{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T20:01:29Z","timestamp":1778961689466,"version":"3.51.4"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031714115","type":"print"},{"value":"9783031714122","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T00:00:00Z","timestamp":1728691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T00:00:00Z","timestamp":1728691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-71412-2_17","type":"book-chapter","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T09:02:04Z","timestamp":1728637324000},"page":"229-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advancing Financial Inclusion and Data Ethics: The Role of Alternative Credit Scoring"],"prefix":"10.1007","author":[{"given":"Keoitshepile","family":"Machikape","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1425-8278","authenticated-orcid":false,"given":"Deborah","family":"Oluwadele","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,12]]},"reference":[{"issue":"1","key":"17_CR1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1080\/19439342.2021.1874481","volume":"13","author":"V Azevedo","year":"2021","unstructured":"Azevedo, V., et al.: Credit cards issued by non-financial companies: an alternative tool for financial inclusion and economic development? J. Dev. Effect. 13(1), 47\u201383 (2021)","journal-title":"J. Dev. Effect."},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Njuguna, R., Sowon, K.: Poster: a scoping review of alternative credit scoring literature. In: ACM SIGCAS Conference on Computing and Sustainable Societies, Cape Town (2021)","DOI":"10.1145\/3460112.3471972"},{"issue":"1","key":"17_CR3","doi-asserted-by":"publisher","first-page":"2203435","DOI":"10.1080\/23322039.2023.2203435","volume":"11","author":"GOC Bongomin","year":"2023","unstructured":"Bongomin, G.O.C., et al.: Agent liquidity: a catalyst for mobile money banking among the unbanked poor population in rural sub-Saharan Africa. Cogent Econ. Finan. 11(1), 2203435 (2023)","journal-title":"Cogent Econ. Finan."},{"issue":"155","key":"17_CR4","doi-asserted-by":"publisher","first-page":"113714","DOI":"10.1016\/j.dss.2021.113714","volume":"2022","author":"N Simumba","year":"2022","unstructured":"Simumba, N., et al.: Multiple objective metaheuristics for feature selection based on stakeholder requirements in credit scoring. Decis. Support. Syst. 2022(155), 113714 (2022)","journal-title":"Decis. Support. Syst."},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Cancino, R., et al.: On the combination of graph data for assessing thin-file borrowers\u2019 creditworthiness (2022). Cornell University Library, arXiv.org: Ithaca","DOI":"10.1016\/j.eswa.2022.118809"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Agarwal, S., Qian, W., Tan, R.: Financial inclusion and financial technology. In: Household Finance. Palgrave Macmillan, Singapore, pp. 307\u2013346 (2020). https:\/\/doi.org\/10.1007\/978-981-15-5526-8_9","DOI":"10.1007\/978-981-15-5526-8_9"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"118809","DOI":"10.1016\/j.eswa.2022.118809","volume":"213","author":"R Mu\u00f1oz-Cancino","year":"2023","unstructured":"Mu\u00f1oz-Cancino, R., et al.: On the combination of graph data for assessing thin-file borrowers\u2019 creditworthiness. Expert Syst. Appl. 213, 118809 (2023)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"17_CR8","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1177\/1024529417712830","volume":"21","author":"R Aitken","year":"2017","unstructured":"Aitken, R.: \u2018All data is credit data\u2019: Constituting the unbanked. Compet. Chang. 21(4), 274\u2013300 (2017)","journal-title":"Compet. Chang."},{"issue":"163","key":"17_CR9","doi-asserted-by":"publisher","first-page":"113766","DOI":"10.1016\/j.eswa.2020.113766","volume":"2021","author":"VB Djeundje","year":"2021","unstructured":"Djeundje, V.B., et al.: Enhancing credit scoring with alternative data. Expert Syst. Appl. 2021(163), 113766 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"17_CR10","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1002\/for.2891","volume":"41","author":"Y Xia","year":"2022","unstructured":"Xia, Y., et al.: Deep learning meets decision trees: an application of a heterogeneous deep forest approach in credit scoring for online consumer lending. J. Forecast. 41(8), 1669\u20131690 (2022)","journal-title":"J. Forecast."},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Patwardhan, A.: Chapter 4 - Financial inclusion in the digital age. In: Handbook of Blockchain, Digital Finance, and Inclusion, Volume 1, Lee Kuo Chuen, D., Deng, R. (eds.). Academic Press, pp. 57\u201389 (2018)","DOI":"10.1016\/B978-0-12-810441-5.00004-X"},{"issue":"1","key":"17_CR12","first-page":"1","volume":"1","author":"N Lainez","year":"2023","unstructured":"Lainez, N., Gardner, J.: Algorithmic credit scoring in Vietnam: a legal proposal for maximizing benefits and minimizing risks. Asian J. Law Soc. 1(1), 1\u201332 (2023)","journal-title":"Asian J. Law Soc."},{"issue":"3","key":"17_CR13","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1002\/jsc.2408","volume":"30","author":"V Baghdasaryan","year":"2021","unstructured":"Baghdasaryan, V., et al.: Comparison of econometric and deep learning approaches for credit default classification. Strateg. Chang. 30(3), 257\u2013268 (2021)","journal-title":"Strateg. Chang."},{"issue":"1","key":"17_CR14","first-page":"43","volume":"37","author":"C Okoli","year":"2015","unstructured":"Okoli, C.: A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst. 37(1), 43 (2015)","journal-title":"Commun. Assoc. Inf. Syst."},{"issue":"1","key":"17_CR15","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/02763869.2017.1259891","volume":"36","author":"MDJ Peters","year":"2017","unstructured":"Peters, M.D.J.: Managing and coding references for systematic reviews and scoping reviews in endnote. Med. Ref. Serv. Q. 36(1), 19\u201331 (2017)","journal-title":"Med. Ref. Serv. Q."},{"issue":"6","key":"17_CR16","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1002\/mar.21657","volume":"39","author":"J Paul","year":"2022","unstructured":"Paul, J., Barari, M.: Meta-analysis and traditional systematic literature reviews\u2014what, why, when, where, and how? Psychol. Mark. 39(6), 1099\u20131115 (2022)","journal-title":"Psychol. Mark."},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"113879","DOI":"10.1016\/j.dss.2022.113879","volume":"164","author":"BJG Rozo","year":"2023","unstructured":"Rozo, B.J.G., Crook, J., Andreeva, G.: The role of web browsing in credit risk prediction. Decis. Support. Syst. 164, 113879 (2023)","journal-title":"Decis. Support. Syst."},{"key":"17_CR18","unstructured":"Lee, J.Y.: Essays on Alternative Data in the Consumer Credit Market. Northwestern University: United States \u2013 Illinois, p. 130 (2022)"},{"issue":"12","key":"17_CR19","doi-asserted-by":"publisher","first-page":"597","DOI":"10.3390\/jrfm15120597","volume":"15","author":"LO Hjelkrem","year":"2022","unstructured":"Hjelkrem, L.O., de Lange, P.E., Nesset, E.: The value of open banking data for application credit scoring: case study of a Norwegian bank. J. Risk Finan. Manage. 15(12), 597 (2022)","journal-title":"J. Risk Finan. Manage."},{"issue":"4","key":"17_CR20","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1111\/fima.12295","volume":"48","author":"J Jagtiani","year":"2019","unstructured":"Jagtiani, J., Lemieux, C.: The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform. Financ. Manage. 48(4), 1009\u20131029 (2019)","journal-title":"Financ. Manage."},{"issue":"1","key":"17_CR21","doi-asserted-by":"publisher","first-page":"29","DOI":"10.3390\/risks7010029","volume":"7","author":"M Leo","year":"2019","unstructured":"Leo, M., Sharma, S., Maddulety, K.: Machine learning in banking risk management: a literature review. Risks 7(1), 29 (2019)","journal-title":"Risks"},{"issue":"11","key":"17_CR22","doi-asserted-by":"publisher","first-page":"2767","DOI":"10.1016\/j.jbankfin.2010.06.001","volume":"34","author":"AE Khandani","year":"2010","unstructured":"Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via machine-learning algorithms. J. Bank. Finance 34(11), 2767\u20132787 (2010)","journal-title":"J. Bank. Finance"},{"issue":"74","key":"17_CR23","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.asoc.2018.10.004","volume":"2019","author":"M \u00d3skarsd\u00f3ttir","year":"2019","unstructured":"\u00d3skarsd\u00f3ttir, M., et al.: The value of big data for credit scoring: enhancing financial inclusion using mobile phone data and social network analytics. Appl. Soft Comput. 2019(74), 26\u201339 (2019)","journal-title":"Appl. Soft Comput."},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Simumba, N., et al.: Alternative scoring factors using non-financial data for credit decisions in agricultural microfinance. In: 2018 IEEE International Systems Engineering Symposium (ISSE) (2018)","DOI":"10.1109\/SysEng.2018.8544442"},{"issue":"3","key":"17_CR25","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1080\/01605682.2018.1434402","volume":"70","author":"S De Cnudde","year":"2019","unstructured":"De Cnudde, S., et al.: What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance. J. Oper. Res. Soc. 70(3), 353\u2013363 (2019)","journal-title":"J. Oper. Res. Soc."},{"issue":"4","key":"17_CR26","doi-asserted-by":"publisher","first-page":"575","DOI":"10.3390\/sym13040575","volume":"13","author":"S Okami","year":"2021","unstructured":"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"},{"issue":"1","key":"17_CR27","doi-asserted-by":"publisher","first-page":"113611","DOI":"10.1016\/j.dss.2021.113611","volume":"149","author":"J Zhou","year":"2021","unstructured":"Zhou, J., et al.: Inferring multi-stage risk for online consumer credit services: an integrated scheme using data augmentation and model enhancement. Decis. Support. Syst. 149(1), 113611 (2021)","journal-title":"Decis. Support. Syst."},{"issue":"1","key":"17_CR28","first-page":"130","volume":"14","author":"K Sunghyon","year":"2022","unstructured":"Sunghyon, K., Kim, D., Shin, J.: Can system log data enhance the performance of credit scoring?\u2014evidence from an internet bank in Korea. Sustainability 14(1), 130 (2022)","journal-title":"Sustainability"},{"issue":"6","key":"17_CR29","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1177\/0022243719852959","volume":"56","author":"O Netzer","year":"2019","unstructured":"Netzer, O., Lemaire, A., Herzenstein, M.: When words sweat: identifying signals for loan default in the text of loan applications. J. Mark. Res. 56(6), 960\u2013980 (2019)","journal-title":"J. Mark. Res."},{"key":"17_CR30","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.dss.2018.05.001","volume":"111","author":"L Ma","year":"2018","unstructured":"Ma, L., et al.: A new aspect on P2P online lending default prediction using meta-level phone usage data in China. Decis. Support. Syst. 111, 60\u201371 (2018)","journal-title":"Decis. Support. Syst."},{"issue":"2","key":"17_CR31","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1016\/j.ejor.2021.01.047","volume":"294","author":"T Fitzpatrick","year":"2021","unstructured":"Fitzpatrick, T., Mues, C.: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments. Eur. J. Oper. Res. 294(2), 711\u2013722 (2021)","journal-title":"Eur. J. Oper. Res."},{"key":"17_CR32","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jempfin.2021.01.009","volume":"62","author":"J Jiang","year":"2021","unstructured":"Jiang, J., et al.: Deciphering big data in consumer credit evaluation. J. Empir. Financ. 62, 28\u201345 (2021)","journal-title":"J. Empir. Financ."},{"issue":"2","key":"17_CR33","doi-asserted-by":"publisher","first-page":"67","DOI":"10.17549\/gbfr.2021.26.2.67","volume":"26","author":"D Kim","year":"2021","unstructured":"Kim, D.: Empirical evidence of faulty credit scoring and business failure in P2P lending. Glob. Bus. Finan. Rev. 26(2), 67\u201382 (2021)","journal-title":"Glob. Bus. Finan. Rev."},{"key":"17_CR34","doi-asserted-by":"publisher","first-page":"114486","DOI":"10.1016\/j.eswa.2020.114486","volume":"169","author":"L Roa","year":"2021","unstructured":"Roa, L., et al.: Super-app behavioral patterns in credit risk models: financial, statistical and regulatory implications. Expert Syst. Appl. 169, 114486 (2021)","journal-title":"Expert Syst. Appl."},{"key":"17_CR35","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jebo.2020.03.016","volume":"173","author":"C Croux","year":"2020","unstructured":"Croux, C., et al.: Important factors determining Fintech loan default: evidence from a lendingclub consumer platform. J. Econ. Behav. Organ. 173, 270\u2013296 (2020)","journal-title":"J. Econ. Behav. Organ."},{"issue":"2","key":"17_CR36","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1080\/08982112.2019.1655159","volume":"32","author":"P Giudici","year":"2020","unstructured":"Giudici, P., Hadji-Misheva, B., Spelta, A.: Network based credit risk models. Qual. Eng. 32(2), 199\u2013211 (2020)","journal-title":"Qual. Eng."},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Wu, Y., Pan, Y.: Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model. Complexity, vol. 2021 (2021)","DOI":"10.1155\/2021\/9222617"},{"issue":"2","key":"17_CR38","doi-asserted-by":"publisher","first-page":"3302","DOI":"10.1016\/j.eswa.2008.01.005","volume":"36","author":"T Bellotti","year":"2009","unstructured":"Bellotti, T., Crook, J.: Support vector machines for credit scoring and discovery of significant features. Expert Syst. Appl. 36(2), 3302\u20133308 (2009)","journal-title":"Expert Syst. Appl."},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Gao, Y., et al.: CATE: Contrastive augmentation and tree-enhanced embedding for credit scoring. Inf. Sci. 651, 119447 (2023)","DOI":"10.1016\/j.ins.2023.119447"}],"container-title":["Communications in Computer and Information Science","Society 5.0"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-71412-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T09:05:14Z","timestamp":1728637514000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-71412-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,12]]},"ISBN":["9783031714115","9783031714122"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-71412-2_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,12]]},"assertion":[{"value":"12 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"No conflict of interest was declared.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"Society 5.0","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Society 5.0","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Moka","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mauritius","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"society5.02024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.conference-society5.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}