{"id":"https://openalex.org/W4400908963","doi":"https://doi.org/10.1109/siu61531.2024.10601148","title":"Predicting Credit Repayment Capacity with Machine Learning Models","display_name":"Predicting Credit Repayment Capacity with Machine Learning Models","publication_year":2024,"publication_date":"2024-05-15","ids":{"openalex":"https://openalex.org/W4400908963","doi":"https://doi.org/10.1109/siu61531.2024.10601148"},"language":"en","primary_location":{"id":"doi:10.1109/siu61531.2024.10601148","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/siu61531.2024.10601148","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 32nd Signal Processing and Communications Applications Conference (SIU)","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/A5009536389","display_name":"G\u00f6zde Filiz","orcid":"https://orcid.org/0000-0003-4891-5122"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00f6zde Filiz","raw_affiliation_strings":["Fen Bilimleri Enstit&#x00FC;s&#x01D6;,&#x0130;stanbul,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fen Bilimleri Enstit&#x00FC;s&#x01D6;,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104971397","display_name":"Tolga Bodur","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tolga Bodur","raw_affiliation_strings":["Gaia Bilgi Sistemleri Ltd. &#x015E;ti,&#x0130;stanbul,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gaia Bilgi Sistemleri Ltd. &#x015E;ti,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104971398","display_name":"Nihal Ya\u015fl\u0131da\u011f","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nihal Ya\u015fl\u0131da\u011f","raw_affiliation_strings":["Gaia Bilgi Sistemleri Ltd. &#x015E;ti,&#x0130;stanbul,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gaia Bilgi Sistemleri Ltd. &#x015E;ti,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025739302","display_name":"Alperen Sayar","orcid":"https://orcid.org/0000-0001-6089-2547"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alperen Sayar","raw_affiliation_strings":["TAM Finans Fakt&#x00F6;ring A.&#x015E;.,&#x0130;stanbul,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TAM Finans Fakt&#x00F6;ring A.&#x015E;.,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017598855","display_name":"Tuna \u00c7akar","orcid":"https://orcid.org/0000-0001-8594-7399"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tuna \u00c7akar","raw_affiliation_strings":["Bilgisayar M&#x00FC;hendisli&#x011F;,&#x0130;stanbul,T&#x00FC;rkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bilgisayar M&#x00FC;hendisli&#x011F;,&#x0130;stanbul,T&#x00FC;rkiye","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7851,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.75599839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9419000148773193,"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":0.9419000148773193,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5716568231582642},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46257832646369934},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.390231192111969}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5716568231582642},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46257832646369934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.390231192111969}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/siu61531.2024.10601148","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/siu61531.2024.10601148","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 32nd Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},{"id":"pmh:oai:gcris.mef.edu.tr:20.500.11779/2337","is_oa":false,"landing_page_url":"https://hdl.handle.net/20.500.11779/2337","pdf_url":null,"source":{"id":"https://openalex.org/S7407055184","display_name":"MEF University Institutional Repository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Object"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"This":[0],"study":[1],"examines":[2],"the":[3,6,15,21,87,91,102,110],"transformation":[4],"in":[5,11,120],"financial":[7,55,113],"services":[8],"sector,":[9],"particularly":[10],"banking,":[12],"driven":[13],"by":[14,127],"rapid":[16],"development":[17],"of":[18,24,50,61,94],"technology":[19],"and":[20,27,54,71,83,105,123],"widespread":[22],"use":[23,93],"big":[25],"data,":[26],"its":[28],"impact":[29],"on":[30],"credit":[31,36,45,59],"prediction":[32,37],"processes.":[33],"The":[34,107],"developed":[35,80],"model":[38],"aims":[39],"to":[40,66,100,115],"more":[41],"accurately":[42],"predict":[43],"customers\u2019":[44],"repayment":[46],"capacities.":[47],"In":[48],"pursuit":[49],"this":[51],"goal,":[52],"demographic":[53],"data":[56,68],"along":[57],"with":[58,81],"histories":[60],"customers":[62],"have":[63],"been":[64],"utilized":[65],"employ":[67],"preprocessing":[69],"techniques":[70,97],"test":[72],"various":[73],"classification":[74],"algorithms.":[75],"Findings":[76],"indicate":[77],"that":[78],"models":[79],"XGBoost":[82],"CATBoost":[84],"algorithms":[85],"exhibit":[86],"highest":[88],"performance,":[89],"while":[90],"effective":[92],"feature":[95],"engineering":[96],"is":[98],"revealed":[99],"enhance":[101],"model\u2019s":[103],"accuracy":[104],"reliability.":[106],"research":[108],"highlights":[109],"potential":[111],"for":[112],"institutions":[114],"gain":[116],"a":[117],"competitive":[118],"advantage":[119],"risk":[121],"management":[122,126],"customer":[124],"relationship":[125],"leveraging":[128],"machine":[129],"learning":[130],"models.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}
