Fraud Detection System Using Machine Learning

Fraud Detection System Using Machine Learning

Author Name : S. Om Prakash, M. Tarun Kumar, K. Ravi Teja, K. Pavan Kumar, M.S.K. Varma

ABSTRACT

The aim of this research article is on detecting fraud in real-world circumstances. In comparison to previous years, the frequency of credit card scams has increased dramatically. Criminals are enticing people with phony identities and other technologies in order to defraud them of their money. As a result, finding a solution to these sorts of scams is critical. In order to reduce their losses, all credit card issuing institutions must implement effective fraud detection systems. It is becoming more difficult to trace the activity and pattern of illicit transactions as technology evolves. To come up with the solution one can make use of technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology; it becomes feasible to automate this process and to save some of the intensive amounts of labour that is put into detecting credit card fraud.

In this proposed paper we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect Illegal and Valid transactions. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a Random Forest algorithm, Bernoulli Naïve Bayes Model and Logistic Regression algorithm, Support Vector Machines, KNN, Decision Tree. Using these algorithms, we can analyze the larger data-set and user provided current data-set. Then augment the recall and f1 score of the result data. Processed some of the attributes provided and then moved on to the next step which can find affected fraud detection in viewing the graphical model of data visualization. The performance of the techniques is gauged based on accuracy, sensitivity, and precision.

Keywords: Machine learning, Random Forest, Decision Tree, Support Vector Machine, KNN, Naïve Bayes, Logistic Regression.