{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T22:26:11Z","timestamp":1782512771914,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>As a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client\u2019s default probability. However, most existing deep learning solutions treat each application as an independent individual, neglecting the explicit connections among different application records. Besides, these attempts suffer from the problem of missing data and imbalanced distribution (i.e., the default records are small samples against all the applications). We believe similar records could provide some auxiliary signals, which are of critical importance to alleviate the data missing issue and facilitate data argumentation. To this end, we propose multi-view loan application graphs, dubbed MLAGs. By evaluating the similarity between the records, a loan application graph can be constructed. Furthermore, we arrange different similarity thresholds to organize various graph structures for multi-graph constructions; thus, a variety of representations can be generated via information propagation and aggregation for small sample argumentation. Consequently, the imbalanced data distribution and missing values issues can be alleviated effectively. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models. Ablation studies also illustrated the validity of each module design.<\/jats:p>","DOI":"10.1007\/s00521-024-09695-x","type":"journal-article","created":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T02:01:20Z","timestamp":1713492080000},"page":"12149-12162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-view GCN for loan default risk prediction"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1802-4284","authenticated-orcid":false,"given":"Zihao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yakun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"9695_CR1","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.knosys.2013.07.008","volume":"51","author":"P Hajek","year":"2013","unstructured":"Hajek P, Michalak K (2013) Feature selection in corporate credit rating prediction. Knowl-Based Syst 51:72\u201384","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"9695_CR2","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1109\/TNNLS.2018.2870573","volume":"30","author":"F Tan","year":"2018","unstructured":"Tan F, Hou X, Zhang J, Wei Z, Yan Z (2018) A deep learning approach to competing risks representation in peer-to-peer lending. IEEE Trans Neural Netw Learn Syst 30(5):1565\u20131574","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9695_CR3","doi-asserted-by":"crossref","unstructured":"Hu B, Zhang Z, Zhou J, Fang J, Jia Q, Fang Y, Yu Q, Qi Y (2020) Loan default analysis with multiplex graph learning. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 2525\u20132532","DOI":"10.1145\/3340531.3412724"},{"key":"9695_CR4","volume-title":"Fair lending compliance: intelligence and implications for credit risk management","author":"CR Abrahams","year":"2008","unstructured":"Abrahams CR, Zhang M (2008) Fair lending compliance: intelligence and implications for credit risk management. Wiley, New York"},{"issue":"1","key":"9695_CR5","doi-asserted-by":"publisher","first-page":"133","DOI":"10.18421\/TEM101-16","volume":"10","author":"Y Aleksandrova","year":"2021","unstructured":"Aleksandrova Y (2021) Comparing performance of machine learning algorithms for default risk prediction in peer to peer lending. TEM J 10(1):133\u2013143","journal-title":"TEM J"},{"issue":"10","key":"9695_CR6","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.1016\/j.eswa.2015.02.001","volume":"42","author":"M Malekipirbazari","year":"2015","unstructured":"Malekipirbazari M, Aksakalli V (2015) Risk assessment in social lending via random forests. Expert Syst Appl 42(10):4621\u20134631","journal-title":"Expert Syst Appl"},{"issue":"3","key":"9695_CR7","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.dss.2010.08.008","volume":"50","author":"S Bhattacharyya","year":"2011","unstructured":"Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50(3):602\u2013613","journal-title":"Decis Support Syst"},{"issue":"7553","key":"9695_CR8","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. nature 521(7553):436\u2013444","journal-title":"nature"},{"key":"9695_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.11.027","volume":"552","author":"F Zhou","year":"2021","unstructured":"Zhou F, Qi X, Xiao C, Wang J (2021) Metarisk: semi-supervised few-shot operational risk classification in banking industry. Inf Sci 552:1\u201316","journal-title":"Inf Sci"},{"key":"9695_CR10","doi-asserted-by":"crossref","unstructured":"Chi J, Zeng G, Zhong Q, Liang T, Feng J, Ao X, Tang J (2020) Learning to undersampling for class imbalanced credit risk forecasting. In: 2020 IEEE international conference on data mining (ICDM), IEEE, pp 72\u201381","DOI":"10.1109\/ICDM50108.2020.00016"},{"key":"9695_CR11","doi-asserted-by":"crossref","unstructured":"Wang D, Zhang Z, Zhou J, Cui P, Fang J, Jia Q, Fang Y, Qi Y (2021) Temporal-aware graph neural network for credit risk prediction. In: Proceedings of the 2021 SIAM international conference on data mining (SDM), SIAM, pp 702\u2013710","DOI":"10.1137\/1.9781611976700.79"},{"key":"9695_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.120081","volume":"659","author":"S Wei","year":"2024","unstructured":"Wei S, Lv J, Guo Y, Yang Q, Chen X, Zhao Y, Li Q, Zhuang F, Kou G (2024) Combining intra-risk and contagion risk for enterprise bankruptcy prediction using graph neural networks. Inform Sci 659:120081","journal-title":"Inform Sci"},{"key":"9695_CR13","unstructured":"Guo X, Quan Y, Zhao H, Yao Q, Li Y, Tu W (2021) Tabgnn: Multiplex graph neural network for tabular data prediction. arXiv:2108.09127"},{"issue":"6","key":"9695_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3078848","volume":"8","author":"H Zhao","year":"2017","unstructured":"Zhao H, Ge Y, Liu Q, Wang G, Chen E, Zhang H (2017) P2p lending survey: platforms, recent advances and prospects. ACM Trans Intell Syst Technol 8(6):1\u201328","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"3","key":"9695_CR15","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1111\/j.1467-985X.1997.00078.x","volume":"160","author":"DJ Hand","year":"1997","unstructured":"Hand DJ, Henley WE (1997) Statistical classification methods in consumer credit scoring: a review. J R Stat Soc A Stat Soc 160(3):523\u2013541","journal-title":"J R Stat Soc A Stat Soc"},{"issue":"3","key":"9695_CR16","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1016\/j.ejor.2006.09.100","volume":"183","author":"JN Crook","year":"2007","unstructured":"Crook JN, Edelman DB, Thomas LC (2007) Recent developments in consumer credit risk assessment. Eur J Oper Res 183(3):1447\u20131465","journal-title":"Eur J Oper Res"},{"issue":"5","key":"9695_CR17","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1016\/j.jbankfin.2004.04.008","volume":"29","author":"RM Stein","year":"2005","unstructured":"Stein RM (2005) The relationship between default prediction and lending profits: integrating roc analysis and loan pricing. J Bank Financ 29(5):1213\u20131236","journal-title":"J Bank Financ"},{"issue":"2","key":"9695_CR18","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/S0377-2217(97)00398-6","volume":"112","author":"S Piramuthu","year":"1999","unstructured":"Piramuthu S (1999) Financial credit-risk evaluation with neural and neurofuzzy systems. Eur J Oper Res 112(2):310\u2013321","journal-title":"Eur J Oper Res"},{"issue":"10","key":"9695_CR19","doi-asserted-by":"publisher","first-page":"0139427","DOI":"10.1371\/journal.pone.0139427","volume":"10","author":"C Serrano-Cinca","year":"2015","unstructured":"Serrano-Cinca C, Guti\u00e9rrez-Nieto B, L\u00f3pez-Palacios L (2015) Determinants of default in p2p lending. PLoS ONE 10(10):0139427","journal-title":"PLoS ONE"},{"issue":"4","key":"9695_CR20","doi-asserted-by":"publisher","first-page":"4007","DOI":"10.1016\/j.eswa.2011.09.075","volume":"39","author":"SY Sohn","year":"2012","unstructured":"Sohn SY, Kim JW (2012) Decision tree-based technology credit scoring for start-up firms: Korean case. Expert Syst Appl 39(4):4007\u20134012","journal-title":"Expert Syst Appl"},{"issue":"4","key":"9695_CR21","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/S0167-9236(03)00086-1","volume":"37","author":"Z Huang","year":"2004","unstructured":"Huang Z, Chen H, Hsu C-J, Chen W-H, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4):543\u2013558","journal-title":"Decis Support Syst"},{"issue":"3","key":"9695_CR22","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1016\/j.ejor.2009.03.036","volume":"201","author":"HS Kim","year":"2010","unstructured":"Kim HS, Sohn SY (2010) Support vector machines for default prediction of smes based on technology credit. Eur J Oper Res 201(3):838\u2013846","journal-title":"Eur J Oper Res"},{"issue":"6","key":"9695_CR23","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/TFUZZ.2005.859320","volume":"13","author":"Y Wang","year":"2005","unstructured":"Wang Y, Wang S, Lai KK (2005) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(6):820\u2013831","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"9695_CR24","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1016\/j.eswa.2006.07.007","volume":"33","author":"C-L Huang","year":"2007","unstructured":"Huang C-L, Chen M-C, Wang C-J (2007) Credit scoring with a data mining approach based on support vector machines. Expert Syst Appl 33(4):847\u2013856","journal-title":"Expert Syst Appl"},{"issue":"3","key":"9695_CR25","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0957-4174(02)00044-1","volume":"23","author":"T-S Lee","year":"2002","unstructured":"Lee T-S, Chiu C-C, Lu C-J, Chen I-F (2002) Credit scoring using the hybrid neural discriminant technique. Expert Syst Appl 23(3):245\u2013254","journal-title":"Expert Syst Appl"},{"issue":"2","key":"9695_CR26","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.dss.2010.11.006","volume":"50","author":"P Ravisankar","year":"2011","unstructured":"Ravisankar P, Ravi V, Rao GR, Bose I (2011) Detection of financial statement fraud and feature selection using data mining techniques. Decis Support Syst 50(2):491\u2013500","journal-title":"Decis Support Syst"},{"key":"9695_CR27","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.knosys.2012.04.025","volume":"35","author":"T-C Wu","year":"2012","unstructured":"Wu T-C, Hsu M-F (2012) Credit risk assessment and decision making by a fusion approach. Knowl-Based Syst 35:102\u2013110","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"9695_CR28","doi-asserted-by":"publisher","first-page":"2650","DOI":"10.1016\/j.eswa.2011.08.120","volume":"39","author":"B-W Chi","year":"2012","unstructured":"Chi B-W, Hsu C-C (2012) A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model. Expert Syst Appl 39(3):2650\u20132661","journal-title":"Expert Syst Appl"},{"issue":"1","key":"9695_CR29","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.eswa.2005.01.003","volume":"29","author":"C-S Ong","year":"2005","unstructured":"Ong C-S, Huang J-J, Tzeng G-H (2005) Building credit scoring models using genetic programming. Expert Syst Appl 29(1):41\u201347","journal-title":"Expert Syst Appl"},{"issue":"4","key":"9695_CR30","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.1016\/j.eswa.2007.08.093","volume":"35","author":"JT Quah","year":"2008","unstructured":"Quah JT, Sriganesh M (2008) Real-time credit card fraud detection using computational intelligence. Expert Syst Appl 35(4):1721\u20131732","journal-title":"Expert Syst Appl"},{"issue":"4","key":"9695_CR31","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.ijforecast.2013.04.003","volume":"29","author":"T Bellotti","year":"2013","unstructured":"Bellotti T, Crook J (2013) Forecasting and stress testing credit card default using dynamic models. Int J Forecast 29(4):563\u2013574","journal-title":"Int J Forecast"},{"issue":"3","key":"9695_CR32","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1016\/j.ejor.2006.10.066","volume":"183","author":"Y Yang","year":"2007","unstructured":"Yang Y (2007) Adaptive credit scoring with kernel learning methods. Eur J Oper Res 183(3):1521\u20131536","journal-title":"Eur J Oper Res"},{"issue":"2","key":"9695_CR33","first-page":"1039","volume":"174","author":"J-J Huang","year":"2006","unstructured":"Huang J-J, Tzeng G-H, Ong C-S (2006) Two-stage genetic programming (2sgp) for the credit scoring model. Appl Math Comput 174(2):1039\u20131053","journal-title":"Appl Math Comput"},{"issue":"2","key":"9695_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/S0305-0483(03)00016-1","volume":"31","author":"R Malhotra","year":"2003","unstructured":"Malhotra R, Malhotra DK (2003) Evaluating consumer loans using neural networks. Omega 31(2):83\u201396","journal-title":"Omega"},{"issue":"2","key":"9695_CR35","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","volume":"36","author":"I-C Yeh","year":"2009","unstructured":"Yeh I-C, Lien C-H (2009) The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst Appl 36(2):2473\u20132480","journal-title":"Expert Syst Appl"},{"key":"9695_CR36","doi-asserted-by":"crossref","unstructured":"Babaev D, Savchenko M, Tuzhilin A, Umerenkov D (2019) Et-rnn: applying deep learning to credit loan applications. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2183\u20132190","DOI":"10.1145\/3292500.3330693"},{"key":"9695_CR37","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.eswa.2018.02.029","volume":"102","author":"H Kvamme","year":"2018","unstructured":"Kvamme H, Sellereite N, Aas K, Sjursen S (2018) Predicting mortgage default using convolutional neural networks. Expert Syst Appl 102:207\u2013217","journal-title":"Expert Syst Appl"},{"key":"9695_CR38","doi-asserted-by":"crossref","unstructured":"Liu Q, Liu Z, Zhang H, Chen Y, Zhu J (2021) Mining cross features for financial credit risk assessment. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 1069\u20131078","DOI":"10.1145\/3459637.3482371"},{"key":"9695_CR39","doi-asserted-by":"crossref","unstructured":"Cui L, Bai L, Wang Y, Bai X, Zhang Z, Hancock ER (2016) P2p lending analysis using the most relevant graph-based features. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR), Springer, pp 3\u201314","DOI":"10.1007\/978-3-319-49055-7_1"},{"key":"9695_CR40","doi-asserted-by":"crossref","unstructured":"Zhong Q, Liu Y, Ao X, Hu B, Feng J, Tang J, He Q (2020) Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network. In: Proceedings of The Web conference 2020, pp 785\u2013795","DOI":"10.1145\/3366423.3380159"},{"key":"9695_CR41","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"9695_CR42","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"9695_CR43","unstructured":"Oono K, Suzuki T (2019) Graph neural networks exponentially lose expressive power for node classification. arXiv:1905.10947"},{"key":"9695_CR44","doi-asserted-by":"crossref","unstructured":"Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, et al (2016) Wide & deep learning for recommender systems. In: 1st Workshop on deep learning for recommender systems, pp 7\u201310","DOI":"10.1145\/2988450.2988454"},{"key":"9695_CR45","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"9695_CR46","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"9695_CR47","unstructured":"Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning, PMLR, pp 6861\u20136871"},{"key":"9695_CR48","unstructured":"Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning, PMLR, pp 1725\u20131735"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09695-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09695-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09695-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T10:11:22Z","timestamp":1720433482000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09695-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,19]]},"references-count":48,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["9695"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09695-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2754272\/v1","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,19]]},"assertion":[{"value":"19 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}