{"id":"https://openalex.org/W4406379610","doi":"https://doi.org/10.1145/3708360.3708389","title":"A machine learning based Anti-fraud approach for life insurance company : A Case Study in a life insurance company","display_name":"A machine learning based Anti-fraud approach for life insurance company : A Case Study in a life insurance company","publication_year":2024,"publication_date":"2024-11-08","ids":{"openalex":"https://openalex.org/W4406379610","doi":"https://doi.org/10.1145/3708360.3708389"},"language":"en","primary_location":{"id":"doi:10.1145/3708360.3708389","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3708360.3708389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Mathematics and Machine Learning","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/A5108649722","display_name":"Yuwen Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I32820368","display_name":"Guangdong Polytechnic of Science and Technology","ror":"https://ror.org/01wq2p249","country_code":"CN","type":"education","lineage":["https://openalex.org/I32820368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuwen Jiang","raw_affiliation_strings":["School of Computer Science and Engineering, Guangzhou Institute of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0001-4022-1436","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Guangzhou Institute of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I32820368"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiangang Feng","orcid":"https://orcid.org/0009-0001-2269-2728"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiangang Feng","raw_affiliation_strings":["School of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, Hainan, China"],"raw_orcid":"https://orcid.org/0009-0001-2269-2728","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, Hainan, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112391173","display_name":"C Q Jiang","orcid":"https://orcid.org/0009-0003-4842-5091"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengxuan Jiang","raw_affiliation_strings":["International Course Center of GuangZhou No.6 Middle School, Guangzhou, GuangDong, China"],"raw_orcid":"https://orcid.org/0009-0003-4842-5091","affiliations":[{"raw_affiliation_string":"International Course Center of GuangZhou No.6 Middle School, Guangzhou, GuangDong, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22037487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"179","last_page":"185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9894000291824341,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9894000291824341,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9478999972343445,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9114000201225281,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/life-insurance","display_name":"Life insurance","score":0.8512805700302124},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.5631006360054016},{"id":"https://openalex.org/keywords/actuarial-science","display_name":"Actuarial science","score":0.4705132842063904},{"id":"https://openalex.org/keywords/insurance-industry","display_name":"Insurance industry","score":0.41550925374031067},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4035884439945221}],"concepts":[{"id":"https://openalex.org/C3020299011","wikidata":"https://www.wikidata.org/wiki/Q626608","display_name":"Life insurance","level":2,"score":0.8512805700302124},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.5631006360054016},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.4705132842063904},{"id":"https://openalex.org/C2984333267","wikidata":"https://www.wikidata.org/wiki/Q43183","display_name":"Insurance industry","level":2,"score":0.41550925374031067},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4035884439945221}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3708360.3708389","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3708360.3708389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Mathematics and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5299999713897705,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W4200070920","https://openalex.org/W4299689471","https://openalex.org/W4383695008"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W383872877","https://openalex.org/W2183971923","https://openalex.org/W2043090105","https://openalex.org/W2488650094","https://openalex.org/W1633247902","https://openalex.org/W2386459834","https://openalex.org/W2577725740","https://openalex.org/W2056097592","https://openalex.org/W2589770271"],"abstract_inverted_index":{"In":[0],"China,":[1],"when":[2],"life":[3,129],"insurance":[4,38,130],"companies":[5],"tried":[6],"to":[7,17,20,35,126],"develop":[8],"agent":[9,78],"channels,":[10],"they":[11],"often":[12],"applied":[13,111,125],"aggressive":[14],"incentive":[15,33,45],"policies":[16,34,39,67],"encourage":[18],"agents":[19,69,96],"sell":[21],"more":[22],"products":[23],"and":[24,40,76,80,97,137],"enlarge":[25],"sales":[26,81],"team.":[27],"Agents":[28],"can":[29,61],"easily":[30],"utilize":[31],"improper":[32],"forge":[36],"false":[37],"defraud":[41],"the":[42,64,72,89,112,114,142],"company":[43],"of":[44,68,74,84,91,116],"fees.":[46],"This":[47,121],"article":[48],"proposed":[49],"a":[50,118],"\u201cfalse":[51,119],"policy\u201d":[52],"detection":[53],"approach":[54,122],"based":[55],"on":[56],"machine":[57],"learning":[58],"algorithm,":[59],"which":[60],"effectively":[62],"detect":[63],"suspected":[65],"arbitrage":[66],"by":[70],"combining":[71],"information":[73,79,83],"policyholder":[75],"insured,":[77],"department":[82],"newly":[85],"underwritten":[86],"policies.":[87],"Firstly,":[88],"data":[90],"policy":[92,94],"holders,":[93],"features,":[95],"business":[98],"unit":[99],"were":[100],"integrates":[101],"as":[102],"an":[103],"analytical":[104],"flat":[105],"table,":[106],"than":[107],"Logistic":[108],"Regression":[109],"was":[110],"predict":[113],"probability":[115],"being":[117],"policy\u201d.":[120],"has":[123,133],"been":[124],"one":[127],"domestic":[128],"company,":[131],"it":[132],"showed":[134],"perfect":[135],"performance":[136],"saved":[138],"considerable":[139],"losses":[140],"for":[141],"company.":[143]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}