{"id":"https://openalex.org/W2946740123","doi":null,"title":"Machine learning approach for credit score analysis : a case study of predicting mortgage loan defaults","display_name":"Machine learning approach for credit score analysis : a case study of predicting mortgage loan defaults","publication_year":2019,"publication_date":"2019-02-08","ids":{"openalex":"https://openalex.org/W2946740123","mag":"2946740123"},"language":"en","primary_location":{"id":"pmh:oai:run.unl.pt:10362/62427","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10362/62427","pdf_url":"http://hdl.handle.net/10362/62427","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"master thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/10362/62427","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068399820","display_name":"Mohamed Hani Abdelhamid Mohamed Tawfik Elmasry","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohamed Hani Abdelhamid Mohamed Tawfik Elmasry","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5068399820"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9976000189781189,"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":0.9976000189781189,"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":0.0010000000474974513,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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-evaluation","display_name":"creditworthiness assessment","score":0.9750000238418579},{"id":"https://openalex.org/keywords/stacking-ensemble","display_name":"stacking ensemble","score":0.7039999961853027},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.5460000038146973},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7760000228881836},{"id":"https://openalex.org/keywords/classification-and-regression-trees","display_name":"classification and regression trees","score":0.8529999852180481},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.8619999885559082},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9890000224113464},{"id":"https://openalex.org/keywords/default-probability","display_name":"default probability","score":0.3330000042915344}],"concepts":[{"id":"https://openalex.org/C69637215","wikidata":"https://www.wikidata.org/wiki/Q702362","display_name":"Default","level":2,"score":0.8798047304153442},{"id":"https://openalex.org/C2777138686","wikidata":"https://www.wikidata.org/wiki/Q1787103","display_name":"Credit score","level":2,"score":0.6281849145889282},{"id":"https://openalex.org/C2777764128","wikidata":"https://www.wikidata.org/wiki/Q189539","display_name":"Loan","level":2,"score":0.5792807340621948},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.46108314394950867},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3461431860923767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3394124507904053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33705586194992065},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.30049756169319153},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2969478368759155},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.26194441318511963}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:run.unl.pt:10362/62427","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10362/62427","pdf_url":"http://hdl.handle.net/10362/62427","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"master thesis"},{"id":"mag:2946740123","doi":null,"is_oa":false,"landing_page_url":"https://run.unl.pt/bitstream/10362/62427/4/TEGI0439.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:run.unl.pt:10362/62427","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10362/62427","pdf_url":"http://hdl.handle.net/10362/62427","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"master thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2946740123.pdf","grobid_xml":"https://content.openalex.org/works/W2946740123.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1519043595","https://openalex.org/W1528428636","https://openalex.org/W1573528873","https://openalex.org/W1750536198","https://openalex.org/W1831729862","https://openalex.org/W1929275767","https://openalex.org/W1930624869","https://openalex.org/W1975846642","https://openalex.org/W1984514442","https://openalex.org/W1985258161","https://openalex.org/W1999692438","https://openalex.org/W2020568219","https://openalex.org/W2026367155","https://openalex.org/W2041552396","https://openalex.org/W2046260364","https://openalex.org/W2052611008","https://openalex.org/W2058646687","https://openalex.org/W2067196267","https://openalex.org/W2088048599","https://openalex.org/W2095526364","https://openalex.org/W2115098571","https://openalex.org/W2125055259","https://openalex.org/W2131816657","https://openalex.org/W2148143831","https://openalex.org/W2149467648","https://openalex.org/W2158698691","https://openalex.org/W2163848058","https://openalex.org/W2331466950","https://openalex.org/W2754214338","https://openalex.org/W2788025656","https://openalex.org/W2903950532","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2921632426","https://openalex.org/W3121588992","https://openalex.org/W3122429246","https://openalex.org/W3125536768","https://openalex.org/W4230887410","https://openalex.org/W4239510810","https://openalex.org/W4300009529"],"related_works":["https://openalex.org/W7039457547","https://openalex.org/W7018412160","https://openalex.org/W7033164942","https://openalex.org/W2968374835","https://openalex.org/W3195282614","https://openalex.org/W6981119716","https://openalex.org/W6885063830","https://openalex.org/W7036763486","https://openalex.org/W6990159379","https://openalex.org/W7161244316"],"abstract_inverted_index":{"To":[0,112],"effectively":[1],"manage":[2],"credit":[3,27],"score":[4],"analysis,":[5],"financial":[6,55],"institutions":[7],"instigated":[8],"techniques":[9,202],"and":[10,101,108,177,185],"models":[11,81],"that":[12,48,199],"are":[13,212],"mainly":[14],"designed":[15],"for":[16,142],"the":[17,21,26,45,58,83,115,130,136,149,165,172,178,182,187,206],"purpose":[18],"of":[19,66,85,135,174,195],"improving":[20],"process":[22],"assessing":[23],"creditworthiness":[24],"during":[25],"evaluation":[28],"process.":[29],"The":[30,140],"foremost":[31],"objective":[32],"is":[33,49,145,192],"to":[34,41,52,68,77,128,203],"discriminate":[35],"their":[36,54,70],"clients":[37],"\u2013":[38,40,122,124,132,134],"borrowers":[39],"fall":[42],"either":[43],"in":[44,82,164],"non-defaulter":[46],"group,":[47],"more":[50],"likely":[51],"pay":[53,69],"obligations,":[56],"or":[57],"defaulter":[59],"one":[60],"which":[61],"has":[62],"a":[63,118],"higher":[64],"probability":[65],"failing":[67],"debts.":[71],"In":[72],"this":[73,143,158],"paper,":[74],"we":[75,160],"devote":[76],"use":[78],"machine":[79,94],"learning":[80,95],"prediction":[84],"mortgage":[86],"defaults.":[87],"This":[88,190],"study":[89,144,191],"employs":[90],"various":[91,196],"single":[92],"classification":[93],"methodologies":[96],"including":[97],"Logistic":[98],"Regression,":[99],"Classification":[100],"Regression":[102],"Trees,":[103],"Random":[104],"Forest,":[105],"K-Nearest":[106],"Neighbors,":[107],"Support":[109],"Vector":[110],"Machine.":[111],"further":[113,204],"improve":[114],"predictive":[116],"power,":[117],"meta-algorithm":[119],"ensemble":[120],"approach":[121],"stacking":[123],"will":[125],"be":[126],"introduced":[127],"combine":[129],"outputs":[131],"probabilities":[133],"afore":[137],"mentioned":[138],"methods.":[139],"sample":[141],"solely":[146],"based":[147],"on":[148],"publicly":[150],"provided":[151],"dataset":[152],"by":[153,180],"Freddie":[154],"Mac.":[155],"By":[156],"modelling":[157],"approach,":[159],"achieve":[161],"an":[162,193],"improvement":[163],"model":[166,207],"predictability":[167],"performance.":[168],"We":[169],"then":[170],"compare":[171],"performance":[173],"each":[175],"model,":[176],"meta-learner,":[179],"plotting":[181],"ROC":[183],"Curve":[184],"computing":[186],"AUC":[188],"rate.":[189],"extension":[194],"preceding":[197],"studies":[198],"used":[200],"different":[201,217],"enhance":[205],"predictivity.":[208],"Finally,":[209],"our":[210],"results":[211],"compared":[213],"with":[214],"work":[215],"from":[216],"authors.":[218]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2019-05-29T00:00:00"}
