{"id":"https://openalex.org/W4402298092","doi":"https://doi.org/10.1016/j.iotcps.2024.09.001","title":"Credit card default prediction using ML and DL techniques","display_name":"Credit card default prediction using ML and DL techniques","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4402298092","doi":"https://doi.org/10.1016/j.iotcps.2024.09.001"},"language":"en","primary_location":{"id":"doi:10.1016/j.iotcps.2024.09.001","doi":"https://doi.org/10.1016/j.iotcps.2024.09.001","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.iotcps.2024.09.001","pdf_url":null,"source":{"id":"https://openalex.org/S4210180977","display_name":"Internet of Things and Cyber-Physical Systems","issn_l":"2667-3452","issn":["2667-3452"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"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":"Internet of Things and Cyber-Physical Systems","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.iotcps.2024.09.001","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031382096","display_name":"Fazal Wahab","orcid":"https://orcid.org/0000-0001-9684-3392","observed_orcids":["https://orcid.org/0000-0001-9684-3392"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fazal Wahab","raw_affiliation_strings":["Corvit Systems Peshawar, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corvit Systems Peshawar, Pakistan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030054197","display_name":"Imran Khan","orcid":"https://orcid.org/0000-0002-5732-4685","observed_orcids":["https://orcid.org/0000-0002-5732-4685"]},"institutions":[{"id":"https://openalex.org/I162608824","display_name":"University of Sussex","ror":"https://ror.org/00ayhx656","country_code":"GB","type":"education","lineage":["https://openalex.org/I162608824"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Imran Khan","raw_affiliation_strings":["Department of Informatics, University of Sussex, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics, University of Sussex, UK","institution_ids":["https://openalex.org/I162608824"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107036931","display_name":"Sneha Sabada","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I141584323","display_name":"University of Hertfordshire","ror":"https://ror.org/0267vjk41","country_code":"GB","type":"education","lineage":["https://openalex.org/I141584323"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sneha Sabada","raw_affiliation_strings":["Department of Engineering and Computer Science, University of Hertfordshire, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering and Computer Science, University of Hertfordshire, UK","institution_ids":["https://openalex.org/I141584323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030054197","https://openalex.org/A5031382096"],"corresponding_institution_ids":["https://openalex.org/I162608824"],"apc_list":{"value":0,"currency":"USD","value_usd":0},"apc_paid":null,"fwci":8.4714,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.98004612,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"4","issue":null,"first_page":"293","last_page":"306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9911999702453613,"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"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9911999702453613,"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"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.002099999925121665,"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-default-prediction","display_name":"credit card default prediction","score":0.9990000128746033},{"id":"https://openalex.org/keywords/bank-loan-default","display_name":"bank loan default","score":0.6330000162124634},{"id":"https://openalex.org/keywords/deep-learning-models","display_name":"deep learning models","score":0.6330000162124634},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.925000011920929},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.9079999923706055},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.8230000138282776},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.7960000038146973},{"id":"https://openalex.org/keywords/credit-risk","display_name":"credit risk","score":0.6710000038146973}],"concepts":[{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.602751612663269},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4929790496826172},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.3683622181415558},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1414031982421875},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.08396682143211365}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1016/j.iotcps.2024.09.001","doi":"https://doi.org/10.1016/j.iotcps.2024.09.001","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.iotcps.2024.09.001","pdf_url":null,"source":{"id":"https://openalex.org/S4210180977","display_name":"Internet of Things and Cyber-Physical Systems","issn_l":"2667-3452","issn":["2667-3452"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"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":"Internet of Things and Cyber-Physical Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4c0431bb896144ac9d9f899c530af6e7","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/4c0431bb896144ac9d9f899c530af6e7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Internet of Things and Cyber-Physical Systems, Vol 4, Iss , Pp 293-306 (2024)","raw_type":"article"},{"id":"pmh:oai:figshare.com:article/28749401","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Credit_card_default_prediction_using_ML_and_DL_techniques/28749401","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1016/j.iotcps.2024.09.001","doi":"https://doi.org/10.1016/j.iotcps.2024.09.001","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.iotcps.2024.09.001","pdf_url":null,"source":{"id":"https://openalex.org/S4210180977","display_name":"Internet of Things and Cyber-Physical Systems","issn_l":"2667-3452","issn":["2667-3452"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"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":"Internet of Things and Cyber-Physical Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W2760103357","https://openalex.org/W2770595689","https://openalex.org/W2783479218","https://openalex.org/W2789758093","https://openalex.org/W2790979755","https://openalex.org/W2791460829","https://openalex.org/W2800423036","https://openalex.org/W2867605356","https://openalex.org/W2886399336","https://openalex.org/W2897704812","https://openalex.org/W2900954917","https://openalex.org/W2910096450","https://openalex.org/W2944842185","https://openalex.org/W2946712695","https://openalex.org/W2948382308","https://openalex.org/W2948793373","https://openalex.org/W2966531722","https://openalex.org/W2966632398","https://openalex.org/W2980149079","https://openalex.org/W2981380462","https://openalex.org/W2981648168","https://openalex.org/W2985044096","https://openalex.org/W2989974457","https://openalex.org/W2994445360","https://openalex.org/W3003553208","https://openalex.org/W3004917840","https://openalex.org/W3006682208","https://openalex.org/W3028573817","https://openalex.org/W3036230325","https://openalex.org/W3048836559","https://openalex.org/W3085581207","https://openalex.org/W3092170384","https://openalex.org/W3093186795","https://openalex.org/W3096019139","https://openalex.org/W3096139439","https://openalex.org/W3102100346","https://openalex.org/W3111672143","https://openalex.org/W3115490817","https://openalex.org/W3138690044","https://openalex.org/W3140676842","https://openalex.org/W3148181069","https://openalex.org/W3151784567","https://openalex.org/W3172695087","https://openalex.org/W3177435843","https://openalex.org/W3186390924","https://openalex.org/W3187405256","https://openalex.org/W3194366213","https://openalex.org/W4200457345","https://openalex.org/W4206501142","https://openalex.org/W4212968553","https://openalex.org/W4224290632","https://openalex.org/W4231846903","https://openalex.org/W4233298474","https://openalex.org/W4246981428","https://openalex.org/W4252276495","https://openalex.org/W4254436240","https://openalex.org/W4282006100","https://openalex.org/W4293060408","https://openalex.org/W4297360383","https://openalex.org/W4308123320","https://openalex.org/W4380291029","https://openalex.org/W4385614824","https://openalex.org/W4390578069","https://openalex.org/W4393312855"],"related_works":["https://openalex.org/W4372324581","https://openalex.org/W4415437352","https://openalex.org/W4390045569","https://openalex.org/W2909408909","https://openalex.org/W4387390305","https://openalex.org/W4391770528","https://openalex.org/W4317831011","https://openalex.org/W4385246651","https://openalex.org/W4406461123","https://openalex.org/W7116985153"],"abstract_inverted_index":{"The":[0,46,94,162,249],"banking":[1],"sector":[2],"is":[3,51,167,278],"widely":[4],"acknowledged":[5],"for":[6,36],"its":[7],"intrinsic":[8],"unpredictability":[9],"and":[10,41,129,147,159,215,255],"susceptibility":[11],"to":[12,58,99,150,168,176,197,210,233,244],"risk.":[13],"Bank":[14],"loans":[15],"have":[16],"emerged":[17],"as":[18,34,155],"one":[19],"of":[20,44,48,77,96,143,164,183,192,223,262,273],"the":[21,27,62,75,117,125,141,170,177,181,193,199,212,218,221,229,235,247,253,258,271,274],"most":[22],"recent":[23],"services":[24],"offered":[25],"over":[26],"past":[28],"several":[29],"decades.":[30],"Banks":[31],"typically":[32],"serve":[33],"intermediaries":[35],"loans,":[37,40],"investments,":[38],"short-term":[39],"other":[42,151],"types":[43],"credit.":[45],"usage":[47],"credit":[49,100,135,184,200,267],"cards":[50],"experiencing":[52],"a":[53,59,144],"steady":[54],"increase,":[55],"thereby":[56],"leading":[57],"rise":[60],"in":[61,113,180,237,265],"default":[63,102,186],"rate":[64,261],"that":[65,174,252],"banks":[66],"encounter.":[67],"Although":[68],"there":[69,83],"has":[70,84,104],"been":[71,85,106],"much":[72],"research":[73,166,189],"investigating":[74],"efficacy":[76,142],"conventional":[78],"Machine":[79],"Learning":[80,91],"(ML)":[81],"models,":[82,153],"relatively":[86],"less":[87],"emphasis":[88],"on":[89],"Deep":[90],"(DL)":[92],"techniques.":[93],"application":[95],"DL":[97,126,145,172],"approaches":[98],"card":[101,136,185,201,268],"prediction":[103],"not":[105],"extensively":[107],"researched":[108],"despite":[109],"their":[110],"considerable":[111],"potential":[112],"numerous":[114],"fields.":[115],"Moreover,":[116],"current":[118],"literature":[119],"frequently":[120],"lacks":[121],"particular":[122],"information":[123],"regarding":[124],"structures,":[127],"hyperparameters,":[128],"optimization":[130],"techniques":[131,207],"employed.":[132],"To":[133],"predict":[134],"default,":[137,269],"this":[138,165],"study":[139],"evaluates":[140],"model":[146],"compares":[148],"it":[149],"ML":[152,195],"such":[154],"Decision":[156],"Tree":[157],"(DT)":[158],"Adaboost":[160],".":[161],"objective":[163],"identify":[169],"specific":[171],"parameters":[173],"contribute":[175],"observed":[178],"enhancements":[179],"accuracy":[182,260,272],"prediction.":[187,238],"This":[188],"makes":[190],"use":[191,222],"UCI":[194],"repository":[196],"access":[198],"defaulted":[202],"customer":[203],"dataset.":[204],"Subsequently,":[205],"various":[206],"are":[208,231],"employed":[209],"preprocess":[211],"unprocessed":[213],"data":[214,225],"visually":[216],"present":[217],"outcomes":[219],"through":[220],"exploratory":[224],"analysis":[226],"(EDA).":[227],"Furthermore,":[228],"algorithms":[230],"hypertuned":[232],"evaluate":[234,245],"enhancement":[236],"We":[239],"used":[240],"standard":[241],"evaluation":[242,250],"metrics":[243],"all":[246],"models.":[248],"indicates":[251],"AdaBoost":[254],"DT":[256],"exhibit":[257],"highest":[259],"82":[263],"\u200b%":[264],"predicting":[266],"surpassing":[270],"ANN":[275],"model,":[276],"which":[277],"78":[279],"\u200b%.":[280]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":9}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2024-09-07T00:00:00"}