{"id":"https://openalex.org/W2303097976","doi":"https://doi.org/10.5815/ijitcs.2016.03.07","title":"Customer Credit Risk Assessment using Artificial Neural Networks","display_name":"Customer Credit Risk Assessment using Artificial Neural Networks","publication_year":2016,"publication_date":"2016-03-07","ids":{"openalex":"https://openalex.org/W2303097976","doi":"https://doi.org/10.5815/ijitcs.2016.03.07","mag":"2303097976"},"language":"en","primary_location":{"id":"doi:10.5815/ijitcs.2016.03.07","doi":"https://doi.org/10.5815/ijitcs.2016.03.07","is_oa":true,"landing_page_url":"https://doi.org/10.5815/ijitcs.2016.03.07","pdf_url":"http://www.mecs-press.org/ijitcs/ijitcs-v8-n3/IJITCS-V8-N3-7.pdf","source":{"id":"https://openalex.org/S2483513740","display_name":"International Journal of Information Technology and Computer Science","issn_l":"2074-9007","issn":["2074-9007","2074-9015"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"International Journal of Information Technology and Computer Science","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"http://www.mecs-press.org/ijitcs/ijitcs-v8-n3/IJITCS-V8-N3-7.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080615552","display_name":"Nasser Mohammadi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I110525433","display_name":"Islamic Azad University, Tehran","ror":"https://ror.org/01kzn7k21","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433"]},{"id":"https://openalex.org/I155419210","display_name":"Islamic Azad University, Science and Research Branch","ror":"https://ror.org/03187yj51","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I155419210"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Nasser Mohammadi","raw_affiliation_strings":["Department of computer engineering, Tehran Science and Research Branch, Islamic Azad University, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of computer engineering, Tehran Science and Research Branch, Islamic Azad University, Iran","institution_ids":["https://openalex.org/I110525433","https://openalex.org/I155419210"]}]},{"author_position":"last","author":{"id":null,"display_name":"Maryam Zangeneh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I110525433","display_name":"Islamic Azad University, Tehran","ror":"https://ror.org/01kzn7k21","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433"]},{"id":"https://openalex.org/I41775361","display_name":"Islamic Azad University Central Tehran Branch","ror":"https://ror.org/03a11m818","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I41775361"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Maryam Zangeneh","raw_affiliation_strings":["Department of computer engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of computer engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I110525433","https://openalex.org/I41775361"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":15.8497,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.98656229,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":"8","issue":"3","first_page":"58","last_page":"66"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.989300012588501,"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":0.989300012588501,"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/T10320","display_name":"Neural Networks and Applications","score":0.0017999999690800905,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.0013000000035390258,"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/multilayer-perceptron","display_name":"multilayer perceptron","score":0.8539999723434448},{"id":"https://openalex.org/keywords/back-propagation-algorithm","display_name":"back-propagation algorithm","score":0.9639999866485596},{"id":"https://openalex.org/keywords/levenberg-marquardt-algorithm","display_name":"Levenberg-Marquardt algorithm","score":0.996999979019165},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"gradient descent","score":0.6940000057220459},{"id":"https://openalex.org/keywords/conjugate-gradient-method","display_name":"conjugate gradient method","score":0.8529999852180481},{"id":"https://openalex.org/keywords/quasi-newton-methods","display_name":"quasi-Newton methods","score":0.6869999766349792},{"id":"https://openalex.org/keywords/hidden-neuron-selection","display_name":"hidden neuron selection","score":0.722000002861023},{"id":"https://openalex.org/keywords/credit-scoring","display_name":"credit scoring","score":0.5960000157356262},{"id":"https://openalex.org/keywords/loan-default-prediction","display_name":"loan default prediction","score":0.7269999980926514},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"receiver operating characteristic","score":0.8309999704360962},{"id":"https://openalex.org/keywords/type-ii-error","display_name":"type II error","score":0.6290000081062317},{"id":"https://openalex.org/keywords/logistic-regression-comparison","display_name":"logistic regression comparison","score":0.8429999947547913},{"id":"https://openalex.org/keywords/decision-tree-comparison","display_name":"decision tree comparison","score":0.9990000128746033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8054889440536499},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7396187782287598},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.5922327637672424},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5342162251472473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5098549723625183},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4924992024898529},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4823974370956421},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4492798447608948},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.41170045733451843},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34898287057876587},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.208701491355896},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5815/ijitcs.2016.03.07","doi":"https://doi.org/10.5815/ijitcs.2016.03.07","is_oa":true,"landing_page_url":"https://doi.org/10.5815/ijitcs.2016.03.07","pdf_url":"http://www.mecs-press.org/ijitcs/ijitcs-v8-n3/IJITCS-V8-N3-7.pdf","source":{"id":"https://openalex.org/S2483513740","display_name":"International Journal of Information Technology and Computer Science","issn_l":"2074-9007","issn":["2074-9007","2074-9015"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"International Journal of Information Technology and Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5815/ijitcs.2016.03.07","doi":"https://doi.org/10.5815/ijitcs.2016.03.07","is_oa":true,"landing_page_url":"https://doi.org/10.5815/ijitcs.2016.03.07","pdf_url":"http://www.mecs-press.org/ijitcs/ijitcs-v8-n3/IJITCS-V8-N3-7.pdf","source":{"id":"https://openalex.org/S2483513740","display_name":"International Journal of Information Technology and Computer Science","issn_l":"2074-9007","issn":["2074-9007","2074-9015"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"International Journal of Information Technology and Computer Science","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/W2303097976.pdf","grobid_xml":"https://content.openalex.org/works/W2303097976.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W349704142","https://openalex.org/W1550982594","https://openalex.org/W1963607983","https://openalex.org/W1973704036","https://openalex.org/W1982120517","https://openalex.org/W1990696070","https://openalex.org/W1997740464","https://openalex.org/W2006544565","https://openalex.org/W2011108441","https://openalex.org/W2055421763","https://openalex.org/W2059447090","https://openalex.org/W2063656687","https://openalex.org/W2070236178","https://openalex.org/W2085839913","https://openalex.org/W2093829413","https://openalex.org/W2116660956","https://openalex.org/W2124776405","https://openalex.org/W2125247651","https://openalex.org/W2135411679","https://openalex.org/W2139364554","https://openalex.org/W2148370449","https://openalex.org/W2155482699","https://openalex.org/W2159047538","https://openalex.org/W2285257517"],"related_works":["https://openalex.org/W2992913135","https://openalex.org/W2138476575","https://openalex.org/W2035279783","https://openalex.org/W2377978014","https://openalex.org/W2354757274","https://openalex.org/W4407055513","https://openalex.org/W4249697958","https://openalex.org/W4248214627","https://openalex.org/W2735993163","https://openalex.org/W10294555"],"abstract_inverted_index":{"Since":[0],"the":[1,19,62,71,164,175,182,206,211,221,230],"granting":[2],"of":[3,70,91,133,143,161,190,196],"banking":[4],"facilities":[5],"in":[6,109,114,163,213,251],"recent":[7],"years":[8],"has":[9,26,256],"faced":[10],"problems":[11],"such":[12,199],"as":[13,154,200],"customer":[14,22,56,214],"credit":[15,23,47,78,215,218],"risk":[16,24,48,216],"and":[17,31,88,94,128,138,177,203],"affects":[18],"profitability":[20],"directly,":[21],"assessment":[25],"become":[27],"imperative":[28],"for":[29,140,247],"banks":[30],"it":[32,60],"is":[33,52,152,232,240],"used":[34,153,241],"to":[35,54,157,174,229,242,253],"distinguish":[36],"good":[37,82,86],"applicants":[38],"from":[39],"those":[40],"who":[41],"will":[42],"probably":[43],"default":[44],"on":[45],"repayments.In":[46],"assessment,":[49],"a":[50,81,155,194,248],"score":[51,65],"assigned":[53],"each":[55],"then":[57],"by":[58,186],"comparing":[59,187],"with":[61,193],"cut-off":[63,245],"point":[64,246],"which":[66],"distinguishes":[67],"two":[68,77],"classes":[69],"applicants,":[72],"customers":[73],"are":[74],"classified":[75],"into":[76],"statuses":[79],"either":[80],"or":[83],"bad":[84],"applicant.Regarding":[85],"performance":[87,180,189],"their":[89,146],"ability":[90],"classification,":[92],"generalization":[93],"learning":[95],"patterns,":[96],"Multilayer":[97],"Perceptron":[98],"Neural":[99],"Network":[100],"model":[101,113,209,231,249],"trained":[102],"using":[103],"various":[104],"Back-Propagation":[105],"(BP)":[106],"algorithms":[107,198],"considered":[108],"designing":[110],"an":[111],"evaluation":[112],"this":[115],"study.The":[116],"BP":[117],"algorithms,":[118],"Levenberg-Marquardt":[119],"(LM),":[120],"Gradient":[121],"descent,":[122],"Conjugate":[123],"gradient,":[124],"Resilient,":[125],"BFGS":[126],"Quasinewton,":[127],"One-step":[129],"secant":[130],"were":[131],"utilized.Each":[132],"these":[134],"six":[135],"networks":[136],"runs":[137],"trains":[139],"different":[141],"numbers":[142],"neurons":[144,162],"within":[145],"hidden":[147,165],"layer.Mean":[148],"squared":[149],"error":[150,226,260],"(MSE)":[151],"criterion":[156],"specify":[158],"optimum":[159],"number":[160,195],"layer.The":[166],"results":[167],"showed":[168],"that":[169,223,250],"LM":[170],"algorithm":[171],"converges":[172],"faster":[173],"network":[176,192,208],"achieves":[178],"better":[179],"than":[181],"other":[183],"algorithms.At":[184],"last,":[185],"classification":[188,197],"neural":[191,207],"Logistic":[201],"Regression":[202],"Decision":[204],"Tree,":[205],"outperformed":[210],"others":[212],"assessment.In":[217],"models,":[219],"because":[220],"cost":[222],"Type":[224,258],"II":[225,259],"rate":[227],"imposes":[228],"too":[233],"high,":[234],"therefore,":[235],"Receiver":[236],"Operating":[237],"Characteristic":[238],"curve":[239],"find":[243],"appropriate":[244],"addition":[252],"high":[254],"Accuracy,":[255],"lower":[257],"rate.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}
