SecureBankingSystemwithAI FraudDetection, ImplementationofFraudPreventionUsingMachine LearningandBehavioralAnalytics

Abstract

Thisthesispresentsthedesign,implementation,andevaluationofasecurebanking systemwithintegratedartificialintelligenceforfrauddetection.Theresearchaddresses thecriticalchallengeoffinancialfraudindigitalbankingplatformsthroughacompre- hensiveapproachcombiningadvancedmachinelearningtechniqueswithrobustsecurity architecture. Theproposedsystememploysamicroservicesarchitecturetoensurescalability, faulttolerance,andsecurityisolation.Atitscore,anAI-poweredfrauddetection serviceanalyzesuserbehaviorpatternsandtransactioncharacteristicsinreal-timeto identifypotentiallyfraudulentactivities.Thesystemimplementsenhancedthreshold classificationtechniquesthatimproveupontraditionalbinaryclassificationmethods, resultinginhigherprecisionandrecallmetricsevenwithimbalanceddatasets. Additionally,theresearchexplorestheintegrationofariskassessmentenginethat complementsthemachinelearningmodelwithrule-basedanalysis.Thishybridapproach providesboththeadaptabilityofAIandtheexplainabilityofrule-basedsystems.The implementationleveragesDockercontainerizationtoensureconsistentdeployment acrossenvironmentswhilemaintainingsecurityisolationbetweencomponents. Experimentalresultsdemonstratesignificantimprovementsovertraditionalfraud detectionapproaches,withtheproposedsystemachieving93.7%accuracyand91.2% precisioninidentifyingfraudulenttransactionswhilemaintainingalowfalsepositive rateof3.8%.Thethesiscontributestothefieldoffinancialcybersecuritybypresentinga comprehensivearchitecturethatcanbeadaptedbybankinginstitutionstoenhancetheir fraudpreventioncapabilitieswhilemaintaininghighperformanceanduserexperience standards. vii