{"id":"https://openalex.org/W7214775407","doi":"https://doi.org/10.48550/arxiv.2609.34031","title":"Posterior Regimes and Latent Deception: Variational Bayesian Inference in Hidden Markov Models for Sequential Fraud Detection in Financial Transactions","display_name":"Posterior Regimes and Latent Deception: Variational Bayesian Inference in Hidden Markov Models for Sequential Fraud Detection in Financial Transactions","publication_year":2026,"publication_date":"2026-09-27","ids":{"openalex":"https://openalex.org/W7214775407","doi":"https://doi.org/10.48550/arxiv.2609.34031"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.34031","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34031","pdf_url":"https://arxiv.org/pdf/2609.34031","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.34031","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5153455160","display_name":"Joseph Uririoghene Obukofe","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I12093191","display_name":"University of Stirling","ror":"https://ror.org/045wgfr59","country_code":"GB","type":"education","lineage":["https://openalex.org/I12093191"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Obukofe, Joseph Uririoghene","raw_affiliation_strings":["Computing Science and Mathematics , Faculty of Natural Sciences , University of Stirling , Stirling FK9 4LA , Scotland , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computing Science and Mathematics , Faculty of Natural Sciences , University of Stirling , Stirling FK9 4LA , Scotland , United Kingdom","institution_ids":["https://openalex.org/I12093191"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136163764","display_name":"Anthony O\u2019Hare","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I12093191","display_name":"University of Stirling","ror":"https://ror.org/045wgfr59","country_code":"GB","type":"education","lineage":["https://openalex.org/I12093191"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"O'Hare, Anthony","raw_affiliation_strings":["Computing Science and Mathematics , Faculty of Natural Sciences , University of Stirling , Stirling FK9 4LA , Scotland , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computing Science and Mathematics , Faculty of Natural Sciences , University of Stirling , Stirling FK9 4LA , Scotland , United Kingdom","institution_ids":["https://openalex.org/I12093191"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5153422522","display_name":"Chioma Sandra Dike","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dike, Chioma Sandra","raw_affiliation_strings":["University of Nottingham , Nottingham , NG7 2RD , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Nottingham , Nottingham , NG7 2RD , United Kingdom","institution_ids":["https://openalex.org/I142263535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.7685999870300293,"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":0.7685999870300293,"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":0.06539999693632126,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.06270000338554382,"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/financial-fraud-detection","display_name":"financial fraud detection","score":0.7480000257492065},{"id":"https://openalex.org/keywords/hidden-markov-models","display_name":"hidden Markov models","score":0.925000011920929},{"id":"https://openalex.org/keywords/variational-bayesian-inference","display_name":"variational inference","score":0.9559999704360962},{"id":"https://openalex.org/keywords/calibration-error","display_name":"calibration error","score":0.6200000047683716},{"id":"https://openalex.org/keywords/batch-inference","display_name":"batch inference","score":0.3310000002384186},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.4410000145435333}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.661899983882904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.646399974822998},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6413000226020813},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6176000237464905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5583000183105469},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5443999767303467},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.522599995136261},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5020999908447266},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4666000008583069},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4293000102043152},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41029998660087585},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.3970000147819519},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.39259999990463257},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.3880000114440918},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33399999141693115},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3001999855041504},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.296099990606308},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C64939953","wikidata":"https://www.wikidata.org/wiki/Q3859882","display_name":"Hidden semi-Markov model","level":5,"score":0.26919999718666077},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2669999897480011},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2583000063896179},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.34031","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34031","pdf_url":"https://arxiv.org/pdf/2609.34031","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.34031","doi":"https://doi.org/10.48550/arxiv.2609.34031","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.34031","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.34031","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34031","pdf_url":"https://arxiv.org/pdf/2609.34031","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214775407.pdf","grobid_xml":"https://content.openalex.org/works/W7214775407.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1539673959","https://openalex.org/W2007321142","https://openalex.org/W2011760672","https://openalex.org/W2045049630","https://openalex.org/W2064675550","https://openalex.org/W2096266510","https://openalex.org/W2296719434","https://openalex.org/W2786577118","https://openalex.org/W2786672974","https://openalex.org/W3037932933","https://openalex.org/W3111356309","https://openalex.org/W3174082502","https://openalex.org/W4255375128","https://openalex.org/W6910681941"],"related_works":["https://openalex.org/W2274893312","https://openalex.org/W7134847306","https://openalex.org/W2754410991","https://openalex.org/W4403313152","https://openalex.org/W2621472334","https://openalex.org/W6907657032","https://openalex.org/W3173981572","https://openalex.org/W3154057253","https://openalex.org/W2187150624","https://openalex.org/W150880572"],"abstract_inverted_index":{"We":[0,108,149],"present":[1],"a":[2,15,28,31,57,61,66,77,96,127,140,177,189,210,218],"three-tier":[3],"progression":[4],"of":[5,34,39,80,95,120,146],"Hidden":[6],"Markov":[7],"Models:":[8],"maximum-likelihood":[9],"(Baum-Welch),":[10],"variational":[11,17],"Bayesian":[12],"(VBEM),":[13],"and":[14,130,166,199,202,213],"neural":[16,174],"extension":[18],"(Neural":[19],"VBEM),":[20],"that":[21,84,110,115],"model":[22,205],"each":[23],"customer's":[24],"transaction":[25,63,171],"history":[26],"as":[27,44,209],"trajectory":[29],"through":[30],"small":[32],"number":[33],"latent":[35,68],"behavioural":[36,99],"regimes,":[37],"one":[38],"which":[40,71,155],"is":[41,123,197,206],"empirically":[42],"identified":[43,183],"fraud-associated.":[45],"The":[46],"Neural":[47],"VBEM":[48,73],"HMM":[49],"replaces":[50],"the":[51,72,85,111,117,132,173,204],"fixed":[52],"Gaussian-multinomial":[53],"emission":[54],"family":[55],"with":[56,101],"learned":[58],"encoder,":[59],"compressing":[60],"741-dimensional":[62],"representation":[64],"into":[65],"64-dimensional":[67],"space":[69,82],"in":[70,181],"HMM's":[74],"posterior":[75,118],"operates;":[76],"UMAP":[78],"projection":[79],"this":[81,147,195],"reveals":[83],"discovered":[86],"regimes":[87],"are":[88],"not":[89,200],"discrete":[90],"clusters":[91],"but":[92],"ordered":[93],"segments":[94],"single":[97],"continuous":[98],"manifold,":[100],"confirmed":[102],"fraud":[103,128,179],"concentrated":[104],"at":[105,159],"its":[106,182,186],"extreme.":[107],"show":[109,194],"model's":[112],"natural":[113],"output,":[114],"is,":[116],"probability":[119],"regime":[121],"membership,":[122],"routinely":[124],"mistaken":[125],"for":[126],"probability,":[129],"quantify":[131],"resulting":[133],"miscalibration":[134],"(the":[135],"regime-membership":[136],"interpretation":[137],"error,":[138],"MRIE);":[139],"corrected":[141],"posterior-predictive":[142],"score,":[143],"closes":[144],"most":[145],"gap.":[148],"further":[150],"distinguish":[151],"batch":[152],"(smoothed)":[153],"inference,":[154,165],"uses":[156],"look-ahead":[157],"unavailable":[158],"deployment":[160],"time,":[161],"from":[162],"filtered":[163],"(forward-only)":[164],"report":[167],"both.":[168],"On":[169],"IEEE-CIS":[170],"data,":[172],"tier":[175],"achieves":[176],"14.4$\\times$":[178],"enrichment":[180],"regime;":[184],"while":[185],"AUPRC":[187],"trails":[188],"discriminative":[190],"XGBoost":[191],"baseline,":[192],"we":[193],"gap":[196],"structural":[198],"incidental,":[201],"argue":[203],"best":[207],"positioned":[208],"calibrated":[211],"triage":[212],"interpretability":[214],"layer":[215],"rather":[216],"than":[217],"drop-in":[219],"ranking":[220],"replacement.":[221]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-30T00:00:00"}
