{"id":"https://openalex.org/W4410005308","doi":"https://doi.org/10.1007/s10791-025-09549-7","title":"Dynamic quantification anti-fraud machine learning model for real-time transaction fraud detection in banking","display_name":"Dynamic quantification anti-fraud machine learning model for real-time transaction fraud detection in banking","publication_year":2025,"publication_date":"2025-05-01","ids":{"openalex":"https://openalex.org/W4410005308","doi":"https://doi.org/10.1007/s10791-025-09549-7"},"language":"en","primary_location":{"id":"doi:10.1007/s10791-025-09549-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09549-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09549-7.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09549-7.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089596159","display_name":"F. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I187380120","display_name":"Agricultural Bank of China","ror":"https://ror.org/015g9sa94","country_code":"CN","type":"other","lineage":["https://openalex.org/I187380120"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Falai Li","raw_affiliation_strings":["Agricultural Bank of China Zhejiang Branch, No. 100 Jiangjin Road, Shangcheng District, Hangzhou City, Zhejiang Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Agricultural Bank of China Zhejiang Branch, No. 100 Jiangjin Road, Shangcheng District, Hangzhou City, Zhejiang Province, China","institution_ids":["https://openalex.org/I187380120"]}]},{"author_position":"last","author":{"id":null,"display_name":"Zaidie Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I226432","display_name":"Universiti Malaysia Kelantan","ror":"https://ror.org/0463y2v87","country_code":"MY","type":"education","lineage":["https://openalex.org/I226432"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Zaidie Chen","raw_affiliation_strings":["Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia","institution_ids":["https://openalex.org/I226432"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5089596159"],"corresponding_institution_ids":["https://openalex.org/I187380120"],"apc_list":null,"apc_paid":null,"fwci":15.4148,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.98771458,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"28","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":0.9977999925613403,"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":0.9977999925613403,"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":0.9887999892234802,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9596999883651733,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/database-transaction","display_name":"Database transaction","score":0.6208562254905701},{"id":"https://openalex.org/keywords/financial-fraud","display_name":"Financial fraud","score":0.60097736120224},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.48195192217826843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.396286278963089},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3769550025463104},{"id":"https://openalex.org/keywords/accounting","display_name":"Accounting","score":0.230192631483078},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08758515119552612}],"concepts":[{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.6208562254905701},{"id":"https://openalex.org/C2985140798","wikidata":"https://www.wikidata.org/wiki/Q28813","display_name":"Financial fraud","level":2,"score":0.60097736120224},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.48195192217826843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.396286278963089},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3769550025463104},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.230192631483078},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08758515119552612}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10791-025-09549-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09549-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09549-7.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10791-025-09549-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09549-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09549-7.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410005308.pdf","grobid_xml":"https://content.openalex.org/works/W4410005308.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2890118063","https://openalex.org/W3102415155","https://openalex.org/W3120385104","https://openalex.org/W3145880463","https://openalex.org/W3213081719","https://openalex.org/W4205471456","https://openalex.org/W4226198757","https://openalex.org/W4226492624","https://openalex.org/W4281848794","https://openalex.org/W4294884191","https://openalex.org/W4297347671","https://openalex.org/W4311529871","https://openalex.org/W4315977370","https://openalex.org/W4366825916","https://openalex.org/W4387072748","https://openalex.org/W4388656555","https://openalex.org/W4389349949","https://openalex.org/W4389405973","https://openalex.org/W4392741075","https://openalex.org/W4394694413","https://openalex.org/W4395672984","https://openalex.org/W4399341737","https://openalex.org/W4400401303","https://openalex.org/W4400743694","https://openalex.org/W4400931130","https://openalex.org/W4400977627","https://openalex.org/W4401169867","https://openalex.org/W4402530676","https://openalex.org/W4402608032","https://openalex.org/W4405595977","https://openalex.org/W4405755948","https://openalex.org/W4406164151","https://openalex.org/W4407142154"],"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/W2971355493","https://openalex.org/W1967476065","https://openalex.org/W3135862168"],"abstract_inverted_index":{"As":[0],"the":[1,8,117,128,154,162,170,179,191,201,206],"scenario":[2],"of":[3,10,101,130,137,172,193,205],"telecom":[4],"network":[5],"fraud":[6,26,131,194],"intensifies,":[7],"development":[9],"anti-fraud":[11,62,119],"models":[12],"has":[13],"become":[14],"a":[15,60,86,141,165,182],"focal":[16],"point":[17,167],"in":[18,70,169,185],"financial":[19],"technology,":[20],"including":[21],"banking":[22],"sectors.":[23],"Traditional":[24],"anti-telecom":[25],"models,":[27],"which":[28],"primarily":[29],"rely":[30],"on":[31,110],"expert":[32],"rules":[33],"and":[34,47,96,116,127,203],"machine":[35],"learning":[36],"methodologies,":[37],"are":[38],"beset":[39],"with":[40],"limitations":[41],"such":[42],"as":[43,149],"poor":[44],"real-time":[45],"performance":[46,126],"high":[48],"false":[49,113],"favourable":[50,114],"rates,":[51],"leading":[52],"to":[53,74,146],"biased":[54],"predictive":[55],"outcomes.":[56],"This":[57,78,196],"paper":[58],"introduces":[59],"novel":[61],"model:":[63],"The":[64],"Dynamic":[65],"Quantification":[66],"Anti-Fraud":[67],"Model":[68],"grounded":[69],"Real-time":[71],"Transaction":[72],"Flows":[73],"address":[75],"these":[76],"issues.":[77],"model":[79,125,163],"combines":[80],"multiple":[81],"F-Beta":[82],"features":[83],"linearly,":[84],"employs":[85],"DML":[87],"(Double":[88],"Machine":[89],"Learning)":[90],"framework":[91],"for":[92,152],"feature":[93,107],"weight":[94],"quantification,":[95],"conducts":[97],"instantaneous":[98],"risk":[99],"assessments":[100],"account":[102],"transactions.":[103],"By":[104],"dynamically":[105],"adjusting":[106],"weights":[108],"based":[109],"historical":[111],"single-feature":[112],"rates":[115],"branch\u2019s":[118],"tolerance":[120],"capacity,":[121],"it":[122],"significantly":[123],"improves":[124],"efficiency":[129],"prevention.":[132],"Using":[133],"transaction":[134],"detail":[135],"data":[136],"implicated":[138,173],"cards":[139,174],"from":[140],"case":[142],"bank":[143],"spanning":[144],"January":[145],"June":[147],"2023":[148],"an":[150],"example":[151],"modelling,":[153],"validation":[155],"sample":[156],"recall":[157],"rate":[158],"reached":[159],"0.506.":[160],"Post-deployment,":[161],"facilitated":[164],"30-percentage":[166],"reduction":[168],"proportion":[171],"within":[175],"six":[176],"months":[177],"at":[178],"bank,":[180],"alongside":[181],"substantial":[183],"drop":[184],"its":[186],"ranking":[187],"among":[188],"peers":[189],"concerning":[190],"number":[192],"cases.":[195],"comprehensive":[197],"improvement":[198],"solidly":[199],"validates":[200],"precision":[202],"efficacy":[204],"proposed":[207],"model.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}