{"id":"https://openalex.org/W4318148072","doi":"https://doi.org/10.1109/bigdata55660.2022.10021010","title":"DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs","display_name":"DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318148072","doi":"https://doi.org/10.1109/bigdata55660.2022.10021010"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10021010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10021010","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5088638517","display_name":"Henrique S. Assump\u00e7\u00e3o","orcid":"https://orcid.org/0009-0001-8747-246X"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Henrique S. Assumpcao","raw_affiliation_strings":["Universidade Federal de Minas Gerais"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112456364","display_name":"Fabr\u00edcio De Souza","orcid":null},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Fabricio Souza","raw_affiliation_strings":["Universidade Federal de Minas Gerais"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072327617","display_name":"Leandro Lacerda Campos","orcid":null},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leandro Lacerda Campos","raw_affiliation_strings":["Universidade Federal de Minas Gerais,InterMind (Inter&#x0026;Co)"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais,InterMind (Inter&#x0026;Co)","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026755506","display_name":"Vinicius T. de Castro Pires","orcid":null},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vinicius T. de Castro Pires","raw_affiliation_strings":["Universidade Federal de Minas Gerais,InterMind (Inter&#x0026;Co)"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais,InterMind (Inter&#x0026;Co)","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032398159","display_name":"Paulo M. Laurentys de Almeida","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paulo M. Laurentys de Almeida","raw_affiliation_strings":["InterMind (Inter&#x0026;Co)"],"affiliations":[{"raw_affiliation_string":"InterMind (Inter&#x0026;Co)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021734616","display_name":"Fabr\u00edcio Murai","orcid":"https://orcid.org/0000-0003-4487-6381"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]},{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR","US"],"is_corresponding":false,"raw_author_name":"Fabricio Murai","raw_affiliation_strings":["Universidade Federal de Minas Gerais,Worcester Polytechnic Institute","Worcester Polytechnic Institute, Universidade Federal de Minas Gerais"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais,Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]},{"raw_affiliation_string":"Worcester Polytechnic Institute, Universidade Federal de Minas Gerais","institution_ids":["https://openalex.org/I107077323","https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5088638517"],"corresponding_institution_ids":["https://openalex.org/I110200422"],"apc_list":null,"apc_paid":null,"fwci":1.7415,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88185185,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"709","last_page":"714"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11838","display_name":"Crime, Illicit Activities, and Governance","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T11838","display_name":"Crime, Illicit Activities, and Governance","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9244999885559082,"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9070000052452087,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/money-laundering","display_name":"Money laundering","score":0.9301201105117798},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7686210870742798},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.6814324855804443},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6212687492370605},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5370503664016724},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.534274160861969},{"id":"https://openalex.org/keywords/financial-fraud","display_name":"Financial fraud","score":0.4959357678890228},{"id":"https://openalex.org/keywords/financial-transaction","display_name":"Financial transaction","score":0.46388301253318787},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4492555260658264},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.426637202501297},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4254581332206726},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41813647747039795},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4122988283634186},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.2149885594844818},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.18261364102363586},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1711815893650055},{"id":"https://openalex.org/keywords/accounting","display_name":"Accounting","score":0.138411283493042},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10031971335411072}],"concepts":[{"id":"https://openalex.org/C2780005421","wikidata":"https://www.wikidata.org/wiki/Q151900","display_name":"Money laundering","level":2,"score":0.9301201105117798},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7686210870742798},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.6814324855804443},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6212687492370605},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5370503664016724},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.534274160861969},{"id":"https://openalex.org/C2985140798","wikidata":"https://www.wikidata.org/wiki/Q28813","display_name":"Financial fraud","level":2,"score":0.4959357678890228},{"id":"https://openalex.org/C164516710","wikidata":"https://www.wikidata.org/wiki/Q1166072","display_name":"Financial transaction","level":3,"score":0.46388301253318787},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4492555260658264},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.426637202501297},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4254581332206726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41813647747039795},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4122988283634186},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2149885594844818},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.18261364102363586},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1711815893650055},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.138411283493042},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10031971335411072},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10021010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10021010","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W243790863","https://openalex.org/W2148143831","https://openalex.org/W2529210213","https://openalex.org/W2612690371","https://openalex.org/W3035403290","https://openalex.org/W3134509497","https://openalex.org/W3201293162","https://openalex.org/W4282975877","https://openalex.org/W4297733535","https://openalex.org/W4318148072","https://openalex.org/W6609267553","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6740667046","https://openalex.org/W6745609711"],"related_works":["https://openalex.org/W3146489226","https://openalex.org/W2550662705","https://openalex.org/W135644120","https://openalex.org/W2268172546","https://openalex.org/W2152118558","https://openalex.org/W2084128363","https://openalex.org/W4383683699","https://openalex.org/W2611827299","https://openalex.org/W1967476065","https://openalex.org/W2109067458"],"abstract_inverted_index":{"Money":[0],"laundering":[1,69,86],"has":[2],"become":[3],"one":[4],"of":[5,55,123,162],"the":[6,31,52,117,160,168,177],"most":[7],"relevant":[8],"criminal":[9],"activities":[10,28,87],"in":[11],"modern":[12],"societies,":[13],"as":[14],"it":[15,35],"causes":[16],"massive":[17],"financial":[18,38],"losses":[19],"for":[20,66,83,106,131],"governments,":[21],"banks":[22],"and":[23,46,103,120],"other":[24],"institutions.":[25],"Detecting":[26],"such":[27],"is":[29,62],"among":[30,167],"top":[32],"priorities":[33],"when":[34],"comes":[36],"to":[37,51,57,71,126,150,159,176],"analysis,":[39],"but":[40],"current":[41],"approaches":[42],"are":[43],"often":[44],"costly":[45],"labor":[47],"intensive":[48],"partly":[49],"due":[50],"sheer":[53],"amount":[54],"data":[56],"be":[58],"analyzed.":[59],"Hence,":[60],"there":[61],"a":[63,80],"growing":[64],"need":[65],"automatic":[67],"anti-money":[68],"systems":[70],"assist":[72],"experts.":[73],"In":[74],"this":[75],"work,":[76],"we":[77],"propose":[78],"DELATOR,":[79],"novel":[81],"framework":[82,119],"detecting":[84],"money":[85],"based":[88],"on":[89],"graph":[90,111],"neural":[91],"networks":[92],"that":[93,157],"learn":[94],"from":[95,108,116,143],"large-scale":[96],"temporal":[97],"graphs.":[98],"DELATOR":[99,134],"provides":[100],"an":[101,140],"effective":[102],"efficient":[104],"method":[105],"learning":[107,125],"heavily":[109],"imbalanced":[110],"data,":[112],"by":[113,146],"adapting":[114],"concepts":[115],"GraphSMOTE":[118],"incorporating":[121],"elements":[122],"multi-task":[124],"obtain":[127],"rich":[128],"node":[129,132],"embeddings":[130],"classification.":[133],"outperforms":[135],"all":[136],"considered":[137],"baselines,":[138],"including":[139],"off-the-shelf":[141],"solution":[142],"Amazon":[144],"AWS":[145],"23%":[147],"with":[148],"respect":[149],"AUC-ROC.":[151],"We":[152],"also":[153],"conducted":[154],"real":[155],"experiments":[156],"led":[158],"discovery":[161],"7":[163],"new":[164],"suspicious":[165],"cases":[166],"50":[169],"analyzed":[170],"ones,":[171],"which":[172],"have":[173],"been":[174],"reported":[175],"authorities.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}
