Credit Card Fraud Detection Based on the Transaction by using Ontology Fraud Detection Algorithm

Credit card fraud is rising significantly with the growth of recent technology and the global superhighways of communication. Credit card costs consumers and the financial company billions of dollars annually. The swindler continuously attempts to find new plan and procedure to commit illegal actions. Hence, fraud detection systems have become necessary for banks and financial institution, to reduce their losses. The most common techniques used to make the fraud detection model. Incidentally, detection and prevention of credit card frauds are one of the vital problems in the digital world that need exact transactions analysis. One method for detecting fraud is to check for suspicious changes in user behavior. This paper provides an inventive fraud detection method, build upon ontology and ontology instance similarity. Ontology is broadly used to facilitate knowledge distribution and reuse. Thus, several personality ontologies can be simply used to present user actions. By calculating the similarity of ontology instances, we can decide whether an account is defrauded. This technique short the data model cost and makes the system very adaptive to many applications.