerned about? Developing a?profit-driven loan default prediction model.?Expert Systems with Applications, 213, 118938.
Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019).?Graph convolutional networks: a comprehensive?review. Computational Social Networks, 6(1),?1-23.
Zhou, L., & Wang, H. (2012). Loan default prediction?on large imbalanced data using random forests.?TELKOMNIKA Indonesian Journal of Electrical?Engineering, 10(6), 1519-1525.
Zhou, X., Zhang, W., & Jiang, Y. (2020). Personal credit?default prediction model based on convolution?neural network. Mathematical Problems in?Engineering, 2020, 1-10.?
기획재정부. (2021.02.17). 씬파일러 정의. 09.05,?2023, from https://www.moef.go.kr/sisa/dictionary/detail?idx3216.
네이버 테크핀 리포트. (2021, 09.04). 09.05, 2023,?from https://www.navercorp.com/navercorp_/research/2022/20220217202609_2.pdf.
통계청. (2023, 03.22). 성별 연령대별 소득. 09.05,?2023, from https://kosis.kr/statHtml/statHtml.do?orgId101&tblIdDT_1EP_2010&conn_pathI2
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