{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:35:55Z","timestamp":1742970955175,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031220630"},{"type":"electronic","value":"9783031220647"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-22064-7_1","type":"book-chapter","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T19:02:04Z","timestamp":1669230124000},"page":"3-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Supplemental Sampling and Interpretable AI in Credit Scoring for Canadian Fintechs: Methods and Case Studies"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1505-7611","authenticated-orcid":false,"given":"Yi","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"1_CR1","unstructured":"Accenture: Collaborating to win in Canada\u2019s Fintech ecosystem, Accenture 2021 Canadian Fintech Report (2021). https:\/\/www.accenture.com\/_acnmedia\/PDF-149\/Accenture-Fintech-report-2020.pdf"},{"key":"1_CR2","unstructured":"Goulard, B., Lake, K.T., Reynolds M.: Canadian Fintech Review, Torys LLP, November 2021. https:\/\/www.torys.com\/our-latest-thinking\/publications\/2021\/11\/canadian-fintech-review"},{"key":"1_CR3","unstructured":"Fair-Isaac: FAQs-About-FICO-Scores-Canada-2019.pdf (2019). https:\/\/www.ficoscore.com\/ficoscore\/pdf\/FAQs-About-FICO-Scores-Canada-2019.pdf"},{"key":"1_CR4","unstructured":"Tian, Z.Y., Xiao, J.L., Feng H.N., Wei, Y.T.: Credit risk assessment based on gradient boosting decision tree. In: 2019 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI2019)"},{"issue":"8","key":"1_CR5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0255216","volume":"16","author":"Z Ma","year":"2021","unstructured":"Ma, Z., Hou, W., Zhang, D.: A credit risk assessment model of borrowers in P2P lending based on BP neural network. PLoS ONE 16(8), e0255216 (2021). https:\/\/doi.org\/10.1371\/journal.pone.0255216","journal-title":"PLoS ONE"},{"key":"1_CR6","unstructured":"Hand, D.J.: Reject inference in credit operations: theory and methods. In: The Handbook of Credit Scoring, pp. 225\u2013240. Glenlake Publishing Company (2001). \/art00177"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Kang, Y., Cui, R., Deng, J., Jia, N.: A novel credit scoring framework for auto loan using an imbalanced-learning-based reject inference. In: 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/CIFEr.2019.8759110"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Barakova, I., Glennon, D., Palvia, A.: Sample selection bias in acquisition credit scoring models: an evaluation of the supplemental-data approach. J. Credit Risk 9, 77\u2013117 (2013)","DOI":"10.21314\/JCR.2013.165"},{"key":"1_CR9","unstructured":"Surrya, P.D., Radcliffea, N.J.: Why size does matter in credit scoring. In: Proceedings of Credit Scoring and Credit Control V, Edinburgh (1997) (1997)"},{"key":"1_CR10","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, D.J., Henley, W.E.: Statistical classification methods in consumer credit scoring: a review. J. R. Stat. Soc. A 160, 523\u2013541 (1997)","journal-title":"J. R. Stat. Soc. A"},{"issue":"2\u20133","key":"1_CR11","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1002\/isaf.325","volume":"18","author":"H Abdou","year":"2011","unstructured":"Abdou, H., Pointon, J.: Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intell. Syst. Account. Finance Manag. 18(2\u20133), 59\u201388 (2011)","journal-title":"Intell. Syst. Account. Finance Manag."},{"key":"1_CR12","doi-asserted-by":"publisher","DOI":"10.1002\/0471722146","volume-title":"Applied Logistic Regression","author":"DW Hosmer","year":"2000","unstructured":"Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression, 2nd edn. Wiley, New York (2000)","edition":"2"},{"key":"1_CR13","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. The Wadsworth, Belmont (1984)"},{"key":"1_CR14","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C.C., Shen, D., Rastogi, R. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13\u201317 August 2016, pp. 785\u2013794. ACM (2016). arXiv:1603.02754. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"1_CR15","doi-asserted-by":"publisher","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H.: Boosting and additive trees. In: Hastie, T., Tibshirani, R., Friedman, J.H (eds.) The Elements of Statistical Learning, 2nd edn., pp. 337\u2013384. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7_10. ISBN 978-0-387-84857-0","DOI":"10.1007\/978-0-387-84858-7_10"},{"key":"1_CR16","unstructured":"Buja, A., Stuetzle, W., Shen, Y.: Loss functions for binary class probability estimation: structure and applications, Technical report, The Wharton School, University of Pennsylvania, January 2005"},{"key":"1_CR17","unstructured":"Shen, Y.: Loss functions for binary classification and class probability estimation, Ph.D. dissertation, The Wharton School, University of Pennsylvania (2005)"},{"key":"1_CR18","unstructured":"McBurnett, M., Sembolini, F., Turner, M., Jordan, L., Hamilton, H., Torres, S.R.: Comparative Analysis of Machine Learning Credit Risk Model Interpretability: Model Explanations, Reasons for Denial and Routes for Score Improvements, Credit Scoring and Credit Control XVII, University of Edinburgh, UK, August 26 2021 (2021)"},{"key":"1_CR19","unstructured":"Turner, M., Jordan, L., Joshua, A.: Machine-learning techniques for monotonic neural networks, Equifax, US patent 11010669 (2021)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22064-7_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T19:02:20Z","timestamp":1669230140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22064-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031220630","9783031220647"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22064-7_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2022.uqcloud.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"198","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}