{"id":"https://openalex.org/W2936086127","doi":"https://doi.org/10.1109/icassp.2019.8682212","title":"Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction","display_name":"Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2936086127","doi":"https://doi.org/10.1109/icassp.2019.8682212","mag":"2936086127"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682212","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026870554","display_name":"Te-Cheng Hsu","orcid":"https://orcid.org/0000-0003-2686-925X"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Te-Cheng Hsu","raw_affiliation_strings":["Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025149860","display_name":"Shing-Tzuo Liou","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shing-Tzuo Liou","raw_affiliation_strings":["Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115594856","display_name":"Yunping Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yun-Ping Wang","raw_affiliation_strings":["Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002055204","display_name":"Yung-Shun Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yung-Shun Huang","raw_affiliation_strings":["Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015929431","display_name":"Che-Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Che-Lin","raw_affiliation_strings":["Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5026870554"],"corresponding_institution_ids":["https://openalex.org/I25846049"],"apc_list":null,"apc_paid":null,"fwci":11.2429,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.98252757,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1572","last_page":"1576"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9991000294685364,"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":0.9991000294685364,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9973999857902527,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.852109432220459},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7767243385314941},{"id":"https://openalex.org/keywords/credit-card","display_name":"Credit card","score":0.7106261253356934},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6085299849510193},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5456898212432861},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5418300628662109},{"id":"https://openalex.org/keywords/lift","display_name":"Lift (data mining)","score":0.5016582012176514},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4917197525501251},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4818226397037506},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4738348424434662},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42180415987968445},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3779858946800232},{"id":"https://openalex.org/keywords/payment","display_name":"Payment","score":0.21640455722808838},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08057901263237}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.852109432220459},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7767243385314941},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.7106261253356934},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6085299849510193},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5456898212432861},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5418300628662109},{"id":"https://openalex.org/C139002025","wikidata":"https://www.wikidata.org/wiki/Q3001212","display_name":"Lift (data mining)","level":2,"score":0.5016582012176514},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4917197525501251},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4818226397037506},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4738348424434662},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42180415987968445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3779858946800232},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.21640455722808838},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08057901263237},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682212","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W142793689","https://openalex.org/W1480376833","https://openalex.org/W1546047689","https://openalex.org/W1947758080","https://openalex.org/W2067594023","https://openalex.org/W2076310205","https://openalex.org/W2085988980","https://openalex.org/W2117352691","https://openalex.org/W2148143831","https://openalex.org/W2168123127","https://openalex.org/W2358893481","https://openalex.org/W2561309841","https://openalex.org/W2774771370","https://openalex.org/W2790348972","https://openalex.org/W2804491889","https://openalex.org/W2951176429","https://openalex.org/W6605806949","https://openalex.org/W6632634077","https://openalex.org/W6747176662"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W1979583797","https://openalex.org/W2964335273","https://openalex.org/W1889624880"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"models":[2],"have":[3],"been":[4],"shown":[5],"to":[6,53,69,100,107,135,145,152],"be":[7],"capable":[8],"of":[9,17],"extracting":[10],"high-level":[11],"representations":[12],"from":[13],"the":[14,71,94,117,123,136],"increasing":[15],"amount":[16],"customer-level":[18],"data":[19],"generated":[20],"via":[21],"fast-growing":[22],"financial":[23,26,158],"activities.":[24],"In":[25,48],"data,":[27],"dynamic":[28,77,90,150],"features":[29,91,96,151],"that":[30,116],"evolve":[31],"with":[32,62,93],"time":[33,39,72],"are":[34,41,81,97],"commonly":[35],"observed.":[36],"However,":[37],"such":[38],"dependencies":[40,73],"often":[42],"ignored":[43],"in":[44,75,126],"classical":[45],"classification":[46],"models.":[47,139],"this":[49,85],"study,":[50],"we":[51],"propose":[52],"learn":[54],"a":[55],"Recurrent":[56],"Neural":[57],"Network":[58],"(RNN)":[59],"feature":[60,86],"extractor":[61],"GRU":[63],"on":[64],"credit":[65,109],"card":[66,110],"payment":[67],"history":[68],"leverage":[70],"embedded":[74],"these":[76],"features.":[78],"Input":[79],"sequences":[80],"first":[82],"preprocessed":[83],"by":[84],"extractor.":[87],"The":[88,140],"extracted":[89],"along":[92],"static":[95,148],"then":[98],"utilized":[99],"train":[101],"an":[102],"enhanced":[103,118],"RNN":[104,119],"model":[105,142],"(RNN-RF)":[106],"predict":[108],"client":[111],"defaults.":[112],"Numerical":[113],"experiments":[114],"confirmed":[115],"predictor":[120],"indeed":[121],"provides":[122],"best":[124],"performance":[125,156],"both":[127],"lift":[128],"index":[129],"(0.659)":[130],"and":[131,149],"AUC":[132],"(0.782)":[133],"compared":[134],"other":[137],"benchmark":[138],"proposed":[141],"allows":[143],"us":[144],"effectively":[146],"combine":[147],"provide":[153],"superior":[154],"predictive":[155],"for":[157],"data.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}