{"id":"https://openalex.org/W3114507257","doi":"https://doi.org/10.1155/2020/6685888","title":"Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data","display_name":"Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data","publication_year":2020,"publication_date":"2020-12-23","ids":{"openalex":"https://openalex.org/W3114507257","doi":"https://doi.org/10.1155/2020/6685888","mag":"3114507257"},"language":"en","primary_location":{"id":"doi:10.1155/2020/6685888","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/6685888","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/6685888.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://downloads.hindawi.com/journals/complexity/2020/6685888.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100725671","display_name":"Jian Liu","orcid":"https://orcid.org/0000-0002-4205-773X"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jian Liu","raw_affiliation_strings":["School of Business, Sichuan University, Chengdu 610064, China"],"affiliations":[{"raw_affiliation_string":"School of Business, Sichuan University, Chengdu 610064, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081048476","display_name":"Xin Gu","orcid":"https://orcid.org/0000-0002-7724-9379"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Gu","raw_affiliation_strings":["Chengdu Soft Innovation Intelligence Association, Chengdu 610023, China","School of Business, Sichuan University, Chengdu 610064, China"],"affiliations":[{"raw_affiliation_string":"Chengdu Soft Innovation Intelligence Association, Chengdu 610023, China","institution_ids":[]},{"raw_affiliation_string":"School of Business, Sichuan University, Chengdu 610064, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029041038","display_name":"Chao Shang","orcid":"https://orcid.org/0000-0003-3905-4631"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Shang","raw_affiliation_strings":["Chengdu Soft Innovation Intelligence Association, Chengdu 610023, China","Institute of New Structural Economics, Peking University, Beijing 100871, China"],"affiliations":[{"raw_affiliation_string":"Chengdu Soft Innovation Intelligence Association, Chengdu 610023, China","institution_ids":[]},{"raw_affiliation_string":"Institute of New Structural Economics, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100725671"],"corresponding_institution_ids":["https://openalex.org/I24185976"],"apc_list":{"value":2300,"currency":"USD","value_usd":2300},"apc_paid":{"value":2300,"currency":"USD","value_usd":2300},"fwci":0.9515,"has_fulltext":true,"cited_by_count":44,"citation_normalized_percentile":{"value":0.81792492,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"2020","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.975600004196167,"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":0.975600004196167,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9067999720573425,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.8346109986305237},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6586113572120667},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6140193343162537},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5831906199455261},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.537856936454773},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5119841694831848},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5027294158935547},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4984757900238037},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4316239655017853},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38982462882995605},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3676952123641968}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8346109986305237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6586113572120667},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6140193343162537},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5831906199455261},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.537856936454773},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5119841694831848},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5027294158935547},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4984757900238037},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4316239655017853},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38982462882995605},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3676952123641968},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1155/2020/6685888","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/6685888","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/6685888.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:hin:complx:6685888","is_oa":false,"landing_page_url":"http://downloads.hindawi.com/journals/8503/2020/6685888.xml","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"article"},{"id":"pmh:oai:doaj.org/article:6fa2fe8ba4b04d24b096726cccfc4419","is_oa":true,"landing_page_url":"https://doaj.org/article/6fa2fe8ba4b04d24b096726cccfc4419","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Complexity, Vol 2020 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2020/6685888","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/6685888","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/6685888.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322990","display_name":"Sichuan University","ror":"https://ror.org/011ashp19"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3114507257.pdf","grobid_xml":"https://content.openalex.org/works/W3114507257.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W801314802","https://openalex.org/W2041298291","https://openalex.org/W2059981748","https://openalex.org/W2157588664","https://openalex.org/W2326700910","https://openalex.org/W2563234394","https://openalex.org/W2599582935","https://openalex.org/W2722297332","https://openalex.org/W2746167089","https://openalex.org/W2777154466","https://openalex.org/W2778740557","https://openalex.org/W2787942974","https://openalex.org/W2788529488","https://openalex.org/W2792513524","https://openalex.org/W2805200330","https://openalex.org/W2819912438","https://openalex.org/W2820494545","https://openalex.org/W2890672150","https://openalex.org/W2897254430","https://openalex.org/W2901420601","https://openalex.org/W2907281275","https://openalex.org/W2987434786","https://openalex.org/W3123902598"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W4233347783","https://openalex.org/W2910064364","https://openalex.org/W4255224757","https://openalex.org/W1996690921"],"abstract_inverted_index":{"At":[0,82],"present,":[1],"there":[2],"are":[3,19,34,73,139],"more":[4,6],"and":[5,14,25,45,121,143,166,199],"frauds":[7,18],"in":[8,49,86],"the":[9,50,71,77,80,83,90,93,99,103,107,160,164,169,174,178,193,197,202],"financial":[10,17,29,64,145,181],"field.":[11],"The":[12],"detection":[13,61,203,207],"prevention":[15],"of":[16,20,38,52,79,102,163,173,180,196,205],"great":[21],"significance":[22],"for":[23,63],"regulating":[24],"maintaining":[26],"a":[27,59,150],"reasonable":[28],"order.":[30],"Deep":[31],"learning":[32],"algorithms":[33],"widely":[35],"used":[36,74,112,140],"because":[37],"their":[39],"high":[40],"recognition":[41],"rate,":[42],"good":[43],"robustness,":[44],"strong":[46],"implementation.":[47],"Therefore,":[48],"context":[51],"e-commerce":[53],"big":[54],"data,":[55,165,175,198],"this":[56,188],"paper":[57],"proposes":[58],"quantitative":[60],"algorithm":[62],"fraud":[65,182,206],"based":[66],"on":[67],"deep":[68,151],"learning.":[69],"First,":[70],"encoders":[72],"to":[75,88,98,113,130,141],"extract":[76],"features":[78,115],"behaviour.":[81],"same":[84],"time,":[85],"order":[87],"reduce":[89],"computational":[91],"complexity,":[92],"feature":[94,122,132],"extraction":[95],"is":[96,111,124],"restricted":[97],"space-time":[100],"volume":[101],"dense":[104],"trajectory.":[105],"Second,":[106],"neural":[108,152],"network":[109,153],"model":[110,154],"transform":[114],"into":[116],"behavioural":[117],"visual":[118],"word":[119],"representations,":[120],"fusion":[123],"performed":[125],"using":[126],"weighted":[127],"correlation":[128],"methods":[129],"improve":[131,201],"classification":[133],"capabilities.":[134],"Finally,":[135],"sparse":[136],"reconstruction":[137],"errors":[138],"judge":[142],"detect":[144],"fraud.":[146],"This":[147],"method":[148,189],"builds":[149],"with":[155],"multiple":[156],"hidden":[157],"layers,":[158],"learns":[159],"characteristic":[161],"expression":[162],"fully":[167],"depicts":[168],"rich":[170],"internal":[171],"information":[172],"thereby":[176],"improving":[177],"accuracy":[179],"detection.":[183],"Experimental":[184],"results":[185],"show":[186],"that":[187],"can":[190],"effectively":[191],"learn":[192],"essential":[194],"characteristics":[195],"significantly":[200],"rate":[204],"algorithms.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}