{"id":"https://openalex.org/W2897811458","doi":"https://doi.org/10.3233/rda-180051","title":"Appropriate machine learning techniques for credit scoring and bankruptcy prediction in banking and finance: A comparative study","display_name":"Appropriate machine learning techniques for credit scoring and bankruptcy prediction in banking and finance: A comparative study","publication_year":2018,"publication_date":"2018-10-19","ids":{"openalex":"https://openalex.org/W2897811458","doi":"https://doi.org/10.3233/rda-180051","mag":"2897811458"},"language":"en","primary_location":{"id":"doi:10.3233/rda-180051","is_oa":false,"landing_page_url":"https://doi.org/10.3233/rda-180051","pdf_url":null,"source":{"id":"https://openalex.org/S2764680446","display_name":"Risk and Decision Analysis","issn_l":"1569-7371","issn":["1569-7371","1875-9173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Risk and Decision Analysis","raw_type":"journal-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/A5046314899","display_name":"Dalila Boughaci","orcid":"https://orcid.org/0000-0001-5210-8951"},"institutions":[{"id":"https://openalex.org/I157169809","display_name":"University of Sciences and Technology Houari Boumediene","ror":"https://ror.org/02kb89c09","country_code":"DZ","type":"education","lineage":["https://openalex.org/I157169809"]}],"countries":["DZ"],"is_corresponding":true,"raw_author_name":"Dalila Boughaci","raw_affiliation_strings":["LRIA/Computer Science Department, University of Sciences and Technology Houari Boumediene, , , , Algeria E-mails: dalila_info@yahoo.fr, dboughaci@usthb.dz"],"affiliations":[{"raw_affiliation_string":"LRIA/Computer Science Department, University of Sciences and Technology Houari Boumediene, , , , Algeria E-mails: dalila_info@yahoo.fr, dboughaci@usthb.dz","institution_ids":["https://openalex.org/I157169809"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091867094","display_name":"Abdullah A. K. Alkhawaldeh","orcid":"https://orcid.org/0000-0003-1844-549X"},"institutions":[{"id":"https://openalex.org/I157392197","display_name":"Hashemite University","ror":"https://ror.org/04a1r5z94","country_code":"JO","type":"education","lineage":["https://openalex.org/I157392197"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Abdullah A. K. Alkhawaldeh","raw_affiliation_strings":[", , The Hashemite University, , Jordan E-mail: alkwaldhh@yahoo.com","Department of Accounting, Faculty of Economics and Administrative Sciences, The Hashemite University, Zarqa, Jordan E-mail:"],"affiliations":[{"raw_affiliation_string":", , The Hashemite University, , Jordan E-mail: alkwaldhh@yahoo.com","institution_ids":["https://openalex.org/I157392197"]},{"raw_affiliation_string":"Department of Accounting, Faculty of Economics and Administrative Sciences, The Hashemite University, Zarqa, Jordan E-mail:","institution_ids":["https://openalex.org/I157392197"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5046314899"],"corresponding_institution_ids":["https://openalex.org/I157169809"],"apc_list":null,"apc_paid":null,"fwci":4.6718,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.9453102,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"8","issue":"1-2","first_page":"15","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9997000098228455,"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":0.9997000098228455,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9940999746322632,"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"}},{"id":"https://openalex.org/T11496","display_name":"Credit Risk and Financial Regulations","score":0.9592000246047974,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7695711851119995},{"id":"https://openalex.org/keywords/bankruptcy","display_name":"Bankruptcy","score":0.701928973197937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.682506799697876},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6551569700241089},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4942186176776886},{"id":"https://openalex.org/keywords/bankruptcy-prediction","display_name":"Bankruptcy prediction","score":0.48187360167503357},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.4805089235305786},{"id":"https://openalex.org/keywords/german","display_name":"German","score":0.4769153296947479},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.42818576097488403},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3028985261917114},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.13211804628372192}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7695711851119995},{"id":"https://openalex.org/C504631918","wikidata":"https://www.wikidata.org/wiki/Q152074","display_name":"Bankruptcy","level":2,"score":0.701928973197937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.682506799697876},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6551569700241089},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4942186176776886},{"id":"https://openalex.org/C2777388754","wikidata":"https://www.wikidata.org/wiki/Q1664594","display_name":"Bankruptcy prediction","level":3,"score":0.48187360167503357},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.4805089235305786},{"id":"https://openalex.org/C154775046","wikidata":"https://www.wikidata.org/wiki/Q188","display_name":"German","level":2,"score":0.4769153296947479},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.42818576097488403},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3028985261917114},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.13211804628372192},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/rda-180051","is_oa":false,"landing_page_url":"https://doi.org/10.3233/rda-180051","pdf_url":null,"source":{"id":"https://openalex.org/S2764680446","display_name":"Risk and Decision Analysis","issn_l":"1569-7371","issn":["1569-7371","1875-9173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Risk and Decision Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W39040241","https://openalex.org/W46659105","https://openalex.org/W1564856853","https://openalex.org/W1594031697","https://openalex.org/W1673066967","https://openalex.org/W1817561967","https://openalex.org/W1988790447","https://openalex.org/W2005510983","https://openalex.org/W2023846550","https://openalex.org/W2024046085","https://openalex.org/W2067223851","https://openalex.org/W2086841270","https://openalex.org/W2088048599","https://openalex.org/W2125055259","https://openalex.org/W2128420091","https://openalex.org/W2132166479","https://openalex.org/W2148603752","https://openalex.org/W2158068969","https://openalex.org/W2168123127","https://openalex.org/W2801727166","https://openalex.org/W2903950532","https://openalex.org/W4212883601","https://openalex.org/W4233045210","https://openalex.org/W6602002561","https://openalex.org/W6633696202"],"related_works":["https://openalex.org/W2285942202","https://openalex.org/W4237436108","https://openalex.org/W2241582670","https://openalex.org/W2347342407","https://openalex.org/W3180694154","https://openalex.org/W2270360991","https://openalex.org/W2940834140","https://openalex.org/W2738355008","https://openalex.org/W4238227620","https://openalex.org/W3012986183"],"abstract_inverted_index":{"Machine":[0],"learning":[1,43,63,128],"techniques":[2,16,44,64],"have":[3],"been":[4],"used":[5,18],"successfully":[6],"in":[7,41,51,140],"several":[8,66,90],"areas":[9],"such":[10],"as":[11],"banking":[12],"and":[13,23,48,53,57,71,113,134,136,155,193,223],"finance.":[14],"These":[15],"are":[17,39,103,181,260],"mainly":[19],"for":[20,45,85,132,232],"prediction,":[21],"classification":[22],"partitioning":[24],"data":[25],"into":[26],"different":[27],"groups":[28],"according":[29],"to":[30,78,92,151,170,205,210,246,263],"a":[31,59,138],"certain":[32],"common":[33],"characteristic.":[34],"In":[35,144],"this":[36,267],"work,":[37],"we":[38,117],"interested":[40],"machine":[42,62,127],"credit":[46],"scoring":[47],"bankruptcy":[49],"prediction":[50],"finance":[52],"banking.":[54],"We":[55,88],"evaluate":[56,93],"compare":[58],"range":[60],"of":[61,96,266],"on":[65,105,175,188,220,252],"datasets":[67],"issued":[68],"from":[69],"banks":[70],"financial":[72],"institutions":[73],"where":[74],"the":[75,80,94,97,119,141,173,177,185,199,211,242,247,258,264],"aim":[76],"is":[77,166,203,218,229],"select":[79],"most":[81,230],"appropriate":[82,231],"methods":[83,129,148,187,259],"suitable":[84],"each":[86],"dataset.":[87,234,268],"use":[89],"metrics":[91],"performance":[95],"obtained":[98],"models.":[99],"The":[100,126,160,215],"empirical":[101],"studies":[102],"conducted":[104],"German,":[106],"Australian,":[107],"Japanese,":[108],"Polish,":[109],"Indian":[110,253],"Qualitative":[111,254],"Bankruptcy":[112,255],"Taiwan":[114,236],"datasets.":[115,178,190],"Also,":[116,179],"consider":[118],"huge":[120],"\u201cGive":[121],"Me":[122,195],"Some":[123,196],"Credit":[124,197],"dataset\u201d.":[125],"produce":[130,206],"scores":[131,208],"applicants":[133,157],"companies":[135],"help":[137],"lot":[139],"decision":[142],"making.":[143],"other":[145,248],"words,":[146],"these":[147],"permit":[149],"us":[150],"distinguish":[152],"between":[153,184],"bad":[154],"good":[156,207],"or":[158],"companies.":[159],"numerical":[161],"study":[162],"shows":[163],"that":[164],"there":[165,180],"no":[167],"method":[168,202,217,228,240],"able":[169,204],"consistently":[171],"outperform":[172],"others":[174,212],"all":[176],"significant":[182],"differences":[183],"studied":[186,213],"some":[189],"For":[191,235],"German":[192],"Give":[194],"datasets,":[198,225],"Bayes":[200],"net":[201],"compared":[209,245],"methods.":[214],"LogitBoost":[216],"competitive":[219],"both":[221],"Polish":[222],"Australian":[224],"while":[226],"AdaBoost":[227],"Japanese":[233],"dataset,":[237,256],"Random":[238],"Forest":[239],"gives":[241],"best":[243],"results":[244],"considered":[249],"techniques.":[250],"However,":[251],"almost":[257],"comparable":[261],"due":[262],"nature":[265]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}