{"id":"https://openalex.org/W4404351534","doi":"https://doi.org/10.1145/3677052.3698648","title":"FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection","display_name":"FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection","publication_year":2024,"publication_date":"2024-11-14","ids":{"openalex":"https://openalex.org/W4404351534","doi":"https://doi.org/10.1145/3677052.3698648"},"language":"en","primary_location":{"id":"doi:10.1145/3677052.3698648","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698648","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698648","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698648","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100530683","display_name":"Junhong Lin","orcid":"https://orcid.org/0009-0002-9121-7098"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junhong Lin","raw_affiliation_strings":["Massachusetts Institute of Technology, US"],"raw_orcid":"https://orcid.org/0009-0002-9121-7098","affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, US","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101930629","display_name":"Xiaojie Guo","orcid":"https://orcid.org/0000-0002-1946-1179"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaojie Guo","raw_affiliation_strings":["IBM T.J. Watson Research Center, US"],"raw_orcid":"https://orcid.org/0000-0002-1946-1179","affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, US","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101792548","display_name":"Yada Zhu","orcid":"https://orcid.org/0000-0002-3338-6371"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yada Zhu","raw_affiliation_strings":["IBM T.J. Watson Research Center, US"],"raw_orcid":"https://orcid.org/0000-0002-3338-6371","affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, US","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Samuel Mitchell","orcid":"https://orcid.org/0009-0004-1978-7904"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samuel Mitchell","raw_affiliation_strings":["Massachusetts Institute of Technology, US"],"raw_orcid":"https://orcid.org/0009-0004-1978-7904","affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, US","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040828024","display_name":"Erik Altman","orcid":"https://orcid.org/0009-0001-0978-0360"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erik Altman","raw_affiliation_strings":["IBM T.J. Watson Research Center, US"],"raw_orcid":"https://orcid.org/0009-0001-0978-0360","affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, US","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051583907","display_name":"Julian Shun","orcid":"https://orcid.org/0000-0001-6163-6625"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Julian Shun","raw_affiliation_strings":["Massachusetts Institute of Technology, US"],"raw_orcid":"https://orcid.org/0000-0001-6163-6625","affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, US","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100530683"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":5.9604,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.96776786,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"292","last_page":"300"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9987999796867371,"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":0.9987999796867371,"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/T12519","display_name":"Cybercrime and Law Enforcement Studies","score":0.9761999845504761,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11838","display_name":"Crime, Illicit Activities, and Governance","score":0.9725000262260437,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49210017919540405},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4277758002281189},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11088168621063232},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10907119512557983},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.07823720574378967}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49210017919540405},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4277758002281189},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11088168621063232},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10907119512557983},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.07823720574378967}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3677052.3698648","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698648","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698648","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},{"id":"pmh:oai:dspace.mit.edu:1721.1/157762","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/157762","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/157762/1/3677052.3698648.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Association for Computing Machinery","raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"doi:10.1145/3677052.3698648","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698648","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698648","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G2935466353","display_name":null,"funder_award_id":"CCF-1845763, CCF-2316235, CCF-2403237","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4404351534.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W2295598076","https://openalex.org/W2348679751","https://openalex.org/W2613993493","https://openalex.org/W2897862648","https://openalex.org/W2924545743","https://openalex.org/W2945996535","https://openalex.org/W2950697450","https://openalex.org/W2964051675","https://openalex.org/W2970632477","https://openalex.org/W3012901223","https://openalex.org/W3016124664","https://openalex.org/W3021338900","https://openalex.org/W3087349682","https://openalex.org/W3097300053","https://openalex.org/W3108458441","https://openalex.org/W3126138172","https://openalex.org/W3133114748","https://openalex.org/W3172942063","https://openalex.org/W3201293162","https://openalex.org/W3209120126","https://openalex.org/W4205471456","https://openalex.org/W4205516974","https://openalex.org/W4210833811","https://openalex.org/W4213019189","https://openalex.org/W4214520160","https://openalex.org/W4281706128","https://openalex.org/W4287888710","https://openalex.org/W4306962648","https://openalex.org/W4310895557","https://openalex.org/W4317767731","https://openalex.org/W4323312758","https://openalex.org/W4378438608","https://openalex.org/W4382469817","https://openalex.org/W4393159712","https://openalex.org/W6775947557","https://openalex.org/W6785482172"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Fraud":[0],"detection":[1,92],"plays":[2],"a":[3,79],"crucial":[4],"role":[5],"in":[6,37,48,57,93,130],"the":[7,23,30,148],"financial":[8,12,70,94,159],"industry,":[9],"preventing":[10],"significant":[11],"losses.":[13],"Traditional":[14],"rule-based":[15],"systems":[16],"and":[17,29,65,82,103,118,122,138,180,185],"manual":[18],"audits":[19],"often":[20],"struggle":[21],"with":[22],"evolving":[24],"nature":[25],"of":[26,33,150,175],"fraud":[27,91],"schemes":[28],"vast":[31],"volume":[32],"transactions.":[34,124],"Recent":[35],"advances":[36],"machine":[38],"learning,":[39],"particularly":[40],"graph":[41,84],"neural":[42],"networks":[43],"(GNNs),":[44],"have":[45],"shown":[46],"promise":[47],"addressing":[49],"these":[50,74],"challenges.":[51],"However,":[52],"GNNs":[53],"still":[54],"face":[55],"limitations":[56],"learning":[58],"intricate":[59],"patterns,":[60],"effectively":[61],"utilizing":[62],"edge":[63,105],"attributes,":[64],"maintaining":[66],"efficiency":[67],"on":[68,155],"large":[69],"graphs.":[71,96],"To":[72],"address":[73],"limitations,":[75],"we":[76],"introduce":[77],"FraudGT,":[78],"simple,":[80],"effective,":[81],"efficient":[83],"transformer":[85],"(GT)":[86],"model":[87,126],"specifically":[88],"designed":[89],"for":[90],"transaction":[95],"FraudGT":[97,151,161],"leverages":[98],"edge-based":[99],"message":[100],"passing":[101],"gates":[102],"an":[104,173],"attribute-based":[106],"attention":[107],"bias":[108],"to":[109,113,143],"enhance":[110],"its":[111],"ability":[112],"discern":[114],"important":[115],"transactional":[116],"features":[117],"differentiate":[119],"between":[120],"normal":[121],"fraudulent":[123,132],"Our":[125,183],"achieves":[127],"state-of-the-art":[128],"performance":[129],"detecting":[131],"activities":[133],"while":[134,171],"demonstrating":[135],"high":[136],"throughput":[137,179],"significantly":[139],"lower":[140],"latency":[141],"compared":[142],"existing":[144],"methods.":[145],"We":[146],"validate":[147],"effectiveness":[149],"through":[152],"extensive":[153],"experiments":[154],"multiple":[156],"large-scale":[157],"synthetic":[158],"datasets.":[160],"consistently":[162],"outperforms":[163],"other":[164],"models,":[165],"achieving":[166],"7.8\u201317.8%":[167],"higher":[168],"F1":[169],"scores,":[170],"delivering":[172],"average":[174],"2.4":[176],"\u00d7":[177],"greater":[178],"reduced":[181],"latency.":[182],"code":[184],"datasets":[186],"are":[187],"available":[188],"at":[189],"https://github.com/junhongmit/FraudGT.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":15}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}