{"id":"https://openalex.org/W3006157661","doi":"https://doi.org/10.1109/uemcon47517.2019.8992993","title":"Machine Learning Pipeline for Fraud Detection and Prevention in E-Commerce Transactions","display_name":"Machine Learning Pipeline for Fraud Detection and Prevention in E-Commerce Transactions","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3006157661","doi":"https://doi.org/10.1109/uemcon47517.2019.8992993","mag":"3006157661"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon47517.2019.8992993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8992993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","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/A5019326992","display_name":"Resham Jhangiani","orcid":null},"institutions":[{"id":"https://openalex.org/I142934699","display_name":"California State University, Fullerton","ror":"https://ror.org/02avqqw26","country_code":"US","type":"education","lineage":["https://openalex.org/I142934699"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Resham Jhangiani","raw_affiliation_strings":["Department of Computer Science, California State University, Fullerton, California","California State University,Department of Computer Science,Fullerton,California,92831"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Fullerton, California","institution_ids":["https://openalex.org/I142934699"]},{"raw_affiliation_string":"California State University,Department of Computer Science,Fullerton,California,92831","institution_ids":["https://openalex.org/I142934699"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001331666","display_name":"Doina Bein","orcid":"https://orcid.org/0000-0002-5072-1979"},"institutions":[{"id":"https://openalex.org/I142934699","display_name":"California State University, Fullerton","ror":"https://ror.org/02avqqw26","country_code":"US","type":"education","lineage":["https://openalex.org/I142934699"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Doina Bein","raw_affiliation_strings":["Department of Computer Science, California State University, Fullerton, California","California State University,Department of Computer Science,Fullerton,California,92831"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Fullerton, California","institution_ids":["https://openalex.org/I142934699"]},{"raw_affiliation_string":"California State University,Department of Computer Science,Fullerton,California,92831","institution_ids":["https://openalex.org/I142934699"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086227422","display_name":"Abhishek Verma","orcid":"https://orcid.org/0000-0001-6687-4809"},"institutions":[{"id":"https://openalex.org/I89554219","display_name":"New Jersey City University","ror":"https://ror.org/0546wew42","country_code":"US","type":"education","lineage":["https://openalex.org/I89554219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhishek Verma","raw_affiliation_strings":["Department of Computer Science, New Jersey City University, Jersey City, NJ","New Jersey City University,Department of Computer Science,Jersey City,NJ,07305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, New Jersey City University, Jersey City, NJ","institution_ids":["https://openalex.org/I89554219"]},{"raw_affiliation_string":"New Jersey City University,Department of Computer Science,Jersey City,NJ,07305","institution_ids":["https://openalex.org/I89554219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8676,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.81836211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2019","issue":null,"first_page":"0135","last_page":"0140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9954000115394592,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9896000027656555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/credit-card-fraud","display_name":"Credit card fraud","score":0.8384043574333191},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7795636653900146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.67713463306427},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6066176891326904},{"id":"https://openalex.org/keywords/transaction-data","display_name":"Transaction data","score":0.5767447352409363},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5638290047645569},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.5405697822570801},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5251864194869995},{"id":"https://openalex.org/keywords/credit-card","display_name":"Credit card","score":0.5246275067329407},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.5012860298156738},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4931625723838806},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4813171625137329},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4228367209434509},{"id":"https://openalex.org/keywords/transaction-processing","display_name":"Transaction processing","score":0.4150509834289551},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.36591529846191406},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3150649070739746},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.18878987431526184},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.12100589275360107},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.1077374517917633}],"concepts":[{"id":"https://openalex.org/C2780747020","wikidata":"https://www.wikidata.org/wiki/Q83873","display_name":"Credit card fraud","level":4,"score":0.8384043574333191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7795636653900146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.67713463306427},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6066176891326904},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.5767447352409363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5638290047645569},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.5405697822570801},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5251864194869995},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.5246275067329407},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.5012860298156738},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4931625723838806},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4813171625137329},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4228367209434509},{"id":"https://openalex.org/C72108876","wikidata":"https://www.wikidata.org/wiki/Q844565","display_name":"Transaction processing","level":3,"score":0.4150509834289551},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.36591529846191406},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3150649070739746},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.18878987431526184},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.12100589275360107},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.1077374517917633},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/uemcon47517.2019.8992993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8992993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"},{"id":"mag:3085796436","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002214423384041","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1966493433","https://openalex.org/W2022223205","https://openalex.org/W2088402748","https://openalex.org/W2112627523","https://openalex.org/W2131809848","https://openalex.org/W2751596245","https://openalex.org/W2756359217","https://openalex.org/W2795873205","https://openalex.org/W3097977132","https://openalex.org/W6676660547","https://openalex.org/W6743838676"],"related_works":["https://openalex.org/W4224237387","https://openalex.org/W2483711049","https://openalex.org/W3150316110","https://openalex.org/W3153799676","https://openalex.org/W4313247660","https://openalex.org/W4367022971","https://openalex.org/W2984276143","https://openalex.org/W4281702918","https://openalex.org/W3157031617","https://openalex.org/W4291450055"],"abstract_inverted_index":{"Fraud":[0],"has":[1],"become":[2],"a":[3,9,32,73,89,171],"major":[4],"problem":[5,148],"in":[6,45,67],"e-commerce":[7,101],"and":[8,18,24,56,131,137,156,160],"lot":[10],"of":[11,35,43,88,110,133,149,158],"resources":[12],"are":[13,27,62,135,140,163],"being":[14,91,117],"invested":[15],"to":[16,29,64,84,106],"recognize":[17],"prevent":[19,30],"it.":[20],"Present":[21],"fraud":[22,54],"detection":[23,55],"prevention":[25,57],"systems":[26],"designed":[28],"only":[31],"small":[33],"fraction":[34],"fraudulent":[36,92],"transactions":[37,61],"processed,":[38],"which":[39],"still":[40],"costs":[41],"billions":[42],"dollars":[44],"loss.":[46],"There":[47],"is":[48,152,168],"an":[49],"urgent":[50],"need":[51],"for":[52],"better":[53],"as":[58],"the":[59,68,86,108,115,144,147,166],"online":[60],"estimated":[63],"increase":[65],"substantially":[66],"coming":[69],"year.":[70],"We":[71],"propose":[72],"data":[74,83,105,161],"driven":[75],"model":[76,96,167],"using":[77],"machine":[78,120],"learning":[79,121],"algorithms":[80,122],"on":[81,99,170],"big":[82],"predict":[85,107],"probability":[87,109],"transaction":[90,104,113],"or":[93],"legitimate.":[94],"The":[95],"was":[97],"trained":[98,169],"historical":[100],"credit":[102],"card":[103],"any":[111],"future":[112],"by":[114],"customer":[116],"fraudulent.":[118],"Supervised":[119],"like":[123],"Random":[124],"Forest,":[125],"Support":[126],"Vector":[127],"Machine,":[128],"Gradient":[129],"Boost":[130],"combinations":[132],"these":[134],"implemented":[136],"their":[138],"performance":[139],"compared.":[141],"While":[142],"at":[143],"same":[145],"time":[146],"class":[150],"imbalance":[151],"taken":[153],"into":[154],"consideration":[155],"techniques":[157],"oversampling":[159],"pre-processing":[162],"performed":[164],"before":[165],"classifier.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}