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Credit Card Fraud Detection and Prevention: The Complete Guide

velikachuprina profile image Roman Chuprina Updated on ・2 min read

As technology progresses credit card fraud is becoming a bigger problem, however, Artificial Intelligence and Machine Learning are here to solve this. In this article, I would like to talk about how modern technologies are able to change Finance and Banking for a better and give an advantage to companies that implement innovation.

From the moment the payment systems came into existence, there have always been people who will find new ways to access someone’s finances illegally. This has become a major problem in the modern era, as all transactions can easily be completed online by only entering your credit card information. Even in the 2010s, many American retail website users were the victims of online transaction fraud right before two-step verification was used for shopping online. Organizations, consumers, banks, and merchants are put at risk when a data breach leads to monetary theft and ultimately the loss of customers’ loyalty along with the company’s reputation.

What is Credit Card Fraud Detection and Prevention?
“Fraud detection is a set of activities that are taken to prevent money or property from being obtained through false pretenses.”

Fraud can be committed in different ways and in many industries. The majority of detection methods combine a variety of fraud detection datasets to form a connected overview of both valid and non-valid payment data to make a decision. This decision must consider IP address, geolocation, device identification, “BIN” data, global latitude/longitude, historic transaction patterns, and the actual transaction information. In practice, this means that merchants and issuers deploy analytically based responses that use internal and external data to apply a set of business rules or analytical algorithms to detect fraud.

Credit Card Fraud Detection with Machine Learning is a process of data investigation by a Data Science team and the development of a model that will provide the best results in revealing and preventing fraudulent transactions. This is achieved through bringing together all meaningful features of card users’ transactions, such as Date, User Zone, Product Category, Amount, Provider, Client’s Behavioral Patterns, etc. The information is then run through a subtly trained model that finds patterns and rules so that it can classify whether a transaction is fraudulent or is legitimate. Now you know what fraud protection is, let’s look at the most common types of threats.

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