{"id":"https://openalex.org/W2144178563","doi":"https://doi.org/10.1080/09720510.2014.961767","title":"A Neural Network Approach for Microfinance Credit Scoring","display_name":"A Neural Network Approach for Microfinance Credit Scoring","publication_year":2015,"publication_date":"2015-03-04","ids":{"openalex":"https://openalex.org/W2144178563","doi":"https://doi.org/10.1080/09720510.2014.961767","mag":"2144178563"},"language":"en","primary_location":{"id":"doi:10.1080/09720510.2014.961767","doi":"https://doi.org/10.1080/09720510.2014.961767","is_oa":false,"landing_page_url":"https://doi.org/10.1080/09720510.2014.961767","pdf_url":null,"source":{"id":"https://openalex.org/S2764706862","display_name":"Journal of Statistics and Management Systems","issn_l":"0972-0510","issn":["0972-0510","2169-0014"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","anvur-scientific","cwts-core","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Statistics and Management Systems","raw_type":"journal-article"},"volume":null,"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/A5066396732","display_name":"Kiruthika","orcid":"https://orcid.org/0000-0001-9655-702X","observed_orcids":["https://orcid.org/0000-0001-9655-702X"]},"institutions":[{"id":"https://openalex.org/I175691731","display_name":"Pondicherry University","ror":"https://ror.org/01a3mef16","country_code":"IN","type":"education","lineage":["https://openalex.org/I175691731"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kiruthika","raw_affiliation_strings":["Department of Statistics Pondicherry University Puducherry -14, India. E-mails: kiruthika.pu@gmail.com, dilsha.m@gmail.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics Pondicherry University Puducherry -14, India. E-mails: kiruthika.pu@gmail.com, dilsha.m@gmail.com","institution_ids":["https://openalex.org/I175691731"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062454196","display_name":"M. K. Dilsha","orcid":"https://orcid.org/0009-0006-7437-6539","observed_orcids":["https://orcid.org/0009-0006-7437-6539"]},"institutions":[{"id":"https://openalex.org/I175691731","display_name":"Pondicherry University","ror":"https://ror.org/01a3mef16","country_code":"IN","type":"education","lineage":["https://openalex.org/I175691731"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Dilsha","raw_affiliation_strings":["Department of Statistics Pondicherry University Puducherry -14, India. E-mails: kiruthika.pu@gmail.com, dilsha.m@gmail.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics Pondicherry University Puducherry -14, India. E-mails: kiruthika.pu@gmail.com, dilsha.m@gmail.com","institution_ids":["https://openalex.org/I175691731"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I175691731"],"apc_list":null,"apc_paid":null,"fwci":8.2391,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.97068963,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"18","issue":"1-2","first_page":"121","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9878000020980835,"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":0.9878000020980835,"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/T10987","display_name":"Microfinance and Financial Inclusion","score":0.00860000029206276,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.000699999975040555,"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-scoring","display_name":"credit scoring","score":0.8999999761581421},{"id":"https://openalex.org/keywords/microfinance","display_name":"microfinance","score":0.996999979019165},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.9879999756813049},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.9919999837875366},{"id":"https://openalex.org/keywords/credit-scorecards","display_name":"credit scorecards","score":0.8479999899864197},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.7960000038146973},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.9739999771118164},{"id":"https://openalex.org/keywords/model-evaluation","display_name":"model evaluation","score":0.8330000042915344}],"concepts":[{"id":"https://openalex.org/C28129649","wikidata":"https://www.wikidata.org/wiki/Q926217","display_name":"Microfinance","level":2,"score":0.9292516708374023},{"id":"https://openalex.org/C199004533","wikidata":"https://www.wikidata.org/wiki/Q210127","display_name":"Balanced scorecard","level":2,"score":0.7507386207580566},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7319042682647705},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7303633093833923},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.5119836926460266},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4850601255893707},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4837620258331299},{"id":"https://openalex.org/C2777138686","wikidata":"https://www.wikidata.org/wiki/Q1787103","display_name":"Credit score","level":2,"score":0.4779410660266876},{"id":"https://openalex.org/C205208723","wikidata":"https://www.wikidata.org/wiki/Q372765","display_name":"Credit rating","level":2,"score":0.4699813425540924},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.4327143132686615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41717803478240967},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.41574597358703613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39476752281188965},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.27375298738479614},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.10248112678527832},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/09720510.2014.961767","doi":"https://doi.org/10.1080/09720510.2014.961767","is_oa":false,"landing_page_url":"https://doi.org/10.1080/09720510.2014.961767","pdf_url":null,"source":{"id":"https://openalex.org/S2764706862","display_name":"Journal of Statistics and Management Systems","issn_l":"0972-0510","issn":["0972-0510","2169-0014"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","anvur-scientific","cwts-core","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Statistics and Management Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1975897238","https://openalex.org/W1976967730","https://openalex.org/W1981976602","https://openalex.org/W2005510983","https://openalex.org/W2030444281","https://openalex.org/W2058595220","https://openalex.org/W2059447090","https://openalex.org/W2076983043","https://openalex.org/W2089811952","https://openalex.org/W2117332653","https://openalex.org/W2121394390","https://openalex.org/W2130812836","https://openalex.org/W2138988968","https://openalex.org/W2154172373","https://openalex.org/W2295785951","https://openalex.org/W3194941070","https://openalex.org/W4248841288"],"related_works":["https://openalex.org/W2347185709","https://openalex.org/W2059447090","https://openalex.org/W2750962508","https://openalex.org/W3087037074","https://openalex.org/W2984893365","https://openalex.org/W2997732387","https://openalex.org/W2140743335","https://openalex.org/W2015768982","https://openalex.org/W6997247994","https://openalex.org/W2740917065"],"abstract_inverted_index":{"Credit":[0],"scoring":[1,16,75],"has":[2],"been":[3,50],"successfully":[4],"applied":[5,51],"in":[6,52,73],"domains":[7],"as":[8],"mortgage":[9],"loans":[10],"and":[11,30,69,82,89,98],"credit":[12,15,40,74],"scorecards.":[13],"However,":[14],"with":[17],"the":[18,28,43,65,96,102],"microfinance":[19,77],"industry":[20],"is":[21],"a":[22,39],"relatively":[23],"recent":[24],"application.":[25],"Most":[26],"of":[27,55,63,95,100],"financial":[29],"banking":[31],"institutions":[32],"are":[33,104],"using":[34],"logistic":[35,70],"regression":[36,71],"to":[37],"build":[38],"scorecard.":[41],"Among":[42],"new":[44],"method":[45],"artificial":[46,66],"neural":[47,67],"networks":[48],"have":[49],"various":[53],"studies":[54],"scorecard":[56],"modelling.":[57],"This":[58],"paper":[59],"analyzes":[60],"comparative":[61],"descriptions":[62],"both":[64,101],"network":[68],"model":[72,90],"for":[76],"data.":[78],"The":[79],"theoretical":[80],"features":[81],"properties,":[83],"which":[84],"include":[85],"parameters,":[86],"variable":[87],"selection,":[88],"evaluation,":[91],"followed":[92],"by":[93],"comparisons":[94],"advantages":[97],"disadvantages":[99],"models":[103],"analytically":[105],"reviewed.":[106]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}