{"id":"https://openalex.org/W7102548530","doi":"https://doi.org/10.25300/misq/2025/18080","title":"Latent Similarity-Enhanced Credit Risk Prediction","display_name":"Latent Similarity-Enhanced Credit Risk Prediction","publication_year":2025,"publication_date":"2025-10-31","ids":{"openalex":"https://openalex.org/W7102548530","doi":"https://doi.org/10.25300/misq/2025/18080"},"language":"en","primary_location":{"id":"doi:10.25300/misq/2025/18080","is_oa":false,"landing_page_url":"https://doi.org/10.25300/misq/2025/18080","pdf_url":null,"source":{"id":"https://openalex.org/S57293258","display_name":"MIS Quarterly","issn_l":"0276-7783","issn":["0276-7783","2162-9730"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4327875293","host_organization_name":"MIS Quarterly","host_organization_lineage":["https://openalex.org/P4327875293"],"host_organization_lineage_names":["MIS Quarterly"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MIS Quarterly","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":null,"display_name":"Hongzhe Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongzhe Zhang","raw_affiliation_strings":["Shenzhen Finance Institute, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Finance Institute, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Qian","raw_affiliation_strings":["Department of Applied Economics and Statistics, University of Delaware, Newark, DE, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Economics and Statistics, University of Delaware, Newark, DE, U.S.A","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xiao Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao Fang","raw_affiliation_strings":["Lerner College of Business and Economics, University of Delaware, Newark, DE, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lerner College of Business and Economics, University of Delaware, Newark, DE, U.S.A","institution_ids":["https://openalex.org/I86501945"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60547163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"2","first_page":"731","last_page":"766"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9383000135421753,"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":0.9383000135421753,"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":0.017799999564886093,"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":0.01269999984651804,"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/credit-risk","display_name":"Credit risk","score":0.7538999915122986},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.5450999736785889},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.510699987411499},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5084999799728394},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.504800021648407},{"id":"https://openalex.org/keywords/credit-history","display_name":"Credit history","score":0.44519999623298645},{"id":"https://openalex.org/keywords/credit-valuation-adjustment","display_name":"Credit valuation adjustment","score":0.3693000078201294},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.34119999408721924},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3151000142097473}],"concepts":[{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.7538999915122986},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.5436999797821045},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.510699987411499},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5084999799728394},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5055999755859375},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.504800021648407},{"id":"https://openalex.org/C68842666","wikidata":"https://www.wikidata.org/wiki/Q1070699","display_name":"Credit history","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.375900000333786},{"id":"https://openalex.org/C89997419","wikidata":"https://www.wikidata.org/wiki/Q2828882","display_name":"Credit valuation adjustment","level":4,"score":0.3693000078201294},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3659999966621399},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3151000142097473},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C197142791","wikidata":"https://www.wikidata.org/wiki/Q540626","display_name":"Bond market","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C31348098","wikidata":"https://www.wikidata.org/wiki/Q3423719","display_name":"Credit enhancement","level":4,"score":0.30149999260902405},{"id":"https://openalex.org/C205208723","wikidata":"https://www.wikidata.org/wiki/Q372765","display_name":"Credit rating","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2777138686","wikidata":"https://www.wikidata.org/wiki/Q1787103","display_name":"Credit score","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C24308983","wikidata":"https://www.wikidata.org/wiki/Q5183781","display_name":"Credit reference","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C76073288","wikidata":"https://www.wikidata.org/wiki/Q1337875","display_name":"Financial risk","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.26460000872612},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26100000739097595},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C43071985","wikidata":"https://www.wikidata.org/wiki/Q2906013","display_name":"Financial risk management","level":3,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.25300/misq/2025/18080","is_oa":false,"landing_page_url":"https://doi.org/10.25300/misq/2025/18080","pdf_url":null,"source":{"id":"https://openalex.org/S57293258","display_name":"MIS Quarterly","issn_l":"0276-7783","issn":["0276-7783","2162-9730"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4327875293","host_organization_name":"MIS Quarterly","host_organization_lineage":["https://openalex.org/P4327875293"],"host_organization_lineage_names":["MIS Quarterly"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MIS Quarterly","raw_type":"journal-article"},{"id":"pmh:oai:aisel.aisnet.org:misq-4058","is_oa":false,"landing_page_url":"https://aisel.aisnet.org/misq/vol50/iss2/16","pdf_url":null,"source":{"id":"https://openalex.org/S30879505","display_name":"Journal of the Association for Information Systems","issn_l":"1536-9323","issn":["1536-9323"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310321080","host_organization_name":"Association for Information Systems","host_organization_lineage":["https://openalex.org/P4310321080"],"host_organization_lineage_names":["Association for Information Systems"],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Management Information Systems Quarterly","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W34646664","https://openalex.org/W39040241","https://openalex.org/W430137803","https://openalex.org/W1626921030","https://openalex.org/W1678356000","https://openalex.org/W1964357740","https://openalex.org/W1980770954","https://openalex.org/W1982120517","https://openalex.org/W1985546543","https://openalex.org/W1988790447","https://openalex.org/W2029864452","https://openalex.org/W2045516668","https://openalex.org/W2046696485","https://openalex.org/W2051455168","https://openalex.org/W2051731935","https://openalex.org/W2106393550","https://openalex.org/W2130354913","https://openalex.org/W2131816657","https://openalex.org/W2139212933","https://openalex.org/W2142513087","https://openalex.org/W2158457715","https://openalex.org/W2168123127","https://openalex.org/W2194059763","https://openalex.org/W2250309026","https://openalex.org/W2295598076","https://openalex.org/W2585847175","https://openalex.org/W2748025215","https://openalex.org/W2787894218","https://openalex.org/W2941259227","https://openalex.org/W2951761234","https://openalex.org/W2985066636","https://openalex.org/W3113307902","https://openalex.org/W3121588992","https://openalex.org/W3123591829","https://openalex.org/W3166283319","https://openalex.org/W3173450313","https://openalex.org/W3197032092","https://openalex.org/W4212883601","https://openalex.org/W4249748580","https://openalex.org/W4391114268","https://openalex.org/W4411021492","https://openalex.org/W4413427422","https://openalex.org/W4414348562"],"related_works":[],"abstract_inverted_index":{"Given":[0],"the":[1,5,10,22,36,58,119,130,149,180,195,222],"sheer":[2],"size":[3],"of":[4,13,24,52,60,124,129,199,209,224],"consumer":[6,14,25,37],"credit":[7,15,17,26,38,41,53,61,81,113,171,184,210],"market":[8],"and":[9,49,54,75,95,132,157,161,182],"huge":[11],"number":[12],"users,":[16,156],"risk":[18,42,82,114,172,185,211],"prediction,":[19,83],"or":[20],"predicting":[21],"probability":[23],"delinquency":[27],"(or":[28],"default),":[29],"has":[30],"become":[31],"a":[32,110,122,127,140,144,206,228],"critical":[33],"problem":[34],"in":[35,47],"industry.":[39],"Effective":[40],"prediction":[43,115,186,212],"aids":[44],"financial":[45],"institutions":[46],"granting":[48],"managing":[50],"extensions":[51],"can":[55],"help":[56],"secure":[57],"availability":[59],"for":[62,79,99,143,170,205],"worthy":[63],"applicants.":[64],"While":[65],"it":[66],"is":[67],"desirable":[68],"to":[69,97,179],"employ":[70],"both":[71,159],"users\u2019":[72],"intrinsic":[73,168],"characteristics":[74,94,169],"similarities":[76,89,101,134,154,163],"among":[77,102,155,164],"them":[78],"effective":[80],"existing":[84],"studies":[85],"rely":[86],"solely":[87],"on":[88],"derived":[90],"from":[91,221],"their":[92,167],"observed":[93,131,160],"fail":[96],"account":[98],"unobserved":[100],"them.":[103,136],"To":[104],"address":[105],"this":[106],"challenge,":[107],"we":[108],"propose":[109],"latent":[111,133,153,162],"similarity-enhanced":[112],"model,":[116],"which":[117],"operationalizes":[118],"similarity":[120],"between":[121,135],"pair":[123],"users":[125,165],"as":[126],"combination":[128],"We":[137,174,214],"then":[138],"present":[139],"new":[141,145],"design":[142],"method":[146,178,201,226],"that":[147],"estimates":[148],"model":[150],"parameters,":[151],"learns":[152],"integrates":[158],"with":[166,191],"prediction.":[173],"further":[175],"extend":[176],"our":[177,200,225],"multiclass":[181],"numerical":[183],"problems.":[187,213],"Extensive":[188],"empirical":[189],"evaluations":[190],"real-world":[192],"data":[193],"demonstrate":[194],"superior":[196],"predictive":[197],"power":[198],"over":[202],"benchmark":[203],"methods":[204],"broad":[207],"spectrum":[208],"also":[215],"show":[216],"substantial":[217],"economic":[218],"value":[219],"generated":[220],"superiority":[223],"through":[227],"case":[229],"study.":[230]},"counts_by_year":[],"updated_date":"2026-06-02T06:17:35.589633","created_date":"2025-10-31T00:00:00"}