{"id":"https://openalex.org/W4401915972","doi":"https://doi.org/10.1080/08839514.2024.2385854","title":"Fraud Detection Based on Credit Review Texts with Dual Channel Memory Networks","display_name":"Fraud Detection Based on Credit Review Texts with Dual Channel Memory Networks","publication_year":2024,"publication_date":"2024-08-27","ids":{"openalex":"https://openalex.org/W4401915972","doi":"https://doi.org/10.1080/08839514.2024.2385854"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2024.2385854","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2024.2385854","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1080/08839514.2024.2385854","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115589343","display_name":"Yansong Wang","orcid":"https://orcid.org/0009-0004-0013-0785"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yansong Wang","raw_affiliation_strings":["School of Data Science, University of Science and Technology of China, Hefei, China","Science and Technology Innovation Center, Chery Huiyin Motor Finance Service Co, Ltd, Wuhu, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Science and Technology Innovation Center, Chery Huiyin Motor Finance Service Co, Ltd, Wuhu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085254654","display_name":"Defu Lian","orcid":"https://orcid.org/0000-0002-3507-9607"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Defu Lian","raw_affiliation_strings":["School of Data Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048237545","display_name":"Enhong Chen","orcid":"https://orcid.org/0000-0002-4835-4102"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Enhong Chen","raw_affiliation_strings":["School of Data Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048237545"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.65984459,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"38","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9991000294685364,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9801999926567078,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9715999960899353,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8807733058929443},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6675636768341064},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4977908134460449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3474389314651489},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2547506093978882}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8807733058929443},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6675636768341064},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4977908134460449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3474389314651489},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2547506093978882},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2024.2385854","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2024.2385854","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6ca53e75398f4bc5a1ce0743e2864693","is_oa":true,"landing_page_url":"https://doaj.org/article/6ca53e75398f4bc5a1ce0743e2864693","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 38, Iss 1 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2024.2385854","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2024.2385854","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6600000262260437,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W2045049630","https://openalex.org/W2070516337","https://openalex.org/W2070941356","https://openalex.org/W2116502002","https://openalex.org/W2175545203","https://openalex.org/W2513319573","https://openalex.org/W2525596522","https://openalex.org/W2548532770","https://openalex.org/W2571178338","https://openalex.org/W2756359217","https://openalex.org/W2891675550","https://openalex.org/W2906115686","https://openalex.org/W2945876440","https://openalex.org/W2971446702","https://openalex.org/W3033991488","https://openalex.org/W3089258356","https://openalex.org/W3104890489","https://openalex.org/W3173753074","https://openalex.org/W3174820758","https://openalex.org/W4200149057","https://openalex.org/W4200303031","https://openalex.org/W4285248211","https://openalex.org/W4286009083","https://openalex.org/W4293095110","https://openalex.org/W4295733249","https://openalex.org/W4312702896","https://openalex.org/W4313307293","https://openalex.org/W4317040541","https://openalex.org/W4367598380","https://openalex.org/W4367722760","https://openalex.org/W4386088991","https://openalex.org/W4386167370","https://openalex.org/W4386724905","https://openalex.org/W4386965067","https://openalex.org/W4387664158","https://openalex.org/W4387861645","https://openalex.org/W4388099759","https://openalex.org/W4388333266","https://openalex.org/W4389562446","https://openalex.org/W4389687725","https://openalex.org/W4391991943","https://openalex.org/W4396521123","https://openalex.org/W4398198485"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W2952912015"],"abstract_inverted_index":{"With":[0],"the":[1,5,33,48,64,120,138,155,160,164,177,190],"rapid":[2],"development":[3],"of":[4,50,58,180,192],"automotive":[6,52],"finance":[7,53],"market":[8],"in":[9,195],"China,":[10],"fraudulent":[11],"behaviors":[12],"present":[13],"new":[14,42],"characteristics":[15],"such":[16],"as":[17],"intellectualization":[18],"and":[19,25,111,137,150,169],"high":[20],"concealment.":[21],"Graph":[22],"neural":[23],"networks":[24,27],"memory":[26,106,113,134,140],"have":[28,81],"strong":[29],"capabilities":[30],"for":[31,44,77,123],"processing":[32],"textual":[34],"data":[35,67,122],"containing":[36],"massive":[37],"complex":[38],"associations,":[39],"providing":[40],"a":[41,55,95,172],"perspective":[43],"fraud":[45,91,96,182,196],"detection.":[46,197],"During":[47],"operation":[49],"an":[51],"company,":[54],"large":[56],"amount":[57],"credit":[59,101,124,156],"review":[60,102,125,157],"texts":[61,71,103],"with":[62,104,186],"recording":[63],"customers\u2019":[65],"multidimensional":[66],"are":[68,143,167],"accumulated.":[69],"These":[70],"contain":[72],"information":[73],"that":[74],"is":[75,127],"helpful":[76],"risk":[78],"management,":[79],"but":[80],"not":[82],"been":[83],"well":[84],"explored.":[85],"In":[86],"order":[87],"to":[88,146,154,175],"effectively":[89],"identify":[90],"risks,":[92],"we":[93],"propose":[94],"detection":[97],"method":[98],"based":[99],"on":[100],"dual-channel":[105],"network,":[107],"which":[108],"combines":[109],"graph":[110,133,148],"text":[112,121,126,139,151],"networks.":[114],"By":[115],"utilizing":[116],"pre-trained":[117],"language":[118],"models,":[119],"encoded":[128],"into":[129,171],"semantic":[130],"vectors.":[131],"The":[132],"network":[135,141,174],"module":[136,142],"then":[144],"employed":[145],"extract":[147],"features":[149,152],"corresponding":[153],"text.":[158],"Finally,":[159],"generated":[161],"results":[162],"from":[163],"three":[165],"modules":[166],"fused":[168],"input":[170],"classification":[173],"obtain":[176],"final":[178],"determination":[179],"financial":[181],"risk.":[183],"Comparative":[184],"experiments":[185],"baseline":[187],"models":[188],"demonstrate":[189],"validity":[191],"our":[193],"model":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}