Credit Card Fraud Detection Using Hybrid Approach of Machine Learning

Abstract

Abstract: The usage of internet banking and credit cards is growing at an exponential rate. As more people use credit cards, online banking, and debit cards, the probability of becoming a victim of fraud of various kinds also increases. In recent times, there have been a number of instances in which users of credit card companies have, as a result of a lack of understanding, given their card information, personal information, and one-time password to an unidentified fraudulent caller. As a direct consequence of this, fraudulent activity will occur on the account. Fraud is a problem for the same reason that it is tough to track down a con artist who used a phone identity sim or made the call that utilized an internet provider: it is difficult to find them. Therefore, in order to detect fraudulent activity, this research makes use of supervised methodologies and algorithms, and the results are quite accurate. Customers lose trust in an organization when it engages in activities that are fraudulent or illegal, which in turn has a huge negative impact on the organization. Additionally, it has an effect on the total income and turnover of the company. The isolation forest technique is used in this study to classify data sets acquired from professional survey firms in order to detect fraud activities.