{"id":"https://openalex.org/W2942967697","doi":null,"title":"APPLICATION OF MACHINE LEARNING ALGORITHMS IN CREDIT CARD DEFAULT PAYMENT PREDICTION","display_name":"APPLICATION OF MACHINE LEARNING ALGORITHMS IN CREDIT CARD DEFAULT PAYMENT PREDICTION","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2942967697","mag":"2942967697"},"language":"en","primary_location":{"id":"mag:3191682272","doi":null,"is_oa":true,"landing_page_url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3848590","pdf_url":null,"source":{"id":"https://openalex.org/S4210172589","display_name":"SSRN Electronic Journal","issn_l":"1556-5068","issn":["1556-5068"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1318003438","host_organization_name":"RELX Group (Netherlands)","host_organization_lineage":["https://openalex.org/I1318003438"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SSRN Electronic Journal","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3848590","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064616712","display_name":"Admel Husejinovic","orcid":"https://orcid.org/0000-0002-1010-5071","observed_orcids":["https://orcid.org/0000-0002-1010-5071"]},"institutions":[{"id":"https://openalex.org/I51716468","display_name":"International Burch University","ror":"https://ror.org/040cm8d21","country_code":"BA","type":"education","lineage":["https://openalex.org/I51716468"]}],"countries":["BA"],"is_corresponding":false,"raw_author_name":"Admel Husejinovic","raw_affiliation_strings":["International Burch University, Faculty of Engineering and Natural Sciences, Department#R##N#of Information Technologies"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"International Burch University, Faculty of Engineering and Natural Sciences, Department#R##N#of Information Technologies","institution_ids":["https://openalex.org/I51716468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052064905","display_name":"Dino \u041a\u0435\u010do","orcid":"https://orcid.org/0000-0002-1583-242X","observed_orcids":["https://orcid.org/0000-0002-1583-242X"]},"institutions":[{"id":"https://openalex.org/I51716468","display_name":"International Burch University","ror":"https://ror.org/040cm8d21","country_code":"BA","type":"education","lineage":["https://openalex.org/I51716468"]}],"countries":["BA"],"is_corresponding":false,"raw_author_name":"Dino \u041a\u0435\u010do","raw_affiliation_strings":["Ph.D., International Burch University, Sarajevo, Bosnia and Herzegovina"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ph.D., International Burch University, Sarajevo, Bosnia and Herzegovina","institution_ids":["https://openalex.org/I51716468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071768924","display_name":"Zerina Ma\u0161eti\u0107","orcid":"https://orcid.org/0000-0002-6226-8868","observed_orcids":["https://orcid.org/0000-0002-6226-8868"]},"institutions":[{"id":"https://openalex.org/I51716468","display_name":"International Burch University","ror":"https://ror.org/040cm8d21","country_code":"BA","type":"education","lineage":["https://openalex.org/I51716468"]}],"countries":["BA"],"is_corresponding":false,"raw_author_name":"Zerina Ma\u0161eti\u0107","raw_affiliation_strings":["Ph.D., International Burch University, Sarajevo, Bosnia and Herzegovina"],"raw_orcid":"https://orcid.org/0000-0002-6226-8868","affiliations":[{"raw_affiliation_string":"Ph.D., International Burch University, Sarajevo, Bosnia and Herzegovina","institution_ids":["https://openalex.org/I51716468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I51716468"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":"10","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9721999764442444,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9721999764442444,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.012799999676644802,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.007000000216066837,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/credit-card-default-prediction","display_name":"credit card default prediction","score":0.968999981880188},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.4860000014305115},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.9760000109672546},{"id":"https://openalex.org/keywords/c4-5-decision-tree","display_name":"C4.5 decision tree","score":0.9670000076293945},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.996999979019165},{"id":"https://openalex.org/keywords/naive-bayes","display_name":"naive Bayes","score":0.9950000047683716},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":1.0},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.8790000081062317},{"id":"https://openalex.org/keywords/bagging","display_name":"bagging","score":0.8949999809265137},{"id":"https://openalex.org/keywords/boosting","display_name":"boosting","score":0.9919999837875366},{"id":"https://openalex.org/keywords/voting-classifier","display_name":"voting classifier","score":0.6949999928474426},{"id":"https://openalex.org/keywords/sensitivity-and-specificity","display_name":"sensitivity and specificity","score":0.5709999799728394},{"id":"https://openalex.org/keywords/banking-decision-making","display_name":"banking decision-making","score":0.6620000004768372}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7676339149475098},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7545900344848633},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.6989356279373169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6901154518127441},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6642464399337769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.649549126625061},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5685529112815857},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5391088128089905},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.538600504398346},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.5214440226554871},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.47741878032684326},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4061664342880249},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2811732888221741},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:3191682272","doi":null,"is_oa":true,"landing_page_url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3848590","pdf_url":null,"source":{"id":"https://openalex.org/S4210172589","display_name":"SSRN Electronic Journal","issn_l":"1556-5068","issn":["1556-5068"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1318003438","host_organization_name":"RELX Group (Netherlands)","host_organization_lineage":["https://openalex.org/I1318003438"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SSRN Electronic Journal","raw_type":null},{"id":"mag:2942967697","doi":null,"is_oa":false,"landing_page_url":"https://www.worldwidejournals.com/international-journal-of-scientific-research-(IJSR)/recent_issues_pdf/2018/October/October_2018_1538388529__251.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S2764914486","display_name":"International journal of scientific research","issn_l":"1992-884X","issn":["1992-884X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320570","host_organization_name":"World Wide Journals","host_organization_lineage":["https://openalex.org/P4310320570"],"host_organization_lineage_names":["World Wide Journals"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International journal of scientific research","raw_type":null}],"best_oa_location":{"id":"mag:3191682272","doi":null,"is_oa":true,"landing_page_url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3848590","pdf_url":null,"source":{"id":"https://openalex.org/S4210172589","display_name":"SSRN Electronic Journal","issn_l":"1556-5068","issn":["1556-5068"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1318003438","host_organization_name":"RELX Group (Netherlands)","host_organization_lineage":["https://openalex.org/I1318003438"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SSRN Electronic Journal","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4386159542","https://openalex.org/W4390045569","https://openalex.org/W4360981885","https://openalex.org/W4406230441","https://openalex.org/W3111242721","https://openalex.org/W4372324581","https://openalex.org/W7134032214","https://openalex.org/W7070602589","https://openalex.org/W2948793373","https://openalex.org/W4401394654"],"abstract_inverted_index":{"Credit":[0],"card":[1,33,104,125],"default":[2,34,102],"payment":[3,35],"prediction":[4,36],"studies":[5],"are":[6],"very":[7],"important":[8],"for":[9,82,101],"any":[10],"\uf001nancial":[11],"institution":[12],"dealing":[13],"with":[14,92,107],"credit":[15,32,103,124],"cards.":[16],"The":[17,61,76,97],"purpose":[18],"of":[19,27,63,95,109,123],"this":[20],"work":[21],"is":[22,66],"to":[23],"evaluate":[24],"the":[25,64,121,130],"performance":[26,62,70],"machine":[28],"learning":[29,55],"methods":[30,56],"on":[31],"using":[37],"logistic":[38,89],"regression,":[39],"C4.5":[40],"decision":[41,134],"tree,":[42],"support":[43],"vector":[44],"machines":[45],"(SVM),":[46],"naive":[47,112],"Bayes,":[48],"k-nearest":[49],"neighbors":[50],"algorithms":[51,65,81],"(k-NN)":[52],"and":[53,59,74,119,127],"ensemble":[54],"voting,":[57],"bagging":[58],"boosting.":[60],"evaluated":[67],"through":[68],"following":[69],"metrics:":[71],"accuracy,":[72],"sensitivity":[73],"speci\uf001city.":[75],"best":[77,98],"result":[78],"among":[79],"all":[80],"overall":[83],"accuracy":[84],"rate":[85,94],"was":[86,111],"achieved":[87],"by":[88],"regression":[90],"model":[91,100],"a":[93],"0.820.":[96],"performing":[99],"customer":[105],"detection,":[106],"success":[108],"71,3%":[110],"Bayes":[113],"model.":[114],"This":[115],"approach":[116],"could":[117],"improve":[118],"ease":[120],"process":[122],"default,":[126],"therefore":[128],"help":[129],"banking":[131],"system":[132],"in":[133],"making.":[135]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-05-09T00:00:00"}
