{"id":"https://openalex.org/W7135222927","doi":"https://doi.org/10.48550/arxiv.2603.11358","title":"Multilingual Financial Fraud Detection Using Machine Learning and Transformer Models: A Bangla-English Study","display_name":"Multilingual Financial Fraud Detection Using Machine Learning and Transformer Models: A Bangla-English Study","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135222927","doi":"https://doi.org/10.48550/arxiv.2603.11358"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11358","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.11358","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129060435","display_name":"Mohammad Shihab Uddin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Uddin, Mohammad Shihab","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129072271","display_name":"Md Hasibul Amin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amin, Md Hasibul","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092942757","display_name":"Nusrat Jahan Ema","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ema, Nusrat Jahan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129010595","display_name":"Bushra Uddin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uddin, Bushra","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102797536","display_name":"Tanvir Ahmed","orcid":"https://orcid.org/0000-0002-6633-9379"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed, Tanvir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5114419924","display_name":"Arif Hassan Zidan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zidan, Arif Hassan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5129060435"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.6248000264167786,"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":0.6248000264167786,"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/T11644","display_name":"Spam and Phishing Detection","score":0.1712000072002411,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.01979999989271164,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5871000289916992},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5210999846458435},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4763000011444092},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4228000044822693},{"id":"https://openalex.org/keywords/phone","display_name":"Phone","score":0.3953000009059906},{"id":"https://openalex.org/keywords/currency","display_name":"Currency","score":0.3865000009536743},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.33559998869895935},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3310999870300293},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.3077000081539154}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7290999889373779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6722999811172485},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6455000042915344},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4228000044822693},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C141121606","wikidata":"https://www.wikidata.org/wiki/Q8142","display_name":"Currency","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2953000068664551},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C540431452","wikidata":"https://www.wikidata.org/wiki/Q16319025","display_name":"FinTech","level":3,"score":0.29249998927116394},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C2994201839","wikidata":"https://www.wikidata.org/wiki/Q624902","display_name":"Digital advertising","level":4,"score":0.2847999930381775},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C48220719","wikidata":"https://www.wikidata.org/wiki/Q10836209","display_name":"Digital currency","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C164516710","wikidata":"https://www.wikidata.org/wiki/Q1166072","display_name":"Financial transaction","level":3,"score":0.2597000002861023},{"id":"https://openalex.org/C139043278","wikidata":"https://www.wikidata.org/wiki/Q837171","display_name":"Financial services","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11358","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.11358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11358","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4527466595172882,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Financial":[0],"fraud":[1,65,142,200],"detection":[2,66],"has":[3],"emerged":[4],"as":[5],"a":[6,68,73],"critical":[7],"research":[8,32],"challenge":[9],"amid":[10],"the":[11,111,124,205],"rapid":[12],"expansion":[13],"of":[14],"digital":[15],"financial":[16,64,79],"platforms.":[17],"Although":[18],"machine":[19,84,191],"learning":[20,85,192],"approaches":[21],"have":[22],"demonstrated":[23],"strong":[24],"performance":[25,113],"in":[26,56,67],"identifying":[27],"fraudulent":[28,78],"activities,":[29],"most":[30],"existing":[31],"focuses":[33],"exclusively":[34],"on":[35],"English-language":[36],"data,":[37],"limiting":[38],"applicability":[39],"to":[40],"multilingual":[41,69,199],"contexts.":[42],"Bangla":[43],"(Bengali),":[44],"despite":[45],"being":[46],"spoken":[47],"by":[48,133,208],"over":[49],"250":[50],"million":[51],"people,":[52],"remains":[53,196],"largely":[54],"unexplored":[55],"this":[57,60],"domain.":[58],"In":[59],"work,":[61],"we":[62],"investigate":[63],"Bangla-English":[70],"setting":[71],"using":[72,94,102],"dataset":[74],"comprising":[75],"legitimate":[76,177],"and":[77,91,118,165,171,182,212],"messages.":[80],"We":[81],"evaluate":[82],"classical":[83,190],"models":[86],"(Logistic":[87],"Regression,":[88],"Linear":[89,108],"SVM,":[90],"Ensemble":[92],"classifiers)":[93],"TF-IDF":[95],"features":[96,195],"alongside":[97],"transformer-based":[98],"architectures.":[99],"Experimental":[100],"results":[101],"5-fold":[103],"stratified":[104],"cross-validation":[105],"demonstrate":[106],"that":[107,189],"SVM":[109],"achieves":[110],"best":[112],"with":[114,193],"91.59":[115],"percent":[116,120,128,131],"accuracy":[117],"91.30":[119],"F1":[121],"score,":[122],"outperforming":[123],"transformer":[125,139],"model":[126],"(89.49":[127],"accuracy,":[129],"88.88":[130],"F1)":[132],"approximately":[134],"2":[135],"percentage":[136],"points.":[137],"The":[138],"exhibits":[140],"higher":[141],"recall":[143],"(94.19":[144],"percent)":[145,170],"but":[146],"suffers":[147],"from":[148],"elevated":[149],"false":[150],"positive":[151],"rates.":[152],"Exploratory":[153],"analysis":[154],"reveals":[155],"distinctive":[156],"patterns:":[157],"scam":[158],"messages":[159,178],"are":[160],"longer,":[161],"contain":[162],"urgency-inducing":[163],"terms,":[164],"frequently":[166],"include":[167],"URLs":[168],"(32":[169],"phone":[172],"numbers":[173],"(97":[174],"percent),":[175],"while":[176,202],"feature":[179],"transactional":[180],"confirmations":[181],"specific":[183],"currency":[184],"references.":[185],"Our":[186],"findings":[187],"highlight":[188],"well-crafted":[194],"competitive":[197],"for":[198],"detection,":[201],"also":[203],"underscoring":[204],"challenges":[206],"posed":[207],"linguistic":[209],"diversity,":[210],"code-mixing,":[211],"low-resource":[213],"language":[214],"constraints.":[215]},"counts_by_year":[],"updated_date":"2026-03-14T06:46:50.379900","created_date":"2026-03-14T00:00:00"}