{"id":"https://openalex.org/W4392982132","doi":"https://doi.org/10.1109/cvmi59935.2023.10464518","title":"SentiNet: Empowering Robust Loan Default Prediction through Ensemble Modeling","display_name":"SentiNet: Empowering Robust Loan Default Prediction through Ensemble Modeling","publication_year":2023,"publication_date":"2023-12-10","ids":{"openalex":"https://openalex.org/W4392982132","doi":"https://doi.org/10.1109/cvmi59935.2023.10464518"},"language":"en","primary_location":{"id":"doi:10.1109/cvmi59935.2023.10464518","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cvmi59935.2023.10464518","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","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/A5113147487","display_name":"Prakrit Pathak","orcid":null},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Prakrit Pathak","raw_affiliation_strings":["IIIT-Delhi,CSE Department,Delhi,India","CSE Department, IIIT-Delhi, Delhi, India"],"affiliations":[{"raw_affiliation_string":"IIIT-Delhi,CSE Department,Delhi,India","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]},{"raw_affiliation_string":"CSE Department, IIIT-Delhi, Delhi, India","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075951609","display_name":"Akash Jain","orcid":null},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Akash Jain","raw_affiliation_strings":["IIIT-Delhi,CSE Department,Delhi,India","CSE Department, IIIT-Delhi, Delhi, India"],"affiliations":[{"raw_affiliation_string":"IIIT-Delhi,CSE Department,Delhi,India","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]},{"raw_affiliation_string":"CSE Department, IIIT-Delhi, Delhi, India","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001987532","display_name":"Mohit Bansal","orcid":"https://orcid.org/0000-0001-5522-1351"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mohit Bansal","raw_affiliation_strings":["IIIT-Delhi,CSE Department,Delhi,India","CSE Department, IIIT-Delhi, Delhi, India"],"affiliations":[{"raw_affiliation_string":"IIIT-Delhi,CSE Department,Delhi,India","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]},{"raw_affiliation_string":"CSE Department, IIIT-Delhi, Delhi, India","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071186894","display_name":"Prashant Singh Rana","orcid":"https://orcid.org/0000-0002-0142-7925"},"institutions":[{"id":"https://openalex.org/I9747756","display_name":"Atal Bihari Vajpayee Indian Institute of Information Technology and Management","ror":"https://ror.org/008b3ap06","country_code":"IN","type":"education","lineage":["https://openalex.org/I9747756"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prashant Singh Rana","raw_affiliation_strings":["Madhav Institute of Technology and Science,IT Department,Gwalior,India","IT Department, Madhav Institute of Technology and Science, Gwalior, India"],"affiliations":[{"raw_affiliation_string":"Madhav Institute of Technology and Science,IT Department,Gwalior,India","institution_ids":["https://openalex.org/I9747756"]},{"raw_affiliation_string":"IT Department, Madhav Institute of Technology and Science, Gwalior, India","institution_ids":["https://openalex.org/I9747756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5113147487"],"corresponding_institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":1.4564,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.86159237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9693999886512756,"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":0.9693999886512756,"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/T11995","display_name":"FinTech, Crowdfunding, Digital Finance","score":0.9399999976158142,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6455872058868408},{"id":"https://openalex.org/keywords/loan","display_name":"Loan","score":0.4643094539642334},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4384954273700714},{"id":"https://openalex.org/keywords/default","display_name":"Default","score":0.43194687366485596},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3558310866355896},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.16254469752311707},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.14373928308486938}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6455872058868408},{"id":"https://openalex.org/C2777764128","wikidata":"https://www.wikidata.org/wiki/Q189539","display_name":"Loan","level":2,"score":0.4643094539642334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4384954273700714},{"id":"https://openalex.org/C69637215","wikidata":"https://www.wikidata.org/wiki/Q702362","display_name":"Default","level":2,"score":0.43194687366485596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3558310866355896},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.16254469752311707},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.14373928308486938}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvmi59935.2023.10464518","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cvmi59935.2023.10464518","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2031767704","https://openalex.org/W4220971169","https://openalex.org/W4221035560","https://openalex.org/W4233278670","https://openalex.org/W4237996848","https://openalex.org/W4319971637","https://openalex.org/W4323036383"],"related_works":["https://openalex.org/W4385561904","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3122155953","https://openalex.org/W3161551118","https://openalex.org/W2768429096","https://openalex.org/W606680577","https://openalex.org/W2612601761","https://openalex.org/W3046775127","https://openalex.org/W3107602296"],"abstract_inverted_index":{"Loan":[0],"default":[1,23,90,112],"prediction":[2,24,125],"is":[3,131,174],"a":[4,18,159],"critical":[5],"task":[6],"for":[7,110],"lending":[8],"institutions":[9],"to":[10,63,88],"minimize":[11],"financial":[12],"risks.":[13],"This":[14,176],"research":[15],"paper":[16],"presents":[17],"comprehensive":[19],"analysis":[20,37],"of":[21,44,119,139,147,189],"loan":[22,89,111,155],"using":[25],"the":[26,42,56,107,137,150,179,190,195],"Lending":[27],"Club":[28],"dataset.":[29,191],"The":[30,133,192],"study":[31],"includes":[32],"data":[33,36,39,59,109],"filtering,":[34],"exploratory":[35],"(EDA),":[38],"standardization,":[40],"and":[41,61,67,74,85,101,164,184,199],"application":[43],"basic":[45],"machine":[46],"learning":[47,129],"(ML)":[48],"models":[49,115],"followed":[50],"by":[51,182],"an":[52,117,127,153],"ensemble":[53,128,134,196],"model.":[54],"Initially,":[55],"dataset":[57],"undergoes":[58],"filtering":[60],"EDA":[62,79],"eliminate":[64],"irrelevant":[65],"features":[66],"gain":[68],"insights.":[69],"Missing":[70],"values":[71],"are":[72,77,104],"handled,":[73],"potential":[75],"outliers":[76],"detected.":[78],"techniques":[80],"reveal":[81],"variable":[82],"distributions,":[83],"correlations,":[84],"patterns":[86],"relevant":[87],"prediction.":[91,113],"Basic":[92],"ML":[93],"models,":[94,142],"such":[95],"as":[96],"logistic":[97],"regression,":[98],"decision":[99],"trees,":[100],"random":[102],"forests,":[103],"trained":[105],"on":[106,186],"preprocessed":[108],"These":[114],"achieve":[116],"accuracy":[118,146],"approximately":[120],"73%.":[121],"To":[122,168],"further":[123],"enhance":[124],"accuracy,":[126],"approach":[130],"employed.":[132],"model":[135,151,170],"combines":[136],"predictions":[138],"multiple":[140,202],"individual":[141],"resulting":[143],"in":[144],"improved":[145],"76.8%.":[148],"Additionally,":[149],"maintains":[152],"80%":[154],"approval":[156],"rate,":[157],"striking":[158],"balance":[160],"between":[161],"risk":[162],"mitigation":[163],"providing":[165],"credit":[166],"access.":[167],"assess":[169],"consistency,":[171],"k-fold":[172],"cross-validation":[173],"applied.":[175],"technique":[177],"evaluates":[178],"model's":[180,197],"performance":[181],"training":[183],"testing":[185],"different":[187],"subsets":[188],"results":[193],"demonstrate":[194],"stability":[198],"reliability":[200],"across":[201],"folds.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}