{"id":"https://openalex.org/W7152664287","doi":"https://doi.org/10.1109/icecet63943.2025.11472005","title":"A Comparative Study of Explainable Machine Learning Models for Corporate Credit Scoring","display_name":"A Comparative Study of Explainable Machine Learning Models for Corporate Credit Scoring","publication_year":2025,"publication_date":"2025-07-03","ids":{"openalex":"https://openalex.org/W7152664287","doi":"https://doi.org/10.1109/icecet63943.2025.11472005"},"language":null,"primary_location":{"id":"doi:10.1109/icecet63943.2025.11472005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet63943.2025.11472005","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET)","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/A5091434120","display_name":"LihChyun Shu","orcid":"https://orcid.org/0000-0003-4125-2020"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"LihChyun Shu","raw_affiliation_strings":["National Cheng Kung University,Institute of Finance,Department of Accountancy and Graduate,Tainan,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Cheng Kung University,Institute of Finance,Department of Accountancy and Graduate,Tainan,Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133263042","display_name":"ChiaCheng Su","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129986","display_name":"Taiwan Clinical Oncology Research Foundation","ror":"https://ror.org/02wz95e76","country_code":"TW","type":"other","lineage":["https://openalex.org/I4210129986"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"ChiaCheng Su","raw_affiliation_strings":["Accounting Development and Research Foundation,Research Division,Taipei,Taiwan"],"affiliations":[{"raw_affiliation_string":"Accounting Development and Research Foundation,Research Division,Taipei,Taiwan","institution_ids":["https://openalex.org/I4210129986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105098120","display_name":"Joseph Shu","orcid":"https://orcid.org/0009-0006-2644-2917"},"institutions":[{"id":"https://openalex.org/I4210139985","display_name":"Medicover","ror":"https://ror.org/03zrqt452","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210139985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Joseph Shu","raw_affiliation_strings":["Grover Deutschland GmbH,Berlin,Germany"],"affiliations":[{"raw_affiliation_string":"Grover Deutschland GmbH,Berlin,Germany","institution_ids":["https://openalex.org/I4210139985"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091434120"],"corresponding_institution_ids":["https://openalex.org/I91807558"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88468411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.6373000144958496,"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":0.6373000144958496,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2134999930858612,"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":0.02019999921321869,"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/feature","display_name":"Feature (linguistics)","score":0.2881999909877777},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2653000056743622},{"id":"https://openalex.org/keywords/credit-risk","display_name":"Credit risk","score":0.2508000135421753},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2296999990940094},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.21060000360012054}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.629800021648407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5501999855041504},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5498999953269958},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.2508000135421753},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2296999990940094},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.21060000360012054},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.20239999890327454},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.20069999992847443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icecet63943.2025.11472005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet63943.2025.11472005","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5704138278961182,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2516809705","https://openalex.org/W2788403449","https://openalex.org/W2891503716","https://openalex.org/W2895269073","https://openalex.org/W2963125461","https://openalex.org/W2981731882","https://openalex.org/W3013460382","https://openalex.org/W3083961251","https://openalex.org/W3108895775","https://openalex.org/W3109267637","https://openalex.org/W3141039006","https://openalex.org/W3151772276","https://openalex.org/W3205154884","https://openalex.org/W4401011076"],"related_works":[],"abstract_inverted_index":{"Loans":[0],"are":[1],"a":[2,131],"key":[3,107],"revenue":[4],"source":[5],"for":[6,98],"banks,":[7],"requiring":[8],"accurate":[9],"credit":[10,20,119],"rating":[11],"to":[12,34,65,80,93],"minimize":[13],"default":[14],"risk.":[15],"While":[16],"machine":[17],"learning":[18],"enhances":[19],"scoring,":[21],"its":[22],"complexity":[23],"often":[24],"compromises":[25],"interpretability.":[26],"Explainable":[27],"artificial":[28],"intelligence":[29],"(XAI)":[30],"methods":[31,74],"have":[32],"emerged":[33],"address":[35],"this":[36],"challenge,":[37],"yet":[38],"limited":[39],"research":[40],"simultaneously":[41],"considers":[42],"both":[43],"predictive":[44],"accuracy":[45],"and":[46,86],"interpretability":[47],"while":[48,110],"aligning":[49],"with":[50,124],"loan":[51,99,115],"officers'":[52],"practical":[53],"needs.":[54],"This":[55],"study":[56],"focuses":[57],"on:":[58],"(1)":[59],"the":[60,70,82,88,125],"application":[61],"of":[62,72,90],"XAI":[63],"techniques":[64],"enhance":[66],"model":[67],"transparency,":[68],"(2)":[69],"evaluation":[71],"these":[73],"based":[75],"on":[76],"frontline":[77],"professionals\u2019":[78],"feedback":[79],"identify":[81],"most":[83],"effective":[84],"explanations,":[85],"(3)":[87],"integration":[89],"explanatory":[91],"insights":[92],"provide":[94],"actionable":[95],"financial":[96,108,129],"guidance":[97],"applicants.":[100],"Results":[101],"indicate":[102],"that":[103],"SHAP":[104],"effectively":[105],"identifies":[106],"features,":[109],"LIME\u2019s":[111],"probability-based":[112],"approach":[113],"aids":[114],"officers":[116],"in":[117],"justifying":[118],"decisions.":[120],"Moreover,":[121],"combining":[122],"LIME":[123],"Anchors":[126],"method":[127],"strengthens":[128],"recommendations,":[130],"strategy":[132],"well":[133],"received":[134],"by":[135],"practitioners.":[136]},"counts_by_year":[],"updated_date":"2026-04-11T06:13:24.991567","created_date":"2026-04-10T00:00:00"}
