{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T05:39:37Z","timestamp":1744695577864,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031154706"},{"type":"electronic","value":"9783031154713"}],"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\/978-3-031-15471-3_32","type":"book-chapter","created":{"date-parts":[[2022,9,11]],"date-time":"2022-09-11T09:04:42Z","timestamp":1662887082000},"page":"375-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Assessment of\u00a0Creditworthiness Models Privacy-Preserving Training with\u00a0Synthetic Data"],"prefix":"10.1007","author":[{"given":"Ricardo","family":"Mu\u00f1oz-Cancino","sequence":"first","affiliation":[]},{"given":"Cristi\u00e1n","family":"Bravo","sequence":"additional","affiliation":[]},{"given":"Sebasti\u00e1n A.","family":"R\u00edos","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Gra\u00f1a","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"issue":"3","key":"32_CR1","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.tjem.2018.08.001","volume":"18","author":"H Akoglu","year":"2018","unstructured":"Akoglu, H.: User\u2019s guide to correlation coefficients. Turk. J. Emerg. Med. 18(3), 91\u201393 (2018)","journal-title":"Turk. J. Emerg. Med."},{"issue":"7","key":"32_CR2","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7), 1145\u20131159 (1997)","journal-title":"Pattern Recognit."},{"key":"32_CR3","doi-asserted-by":"publisher","first-page":"113766","DOI":"10.1016\/j.eswa.2020.113766","volume":"163","author":"VB Djeundje","year":"2021","unstructured":"Djeundje, V.B., Crook, J., Calabrese, R., Hamid, M.: Enhancing credit scoring with alternative data. Expert Syst. with Appl. 163, 113766 (2021)","journal-title":"Expert Syst. with Appl."},{"key":"32_CR4","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.ins.2017.12.030","volume":"479","author":"U Fiore","year":"2019","unstructured":"Fiore, U., De Santis, A., Perla, F., Zanetti, P., Palmieri, F.: Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Inf. Sci. 479, 448\u2013455 (2019)","journal-title":"Inf. Sci."},{"key":"32_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511973000","volume-title":"Machine Learning - The Art and Science of Algorithms that Make Sense of Data","author":"PA Flach","year":"2012","unstructured":"Flach, P.A.: Machine Learning - The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge (2012)"},{"key":"32_CR6","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Annals Stat. 29, 1189\u20131232 (2001)","journal-title":"Annals Stat."},{"issue":"2","key":"32_CR7","doi-asserted-by":"publisher","first-page":"e12363","DOI":"10.1111\/exsy.12363","volume":"36","author":"A Gici\u0107","year":"2019","unstructured":"Gici\u0107, A., Subasi, A.: Credit scoring for a microcredit data set using the synthetic minority oversampling technique and ensemble classifiers. Expert Syst. 36(2), e12363 (2019)","journal-title":"Expert Syst."},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Goh, R.Y., Lee, L.S.: Credit scoring: a review on support vector machines and metaheuristic approaches. Adv. Oper. Res. 2019 (2019)","DOI":"10.1155\/2019\/1974794"},{"key":"32_CR9","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Hagberg, A., Swart, P., SChult, D.: Exploring network structure, dynamics, and function using networkx. In: In Proceedings of the 7th Python in Science Conference (SciPy), pp. 11\u201315. Citeseer (2008)","DOI":"10.25080\/TCWV9851"},{"issue":"1","key":"32_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3348\/kjr.2004.5.1.11","volume":"5","author":"PS Ho","year":"2004","unstructured":"Ho, P.S., Mo, G.J., Chan-Hee, J.: Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J. Radiol. 5(1), 11\u201318 (2004)","journal-title":"Korean J. Radiol."},{"issue":"5","key":"32_CR12","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/BF02589501","volume":"3","author":"J Hodges","year":"1958","unstructured":"Hodges, J.: The significance probability of the smirnov two-sample test. Arkiv f\u00f6r Matematik 3(5), 469\u2013486 (1958)","journal-title":"Arkiv f\u00f6r Matematik"},{"issue":"3","key":"32_CR13","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1197\/jamia.M1733","volume":"12","author":"G Hripcsak","year":"2005","unstructured":"Hripcsak, G., Rothschild, A.S.: Agreement, the F-measure, and reliability in information retrieval. J. Am. Med. Inform. Assoc. 12(3), 296\u2013298 (2005)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"4","key":"32_CR14","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1016\/j.eswa.2012.08.052","volume":"40","author":"K Kennedy","year":"2013","unstructured":"Kennedy, K., Mac Namee, B., Delany, S., O\u2019Sullivan, M., Watson, N.: A window of opportunity: assessing behavioural scoring. Expert Syst. Appl. 40(4), 1372\u20131380 (2013)","journal-title":"Expert Syst. Appl."},{"key":"32_CR15","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"issue":"12","key":"32_CR16","doi-asserted-by":"publisher","first-page":"8451","DOI":"10.1007\/s00521-019-04335-1","volume":"32","author":"K Lei","year":"2019","unstructured":"Lei, K., Xie, Y., Zhong, S., Dai, J., Yang, M., Shen, Y.: Generative adversarial fusion network for class imbalance credit scoring. Neural Comput. Appl. 32(12), 8451\u20138462 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04335-1","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"32_CR17","doi-asserted-by":"publisher","first-page":"143","DOI":"10.11613\/BM.2013.018","volume":"23","author":"ML McHugh","year":"2013","unstructured":"McHugh, M.L.: The chi-square test of independence. Biochemia. Med. 23(2), 143\u2013149 (2013)","journal-title":"Biochemia. Med."},{"key":"32_CR18","unstructured":"Mu\u00f1oz-Cancino, R., Bravo, C., R\u00edos, S.A., Gra\u00f1a, M.: On the combination of graph data for assessing thin-file borrowers\u2019 creditworthiness. arXiv preprint arXiv:2111.13666 (2021)"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Cancino, R., Bravo, C., R\u00edos, S.A., Gra\u00f1a, M.: On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance. arXiv preprint arXiv:2204.06122 (2022)","DOI":"10.2139\/ssrn.4092346"},{"issue":"3","key":"32_CR20","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3390\/risks9030049","volume":"9","author":"KS Ngwenduna","year":"2021","unstructured":"Ngwenduna, K.S., Mbuvha, R.: Alleviating class imbalance in actuarial applications using generative adversarial networks. Risks 9(3), 49 (2021)","journal-title":"Risks"},{"key":"32_CR21","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.asoc.2018.10.004","volume":"74","author":"M \u00d3skarsd\u00f3ttir","year":"2019","unstructured":"\u00d3skarsd\u00f3ttir, M., Bravo, C., Sarraute, C., Vanthienen, J., Baesens, B.: The value of big data for credit scoring: enhancing financial inclusion using mobile phone data and social network analytics. Appl. Soft Comput. 74, 26\u201339 (2019)","journal-title":"Appl. Soft Comput."},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Patki, N., Wedge, R., Veeramachaneni, K.: The synthetic data vault. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 399\u2013410 (2016)","DOI":"10.1109\/DSAA.2016.49"},{"issue":"4","key":"32_CR23","doi-asserted-by":"publisher","first-page":"575","DOI":"10.3390\/sym13040575","volume":"13","author":"N Simumba","year":"2021","unstructured":"Simumba, N., Okami, S., Kodaka, A., Kohtake, N.: Spatiotemporal integration of mobile, satellite, and public geospatial data for enhanced credit scoring. Symmetry 13(4), 575 (2021)","journal-title":"Symmetry"},{"key":"32_CR24","unstructured":"The Basel Committee on Banking Supervision: Principles for the management of credit risk. Basel Committee Publications 75 (2000). www.bis.org\/publ\/bcbs75.pdf"},{"key":"32_CR25","unstructured":"Torres, D.G.: Generation of synthetic data with generative adversarial networks. Ph.D. thesis, Ph. D. Thesis, Royal Institute of Technology, Stockholm, Sweden, 26 November (2018)"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Wan, Z., Zhang, Y., He, H.: Variational autoencoder based synthetic data generation for imbalanced learning. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1\u20137 (2017)","DOI":"10.1109\/SSCI.2017.8285168"},{"key":"32_CR27","unstructured":"Xu, L.: Synthesizing tabular data using conditional GAN. Ph.D. thesis, Massachusetts Institute of Technology (2020)"},{"key":"32_CR28","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. CoRR abs\/1907.00503 (2019)"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15471-3_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T17:01:39Z","timestamp":1727974899000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15471-3_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031154706","9783031154713"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15471-3_32","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":"12 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamancaa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"5 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2022.haisconference.eu\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","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":"43","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":"64% - 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":"3","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":"2","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)"}}]}}