{"id":"https://openalex.org/W3206477128","doi":"https://doi.org/10.1109/indin45523.2021.9557545","title":"A Predicting Model For Accounting Fraud Based On Ensemble Learning","display_name":"A Predicting Model For Accounting Fraud Based On Ensemble Learning","publication_year":2021,"publication_date":"2021-07-21","ids":{"openalex":"https://openalex.org/W3206477128","doi":"https://doi.org/10.1109/indin45523.2021.9557545","mag":"3206477128"},"language":"en","primary_location":{"id":"doi:10.1109/indin45523.2021.9557545","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin45523.2021.9557545","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","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/A5041013734","display_name":"Yunchuan Sun","orcid":"https://orcid.org/0000-0001-6064-3380"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunchuan Sun","raw_affiliation_strings":["International Inatitute of Big Data in Finance Business School, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International Inatitute of Big Data in Finance Business School, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001235535","display_name":"Zixiu Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zixiu Ma","raw_affiliation_strings":["International Inatitute of Big Data in Finance Business School, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International Inatitute of Big Data in Finance Business School, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062788280","display_name":"Xiaoping Zeng","orcid":"https://orcid.org/0000-0002-9083-3860"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoping Zeng","raw_affiliation_strings":["International Inatitute of Big Data in Finance Business School, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International Inatitute of Big Data in Finance Business School, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021450973","display_name":"Yao Guo","orcid":"https://orcid.org/0000-0001-5064-5286"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Guo","raw_affiliation_strings":["Artificial Intelligence School, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence School, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041013734"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.6799,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.7610556,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9976000189781189,"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":0.9976000189781189,"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/T10081","display_name":"Auditing, Earnings Management, Governance","score":0.9921000003814697,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9484999775886536,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6692736148834229},{"id":"https://openalex.org/keywords/accounting","display_name":"Accounting","score":0.5865265130996704},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5294151902198792},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.5176563858985901},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5089972019195557},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.43727853894233704},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.43482688069343567},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.42833754420280457},{"id":"https://openalex.org/keywords/financial-ratio","display_name":"Financial ratio","score":0.42823970317840576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3708495497703552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3550925552845001},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1725986897945404},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.11749249696731567}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6692736148834229},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.5865265130996704},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5294151902198792},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.5176563858985901},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5089972019195557},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.43727853894233704},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.43482688069343567},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.42833754420280457},{"id":"https://openalex.org/C98014903","wikidata":"https://www.wikidata.org/wiki/Q832161","display_name":"Financial ratio","level":2,"score":0.42823970317840576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3708495497703552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3550925552845001},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1725986897945404},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.11749249696731567},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/indin45523.2021.9557545","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin45523.2021.9557545","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2088535455","https://openalex.org/W2117744296","https://openalex.org/W2153262820","https://openalex.org/W2167332641","https://openalex.org/W2295598076","https://openalex.org/W2313384944","https://openalex.org/W2315678552","https://openalex.org/W2793234650","https://openalex.org/W2889572517","https://openalex.org/W2917490808","https://openalex.org/W2947790789","https://openalex.org/W3042367695","https://openalex.org/W3125116538","https://openalex.org/W3125433491","https://openalex.org/W3125614333","https://openalex.org/W6780468190"],"related_works":["https://openalex.org/W3039673966","https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2002351707","https://openalex.org/W2017776670","https://openalex.org/W2952760143","https://openalex.org/W4293699968","https://openalex.org/W2035096001","https://openalex.org/W2108929709"],"abstract_inverted_index":{"Accounting":[0],"fraud,":[1],"usually":[2],"difficult":[3],"to":[4,10,15],"detect,":[5],"can":[6],"cause":[7],"significant":[8],"harm":[9],"stakeholders":[11],"and":[12,29,61,92,94,111,142],"serious":[13],"damage":[14],"the":[16,27,74,79,99,112,146,156,163,174],"market.":[17],"Effective":[18],"methods":[19],"of":[20,31,81],"accounting":[21,32,40,59,64,122,152,170,179],"fraud":[22,41,82],"detection":[23],"are":[24],"needed":[25],"for":[26],"prevention":[28],"governance":[30],"fraud.In":[33],"this":[34],"study,":[35],"we":[36,85],"develop":[37],"a":[38,46,136],"novel":[39],"prediction":[42,83,129,148,165],"model":[43,75,106,118,130,140,149,166],"using":[44],"XGBoost,":[45],"powerful":[47],"ensemble":[48],"learning":[49],"approach.":[50],"We":[51],"respectively":[52],"select":[53,86],"12":[54],"financial":[55,71,109,159],"ratios,":[56,110],"28":[57,177],"raw":[58,63,121,151,169,178],"numbers":[60,65,153,171,180],"99":[62,168],"available":[66],"from":[67],"Chinese":[68],"listed":[69],"firms\u2019":[70],"statements,":[72],"as":[73],"input.":[76,181],"To":[77],"assess":[78],"performance":[80],"models,":[84],"two":[87,95,132],"evaluation":[88,143],"metrics":[89],"-":[90,98],"AUC":[91],"NDCG@k,":[93],"benchmark":[96,133],"models":[97,134],"Dechow":[100],"et":[101,114],"al.":[102,115],"(2011)":[103],"logistic":[104],"regression":[105],"based":[107,119],"on":[108,120],"Bao":[113],"(2020)":[116],"AdaBoost":[117],"numbers.Results":[123],"show":[124],"that:":[125],"1)":[126],"our":[127],"XGBoost-based":[128,147],"outperforms":[131,155,173],"by":[135],"large":[137],"margin":[138],"whatever":[139],"inputs":[141],"metrics;":[144],"2)":[145],"with":[150,158,167,176],"input":[154,172],"one":[157,175],"ratios":[160],"input;":[161],"3)":[162],"XGoost-based":[164]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}