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一种基于随机森林的可疑交易检测方法
Title:SuspiciousTransactionDetectionMethodBasedonRandomForest
Abstract:
Withtherapiddevelopmentofthefinancialindustryandtheincreasingnumberofelectronictransactions,detectingsuspicioustransactionshasbecomeacriticaltaskforfinancialinstitutions.ThispaperproposesanovelmethodforidentifyingsuspicioustransactionsusingaRandomForestalgorithm.RandomForestisanensemblelearningtechniquethatcombinesmultipledecisiontreestoimproveclassificationaccuracy.ByleveragingthepowerofRandomForest,thismethodcaneffectivelydetectfraud,moneylaundering,andotherillegalactivitiesinelectronictransactions.
1.Introduction
Inrecentyears,thefinanciallandscapehasbeentransformedbyadvancementsintechnology,particularlyintheareaofelectronictransactions.However,thisriseinelectronictransactionshasalsoledtoanincreaseinfraudulentactivities,makingitessentialforfinancialinstitutionstodeveloprobustandefficientmethodsfordetectingsuspicioustransactions.Traditionalrule-basedapproachesoftenstruggletokeeppacewiththeevolvingtechniquesusedbyfraudsters.Machinelearningalgorithms,suchasRandomForest,offerapromisingsolutiontothischallenge.
2.Background
2.1SuspiciousTransactionDetection
Suspicioustransactiondetectioninvolvesidentifyingactivitiesthatdeviatefromtheexpectedbehaviorwithinafinancialsystem.Theseactivitiesmayincludefraud,moneylaundering,insidertrading,orotherillicitactivities.Traditionalmethodstypicallyrelyonpredefinedrulesorheuristics,whichcanbeinflexibleandlimitedintheirabilitytoadapttonewfraudpatterns.Machinelearningalgorithms,ontheotherhand,canlearnfromhistoricaldataandidentifycomplexpatternsinreal-timetransactions.
2.2RandomForestAlgorithm
RandomForestisanensemblelearningtechniquethatcombinesmultipledecisiontreestomakepredictions.Eachdecisiontreeistrainedonarandomsubsetofthetrainingdataandusesarandomsubsetoffeatures.Byconsideringthepredictionsfrommultipletrees,RandomForestcanmitigateoverfittingandmakemoreaccuratepredictions.Itisknownforitsrobustness,scalability,andabilitytohandlehigh-dimensional