{"id":"https://openalex.org/W7224085237","doi":"https://doi.org/10.64388/irev10i4-1724375","title":"Explainable AI for Imbalanced Credit Card Fraud Detection: A Comparative Study","display_name":"Explainable AI for Imbalanced Credit Card Fraud Detection: A Comparative Study","publication_year":2026,"publication_date":"2026-10-27","ids":{"openalex":"https://openalex.org/W7224085237","doi":"https://doi.org/10.64388/irev10i4-1724375"},"language":"en","primary_location":{"id":"doi:10.64388/irev10i4-1724375","doi":"https://doi.org/10.64388/irev10i4-1724375","is_oa":true,"landing_page_url":"https://doi.org/10.64388/irev10i4-1724375","pdf_url":"https://www.irejournals.com/formatedpaper/1724375.pdf","source":{"id":"https://openalex.org/S5407000720","display_name":"Iconic Research and Engineering Journals","issn_l":"2456-8880","issn":["2456-8880"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iconic Research and Engineering Journals","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.irejournals.com/formatedpaper/1724375.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159341208","display_name":"Vrushali Godse","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vrushali Godse","raw_affiliation_strings":["Zeal Institute of Business Administration, Computer Application and Research, Pune, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zeal Institute of Business Administration, Computer Application and Research, Pune, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159996795","display_name":"Vani Ramkrishna Talkokul","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vani Ramkrishna Talkokul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160002431","display_name":"Sanyukta Sasane","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanyukta Sasane","raw_affiliation_strings":["Zeal Institute of Business Administration , Computer Application and Research , Pune , Maharashtra , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zeal Institute of Business Administration , Computer Application and Research , Pune , Maharashtra , India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066044759","display_name":"K. K. Singh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khushi K. Singh","raw_affiliation_strings":["Zeal Institute of Business Administration , Computer Application and Research , Pune , Maharashtra , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zeal Institute of Business Administration , Computer Application and Research , Pune , Maharashtra , India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.80645217,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"4","first_page":"3776","last_page":"3795"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":1.0,"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":1.0,"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":0.0,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0,"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-detection","display_name":"credit card fraud detection","score":0.9940000176429749},{"id":"https://openalex.org/keywords/imbalanced-data-classification","display_name":"imbalanced classification","score":0.6600000262260437},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.9660000205039978},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.9440000057220459},{"id":"https://openalex.org/keywords/smote","display_name":"SMOTE","score":0.6800000071525574},{"id":"https://openalex.org/keywords/class-weights","display_name":"class weighting","score":0.9980000257492065},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.984000027179718},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.8759999871253967},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.9079999923706055},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.9869999885559082},{"id":"https://openalex.org/keywords/precision-recall-trade-off","display_name":"precision-recall trade-off","score":0.5849999785423279},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.40700000524520874}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6814000010490417},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6601999998092651},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6238999962806702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5861999988555908},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.569599986076355},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5637000203132629},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.5436999797821045},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5357000231742859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5049999952316284},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5023999810218811},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.46950000524520874},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C2780747020","wikidata":"https://www.wikidata.org/wiki/Q83873","display_name":"Credit card fraud","level":4,"score":0.39910000562667847},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3874000012874603},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.37400001287460327},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.29670000076293945},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C84839998","wikidata":"https://www.wikidata.org/wiki/Q5249245","display_name":"Decision rule","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.64388/irev10i4-1724375","doi":"https://doi.org/10.64388/irev10i4-1724375","is_oa":true,"landing_page_url":"https://doi.org/10.64388/irev10i4-1724375","pdf_url":"https://www.irejournals.com/formatedpaper/1724375.pdf","source":{"id":"https://openalex.org/S5407000720","display_name":"Iconic Research and Engineering Journals","issn_l":"2456-8880","issn":["2456-8880"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iconic Research and Engineering Journals","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.64388/irev10i4-1724375","doi":"https://doi.org/10.64388/irev10i4-1724375","is_oa":true,"landing_page_url":"https://doi.org/10.64388/irev10i4-1724375","pdf_url":"https://www.irejournals.com/formatedpaper/1724375.pdf","source":{"id":"https://openalex.org/S5407000720","display_name":"Iconic Research and Engineering Journals","issn_l":"2456-8880","issn":["2456-8880"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iconic Research and Engineering Journals","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224085237.pdf","grobid_xml":"https://content.openalex.org/works/W7224085237.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W2148143831","https://openalex.org/W2295598076"],"related_works":["https://openalex.org/W7204191757","https://openalex.org/W7125588572","https://openalex.org/W7160248939","https://openalex.org/W7216746684","https://openalex.org/W4411885368","https://openalex.org/W7204636454","https://openalex.org/W7163871238","https://openalex.org/W4417403823","https://openalex.org/W7126228916","https://openalex.org/W7123583850"],"abstract_inverted_index":{"Credit-card":[0],"fraud":[1],"detection":[2],"is":[3,165,194],"difficult":[4],"because":[5],"fraudulent":[6,85,242],"transactions":[7],"make":[8],"up":[9],"only":[10,240],"a":[11,21,96,152,180,203,236,248],"small":[12],"fraction":[13],"of":[14,136,142,155,253],"observed":[15],"payments.":[16],"Under":[17],"this":[18],"class":[19,65,163],"imbalance,":[20],"classifier":[22],"can":[23],"achieve":[24],"high":[25],"accuracy":[26,33],"while":[27],"failing":[28],"to":[29,105,109,196],"identify":[30,221],"fraud,":[31],"making":[32],"alone":[34],"unsuitable":[35],"for":[36],"model":[37],"selection":[38],"[1],":[39],"[3].":[40],"This":[41],"study":[42],"compares":[43],"Logistic":[44,184],"Regression,":[45],"Decision":[46,187],"Tree,":[47],"Random":[48,191],"Forest,":[49],"and":[50,64,101,128,140,159,174,186,199,202,210,247],"XGBoost":[51,161],"under":[52],"three":[53],"training":[54,107],"conditions:":[55],"no":[56],"imbalance":[57],"adjustment,":[58],"Synthetic":[59],"Minority":[60],"Oversampling":[61],"Technique":[62],"(SMOTE),":[63],"weighting.":[66],"Experiments":[67],"use":[68],"the":[69,91,106],"public":[70],"ULB":[71],"Credit":[72],"Card":[73],"Fraud":[74],"Detection":[75],"dataset,":[76],"which":[77],"contains":[78],"284,807":[79],"transactions,":[80,244],"including":[81],"492":[82],"labelled":[83],"as":[84],"(0.173%).":[86],"After":[87],"removing":[88],"duplicate":[89],"records,":[90],"data":[92,108],"are":[93,103,116,148,217,233],"divided":[94],"using":[95,123,151],"stratified":[97],"80/20":[98],"split.":[99],"Resampling":[100],"scaling":[102],"restricted":[104],"prevent":[110],"test-set":[111],"leakage.":[112],"The":[113,231],"twelve":[114],"configurations":[115],"evaluated":[117],"on":[118],"an":[119],"untouched":[120],"test":[121,243],"set":[122],"Precision,":[124,157],"Recall,":[125,156],"F1-score,":[126,158],"ROC-AUC,":[127],"PR-AUC.":[129,160],"A":[130],"prespecified":[131],"eligibility":[132],"rule":[133],"requires":[134],"Recall":[135,168],"at":[137,143],"least":[138,144],"0.60":[139],"Precision":[141,170],"0.30;":[145],"eligible":[146],"models":[147],"then":[149],"ranked":[150],"weighted":[153],"composite":[154],"with":[162,167],"weighting":[164],"selected,":[166],"0.789,":[169],"0.974,":[171],"F1-score":[172],"0.872,":[173],"PR-AUC":[175],"0.823.":[176],"Bootstrap":[177],"comparisons":[178],"indicate":[179],"clearer":[181],"advantage":[182],"over":[183,190],"Regression":[185],"Tree":[188],"than":[189],"Forest.":[192],"SHAP":[193],"used":[195],"explain":[197],"global":[198],"transaction-level":[200],"predictions,":[201],"Flask":[204],"prototype":[205],"displays":[206],"scores,":[207],"risk":[208],"bands,":[209],"feature":[211],"contributions.":[212],"Because":[213],"most":[214],"input":[215],"variables":[216],"anonymized,":[218],"these":[219],"explanations":[220],"influential":[222],"components":[223],"but":[224],"do":[225],"not":[226],"establish":[227],"real-world":[228],"transaction":[229],"causes.":[230],"findings":[232],"limited":[234],"by":[235],"single":[237],"random":[238],"split,":[239],"95":[241],"fixed":[245],"hyperparameters,":[246],"dataset":[249],"covering":[250],"two":[251],"days":[252],"transactions.":[254]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-29T00:00:00"}
