{"id":"https://openalex.org/W2900843648","doi":"https://doi.org/10.4236/me.2018.911115","title":"Machine Learning Approaches to Predict Default of Credit Card Clients","display_name":"Machine Learning Approaches to Predict Default of Credit Card Clients","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2900843648","doi":"https://doi.org/10.4236/me.2018.911115","mag":"2900843648"},"language":"en","primary_location":{"id":"doi:10.4236/me.2018.911115","doi":"https://doi.org/10.4236/me.2018.911115","is_oa":true,"landing_page_url":"https://doi.org/10.4236/me.2018.911115","pdf_url":"http://www.scirp.org/journal/PaperDownload.aspx?paperID=88577","source":{"id":"https://openalex.org/S72925349","display_name":"Modern Economy","issn_l":"2152-7245","issn":["2152-7245","2152-7261"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core"],"host_organization":"https://openalex.org/P4310320480","host_organization_name":"Scientific Research Publishing","host_organization_lineage":["https://openalex.org/P4310320480"],"host_organization_lineage_names":["Scientific Research Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Modern Economy","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"http://www.scirp.org/journal/PaperDownload.aspx?paperID=88577","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101428011","display_name":"Ruilin Liu","orcid":"https://orcid.org/0009-0008-6242-8316","observed_orcids":["https://orcid.org/0009-0008-6242-8316"]},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ruilin Liu","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101428011"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.7542,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.76664258,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":99},"biblio":{"volume":"09","issue":"11","first_page":"1828","last_page":"1838"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9570000171661377,"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":0.9570000171661377,"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":0.01590000092983246,"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":0.012799999676644802,"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":0.9829999804496765},{"id":"https://openalex.org/keywords/default-risk","display_name":"default risk","score":0.6230000257492065},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.8109999895095825},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.9990000128746033},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.996999979019165},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":1.0},{"id":"https://openalex.org/keywords/feedforward-neural-networks","display_name":"feedforward neural networks","score":0.9980000257492065},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"long short-term memory","score":0.9850000143051147},{"id":"https://openalex.org/keywords/dropout","display_name":"dropout","score":0.8169999718666077},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.6639999747276306}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.9019736051559448},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.785102367401123},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6828544735908508},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6498813629150391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.604031503200531},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.5910150408744812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5816296339035034},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5344364643096924},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4985792636871338},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.47438982129096985},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.45269376039505005},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06170406937599182},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control 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/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4236/me.2018.911115","doi":"https://doi.org/10.4236/me.2018.911115","is_oa":true,"landing_page_url":"https://doi.org/10.4236/me.2018.911115","pdf_url":"http://www.scirp.org/journal/PaperDownload.aspx?paperID=88577","source":{"id":"https://openalex.org/S72925349","display_name":"Modern Economy","issn_l":"2152-7245","issn":["2152-7245","2152-7261"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core"],"host_organization":"https://openalex.org/P4310320480","host_organization_name":"Scientific Research Publishing","host_organization_lineage":["https://openalex.org/P4310320480"],"host_organization_lineage_names":["Scientific Research Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Modern Economy","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.4236/me.2018.911115","doi":"https://doi.org/10.4236/me.2018.911115","is_oa":true,"landing_page_url":"https://doi.org/10.4236/me.2018.911115","pdf_url":"http://www.scirp.org/journal/PaperDownload.aspx?paperID=88577","source":{"id":"https://openalex.org/S72925349","display_name":"Modern Economy","issn_l":"2152-7245","issn":["2152-7245","2152-7261"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core"],"host_organization":"https://openalex.org/P4310320480","host_organization_name":"Scientific Research Publishing","host_organization_lineage":["https://openalex.org/P4310320480"],"host_organization_lineage_names":["Scientific Research Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Modern Economy","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2900843648.pdf","grobid_xml":"https://content.openalex.org/works/W2900843648.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2040870580","https://openalex.org/W2064675550","https://openalex.org/W2085988980","https://openalex.org/W2122111042","https://openalex.org/W2149706766","https://openalex.org/W2911964244","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W4406230441","https://openalex.org/W2944984517","https://openalex.org/W4360981885","https://openalex.org/W3086312560","https://openalex.org/W4396711855","https://openalex.org/W7070602589","https://openalex.org/W3131515772","https://openalex.org/W4386159542","https://openalex.org/W4413858590","https://openalex.org/W2909408909"],"abstract_inverted_index":{"This":[0,39],"paper":[1,40],"compares":[2],"traditional":[3,37],"machine":[4],"learning":[5],"models,":[6],"i.e.":[7],"Support":[8],"Vector":[9],"Machine,":[10],"k-Nearest":[11],"Neighbors,":[12],"Decision":[13],"Tree":[14],"and":[15,22],"Random":[16],"Forest,":[17],"with":[18],"Feedforward":[19,58],"Neural":[20,59],"Network":[21],"Long":[23],"Short-Term":[24],"Memory.":[25],"We":[26,54],"observe":[27,55],"that":[28,56],"the":[29],"two":[30],"neural":[31,52],"networks":[32],"achieve":[33],"higher":[34,91],"accuracies":[35],"than":[36],"models.":[38],"also":[41],"tries":[42],"to":[43,65],"figure":[44],"out":[45],"whether":[46],"dropout":[47,62,79,87],"can":[48,63],"improve":[49],"accuracy":[50],"of":[51,78],"networks.":[53],"for":[57],"Network,":[60],"applying":[61],"lead":[64],"better":[66],"performances":[67,73],"in":[68,74],"certain":[69],"cases":[70],"but":[71],"worse":[72],"others.":[75],"The":[76],"influence":[77],"on":[80],"LSTM":[81],"models":[82],"is":[83],"small.":[84],"Therefore,":[85],"using":[86],"does":[88],"not":[89],"guarantee":[90],"accuracy.":[92]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2018-11-29T00:00:00"}
