Credit Card Fraud Detection Solutions – How to Implement Them in Your Business

Credit Card Fraud Detection is a major problem for industries like Retail, E-Commerce, and Finance. But essentially, every business working with credit cards can become a victim of Credit Card Fraud. This article will focus on the measures to prevent it by leveraging modern and advanced techniques like Artificial Intelligence and Machine Learning.

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.

Unauthorized card operations hit an astonishing amount of 16.7 million victims in 2017. Additionally, as reported by the Federal Trade Commission (FTC), the number of credit card fraud claims in 2017 was 40% higher than the previous year’s number. There were around 13,000 reported credit card fraud cases in California and 8,000 in Florida, which are the largest states per capita for such type of crime. The amount of money at stake will exceed approximately $30 billion by 2020.

Business Intelligence could be imagined as a very precise and detailed report of performance data. It provides business feedback in the form of structured reports and allows business managers to build an effective strategy based upon them. It may also include an analysis of every action taken by the fraud detection system. The most important thing you need here is a solid data warehousing architecture that will allow fast access to data for further management of information.

Data Science is a more complex route that involves the advanced technologies used for prescriptive and predictive analysis. In the context of fraud detection, predictive analytics are aimed at making forecasts of events that will happen in the future. Prescriptive analytics will help you build the best strategies based on the received predictions.

The required volume and variety of data led to the popularization and wider adoption of Big Data technology. The data storage systems used previously simply can’t handle such an enormous amount of information. Big Data allows for a deeper understanding and improvement of the efficiency of detecting fraud in this case.

Artificial Intelligence, Machine Learning, and Deep Learning probably could win a contest in terms of hype and popularity. Does this buzz have any value? We will find out about it in this article; let’s start with the terminology!

Artificial Intelligence is a theory and it is the development of computer systems that are aimed to perform tasks usually done by humans. Machine Learning is a subdivision of Artificial Intelligence focused on teaching computers to learn by themselves without the need to be extensively programmed manually by humans. Machine Learning is divided into three main types: Supervised Learning, Unsupervised Learning, and Semi-supervised Learning (we will cover them in detail later in the article). Deep Learning is a class of Machine Learning that is very popular in fraud detection solutions and is focused on building neural networks.

Going back to our topic, let’s figure out why Fraud Analytics — in the form of Machine Learning — is superior to the traditional methods of battling credit card fraud.