{"id":"https://openalex.org/W4388936926","doi":"https://doi.org/10.1109/icccnt56998.2023.10307009","title":"Prediction of Insurance Premium using Machine Learning with an Adaptive Approach","display_name":"Prediction of Insurance Premium using Machine Learning with an Adaptive Approach","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388936926","doi":"https://doi.org/10.1109/icccnt56998.2023.10307009"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10307009","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10307009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5109065759","display_name":"Sartaj Ahmad","orcid":"https://orcid.org/0009-0008-0266-638X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sartaj Ahmad","raw_affiliation_strings":["Affiliated to AKTU, Lucknow,KIET Group of Institutions,India","KIET Group of Institutions, Affiliated to AKTU, Lucknow, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Affiliated to AKTU, Lucknow,KIET Group of Institutions,India","institution_ids":[]},{"raw_affiliation_string":"KIET Group of Institutions, Affiliated to AKTU, Lucknow, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078083467","display_name":"Ajay Agarwal","orcid":"https://orcid.org/0000-0003-0499-5511"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ajay Agarwal","raw_affiliation_strings":["Affiliated to AKTU, Lucknow,KIET Group of Institutions,India","KIET Group of Institutions, Affiliated to AKTU, Lucknow, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Affiliated to AKTU, Lucknow,KIET Group of Institutions,India","institution_ids":[]},{"raw_affiliation_string":"KIET Group of Institutions, Affiliated to AKTU, Lucknow, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008117441","display_name":"Huzaifa Ansari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huzaifa Ansari","raw_affiliation_strings":["Affiliated to AKTU, Lucknow,KIET Group of Institutions,India","KIET Group of Institutions, Affiliated to AKTU, Lucknow, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Affiliated to AKTU, Lucknow,KIET Group of Institutions,India","institution_ids":[]},{"raw_affiliation_string":"KIET Group of Institutions, Affiliated to AKTU, Lucknow, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5435,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85118845,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9909999966621399,"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"}},"topics":[{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9909999966621399,"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/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9782999753952026,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13693","display_name":"Smart Systems and Machine Learning","score":0.953499972820282,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/random-forest","display_name":"Random forest","score":0.6650588512420654},{"id":"https://openalex.org/keywords/payroll","display_name":"Payroll","score":0.6345793604850769},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5719879269599915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5313587188720703},{"id":"https://openalex.org/keywords/actuarial-science","display_name":"Actuarial science","score":0.4676342308521271},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4555160105228424},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45495644211769104},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4522833824157715},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.2563377320766449},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10445770621299744}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6650588512420654},{"id":"https://openalex.org/C2778873167","wikidata":"https://www.wikidata.org/wiki/Q59434791","display_name":"Payroll","level":2,"score":0.6345793604850769},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5719879269599915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5313587188720703},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.4676342308521271},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4555160105228424},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45495644211769104},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4522833824157715},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2563377320766449},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10445770621299744},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10307009","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10307009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2796265311","https://openalex.org/W2796397915","https://openalex.org/W2797419437","https://openalex.org/W2800023445","https://openalex.org/W2809986361","https://openalex.org/W2917490808","https://openalex.org/W2966218717","https://openalex.org/W3019435264","https://openalex.org/W3118299338","https://openalex.org/W3119309298","https://openalex.org/W3119914010","https://openalex.org/W3137127133","https://openalex.org/W3163534539","https://openalex.org/W3190860874","https://openalex.org/W3197276397","https://openalex.org/W6792324637"],"related_works":["https://openalex.org/W4390591544","https://openalex.org/W4312623679","https://openalex.org/W2799459078","https://openalex.org/W4206119624","https://openalex.org/W4254766821","https://openalex.org/W2266949634","https://openalex.org/W1531823379","https://openalex.org/W3118482862","https://openalex.org/W3081174928","https://openalex.org/W193181552"],"abstract_inverted_index":{"The":[0,63,173],"insurance":[1,20,42,105,224],"market":[2],"is":[3,37,58,68,179],"very":[4,59],"large":[5],"and":[6,44,98,114,130,133,144,167,238],"expanding":[7],"day":[8],"by":[9,181,226],"day.":[10],"There":[11],"are":[12,196,221],"many":[13],"parameters":[14],"to":[15,26,39,49,69,81,91,138,150,155,165,198,210,213],"consider":[16],"before":[17,31],"deciding":[18],"on":[19,74],"premiums.":[21],"Sometimes":[22],"it":[23,36,152],"becomes":[24],"difficult":[25],"browse":[27],"all":[28],"the":[29,41,83,176,183,187,191,216,228,232,239],"documents":[30],"applying":[32],"for":[33,215],"insurance,":[34],"so":[35],"necessary":[38],"understand":[40],"industry":[43],"point":[45],"out":[46],"issues":[47],"related":[48],"competition":[50],"in":[51,61,223],"that":[52,161,220],"industry.":[53],"This":[54,119],"type":[55],"of":[56,65,78,175,190,231],"company":[57],"interested":[60],"forecasting.":[62],"goal":[64],"this":[66],"article":[67],"find":[70],"accurate":[71,116],"predictions":[72],"based":[73],"considering":[75],"different":[76],"dimensions":[77],"machine":[79,123,170],"learning":[80,88,171],"reduce":[82],"company's":[84],"financial":[85,201],"losses.":[86,100],"Machine":[87],"helps":[89],"companies":[90,106],"optimize":[92],"their":[93],"services":[94],"with":[95,141,186,235],"greater":[96],"accuracy":[97,143],"fewer":[99],"It":[101],"can":[102,162],"also":[103],"help":[104],"effectively":[107],"screen":[108],"cases,":[109],"evaluate":[110],"them":[111],"more":[112],"accurately,":[113],"make":[115],"cost":[117],"forecasts.":[118],"research":[120],"work":[121],"uses":[122],"learning-based":[124],"methods":[125],"like":[126],"linear":[127],"regression,":[128],"KStar,":[129],"Random":[131],"Forest":[132],"suggests":[134],"a":[135,157,169],"suitable":[136],"method":[137],"produce":[139],"results":[140],"high":[142],"less":[145],"relative":[146],"error.":[147],"In":[148],"addition":[149],"this,":[151],"demonstrates":[153],"how":[154],"create":[156],"specific":[158],"data":[159],"subset":[160],"be":[163],"used":[164],"test":[166],"train":[168],"system.":[172,241],"effectiveness":[174],"suggested":[177],"strategy":[178],"assessed":[180],"contrasting":[182],"estimated":[184],"value":[185,189],"actual":[188],"simulated":[192],"data.":[193],"Insurance":[194],"firms":[195],"capable":[197],"construct":[199],"consistent":[200],"structures,":[202],"such":[203],"as":[204],"monthly":[205],"premiums":[206],"or":[207],"payroll":[208],"taxes,":[209],"provide":[211],"funds":[212],"pay":[214],"medical":[217,240],"benefit":[218],"agreements":[219],"defined":[222],"policies":[225],"calculating":[227],"whole":[229],"risk":[230],"expenses":[233],"associated":[234],"health":[236],"care":[237]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
