{"id":"https://openalex.org/W7151963879","doi":"https://doi.org/10.1109/isdfs69419.2026.11459062","title":"A Comparative Study of Machine Learning Models for Micro-Segment Credit Risk Prediction","display_name":"A Comparative Study of Machine Learning Models for Micro-Segment Credit Risk Prediction","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7151963879","doi":"https://doi.org/10.1109/isdfs69419.2026.11459062"},"language":null,"primary_location":{"id":"doi:10.1109/isdfs69419.2026.11459062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isdfs69419.2026.11459062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 14th International Symposium on Digital Forensics and Security (ISDFS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133173389","display_name":"Kerem Kaya","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126406","display_name":"Xuzhou Construction Machinery Group (China)","ror":"https://ror.org/02y5rmj89","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126406"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kerem Kaya","raw_affiliation_strings":["Mindsane Ltd Co.,R&#x0026;D Department,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"Mindsane Ltd Co.,R&#x0026;D Department,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I4210126406"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133155705","display_name":"Emir \u00c7etin Memi\u015f","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126406","display_name":"Xuzhou Construction Machinery Group (China)","ror":"https://ror.org/02y5rmj89","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126406"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Emir \u00c7etin Memi\u015f","raw_affiliation_strings":["Mindsane Ltd Co.,R&#x0026;D Department,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"Mindsane Ltd Co.,R&#x0026;D Department,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I4210126406"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056236010","display_name":"Seyit Ertu\u011frul","orcid":"https://orcid.org/0000-0003-0828-7336"},"institutions":[{"id":"https://openalex.org/I4210126406","display_name":"Xuzhou Construction Machinery Group (China)","ror":"https://ror.org/02y5rmj89","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126406"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Seyit Ertu\u011frul","raw_affiliation_strings":["TAM Finans Inc.,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"TAM Finans Inc.,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I4210126406"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133197386","display_name":"Hakan Karamanl\u0131","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126406","display_name":"Xuzhou Construction Machinery Group (China)","ror":"https://ror.org/02y5rmj89","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126406"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hakan Karamanl\u0131","raw_affiliation_strings":["TAM Finans Inc.,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"TAM Finans Inc.,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I4210126406"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048872144","display_name":"Yassine Drias","orcid":"https://orcid.org/0000-0002-8896-6170"},"institutions":[{"id":"https://openalex.org/I1290270558","display_name":"Goodyear (United Kingdom)","ror":"https://ror.org/03tvt3c62","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290270558","https://openalex.org/I4210143271"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yassine Drias","raw_affiliation_strings":["Computer Engineering, MEF University,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"Computer Engineering, MEF University,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I1290270558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061349020","display_name":"Semen Son-Turan","orcid":"https://orcid.org/0000-0002-7457-8417"},"institutions":[{"id":"https://openalex.org/I1290270558","display_name":"Goodyear (United Kingdom)","ror":"https://ror.org/03tvt3c62","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290270558","https://openalex.org/I4210143271"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Semen Son-Turan","raw_affiliation_strings":["MEF University,Department of Business Administration,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"MEF University,Department of Business Administration,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I1290270558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118397255","display_name":"Nazl\u0131 Toraganl\u0131-Karamollao\u011flu","orcid":null},"institutions":[{"id":"https://openalex.org/I1301968116","display_name":"MEF University","ror":"https://ror.org/05jz51y94","country_code":"TR","type":"education","lineage":["https://openalex.org/I1301968116"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Nazl\u0131 Tora\u011fanl\u0131-Karamollao\u011flu","raw_affiliation_strings":["MEF University,Department of Economics,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"MEF University,Department of Economics,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I1301968116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017598855","display_name":"Tuna \u00c7akar","orcid":"https://orcid.org/0000-0001-8594-7399"},"institutions":[{"id":"https://openalex.org/I1290270558","display_name":"Goodyear (United Kingdom)","ror":"https://ror.org/03tvt3c62","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290270558","https://openalex.org/I4210143271"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tuna \u00c7akar","raw_affiliation_strings":["Computer Engineering, MEF University,&#x0130;stanbul,T&#x00FC;rkiye"],"affiliations":[{"raw_affiliation_string":"Computer Engineering, MEF University,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":["https://openalex.org/I1290270558"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5133173389"],"corresponding_institution_ids":["https://openalex.org/I4210126406"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.95811015,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.7961000204086304,"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":0.7961000204086304,"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/T11496","display_name":"Credit Risk and Financial Regulations","score":0.08959999680519104,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.00930000003427267,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/credit-risk","display_name":"Credit risk","score":0.4867999851703644},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.30230000615119934},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.2994999885559082},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29649999737739563},{"id":"https://openalex.org/keywords/risk-assessment","display_name":"Risk assessment","score":0.2768000066280365},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.273499995470047}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5566999912261963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.541100025177002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5396999716758728},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.4867999851703644},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.27309998869895935},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2554999887943268},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isdfs69419.2026.11459062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isdfs69419.2026.11459062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 14th International Symposium on Digital Forensics and Security (ISDFS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6139979958534241}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2085766370","https://openalex.org/W2118978333","https://openalex.org/W2131816657","https://openalex.org/W2172852798","https://openalex.org/W2295598076","https://openalex.org/W2911964244","https://openalex.org/W2949676527","https://openalex.org/W4388451421","https://openalex.org/W4392601910","https://openalex.org/W4402194596","https://openalex.org/W4408543711","https://openalex.org/W4416302727"],"related_works":[],"abstract_inverted_index":{"We":[0,137],"studied":[1],"the":[2,9,30,36,49,58,63,78,184,190],"early":[3],"prediction":[4],"of":[5,40,109,193],"credit":[6,119],"risk":[7],"in":[8,88],"micro":[10],"segment":[11],"as":[12],"a":[13,22,81,213],"binary":[14],"classification":[15],"problem.":[16],"The":[17,25],"dataset":[18,38,79],"was":[19,52,55,75,86,180,197,204],"provided":[20],"by":[21],"financial":[23],"company.":[24],"tables":[26],"were":[27,68,127,170],"combined":[28],"with":[29,129,212],"common":[31],"identifier":[32],"feature":[33,216],"to":[34,70,77,97,118,132,163,199],"create":[35],"final":[37],"consisting":[39],"over":[41],"150":[42],"thousand":[43],"rows":[44],"and":[45,111,113,121,143,146,166,202],"92":[46],"columns.":[47,136],"Since":[48],"target":[50],"distribution":[51],"unbalanced,":[53],"undersampling":[54],"applied":[56],"during":[57],"model":[59],"training":[60],"phase.":[61],"In":[62],"preprocessing":[64],"step,":[65],"date":[66],"fields":[67],"reduced":[69],"month-period":[71],"level,":[72],"inflation":[73,130,135],"data":[74],"added":[76],"on":[80,104],"month-bymonth":[82],"basis.":[83],"Feature":[84],"engineering":[85],"structured":[87],"two":[89],"main":[90],"groups:":[91],"payment":[92,106],"history":[93,108],"flags":[94],"for":[95,153,195],"up":[96],"<tex":[98],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[99],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathbf{1":[100],"8}$</tex>":[101],"months":[102],"based":[103],"previous":[105],"performance":[107,208],"customers":[110],"limit":[112],"balance":[114],"aggregations":[115,126],"derived":[116],"according":[117],"type":[120],"time":[122],"windows":[123],"(L3M-L36M).":[124],"Monetary":[125],"normalized":[128],"information":[131],"produce":[133],"various":[134],"evaluated":[138],"Logistic":[139],"Regression,":[140],"Random":[141],"Forest,":[142],"Extra":[144],"Trees,":[145],"three":[147],"gradient-boosting":[148],"models":[149,161],"(XGBoost,":[150],"LightGBM,":[151],"CatBoost)":[152],"comparative":[154],"performance.":[155],"Cross-validation":[156],"results":[157],"showed":[158],"that":[159,206],"boosting-based":[160],"tended":[162],"perform":[164],"better,":[165],"macro":[167],"F1":[168],"scores":[169],"obtained":[171],"at":[172],"XGBoost":[173,196],"0.81939,":[174],"LightGBM":[175,179],"0.82157,":[176],"CatBoost":[177],"0.82031;":[178],"marginally":[181],"higher":[182],"than":[183],"other":[185],"boosting":[186],"models.":[187],"With":[188],"RFECV,":[189],"optimal":[191],"number":[192],"features":[194],"found":[198],"be":[200,210],"70,":[201],"it":[203],"shown":[205],"similar":[207],"could":[209],"maintained":[211],"more":[214],"compact":[215],"set":[217]},"counts_by_year":[],"updated_date":"2026-04-10T06:02:16.177343","created_date":"2026-04-09T00:00:00"}
