How Is Big Data Used To Fight Against Credit Card Fraud?

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Credit card vs. technology seems to be a never-ending fight, in which the stakes are incredibly high. Cyber-criminals and hackers are creating new sophisticated techniques to get the credit card information and later use it for their benefits. Credit card fraud has become a pressing topic over time. With fake transaction values going into billions, most of the credit card companies are struggling to find an effective solution to the problem of credit card fraud once and for all.

Most of the credit card companies possess an immense interest in identifying the financial transactions that are criminal and illegal. According to the Federal Reserve Payments Study, the US citizens used credit cards to pay 26.2 billion purchases in 2012. The estimated loss due to various unauthorized transactions that year was $6.1 billion. Later, the federal Fair Credit Billing Act restricts the maximum liability of a credit card owner to $50 for illegal transactions, leaving the credit card companies on the verge of the balance.

In the previous years, credit card companies successfully detected credit card fraud by flagging suspicious transactions and later calling human investigators to inspect and review the transactions carefully. The process has also included phone calls of the users and asks for verifications. With the number of credit card trades growing yearly at a rate of 8% by volume during 2012 and 2015, the process shows that it wasn’t much workable. This raised the need for some advanced technology, which comes in the shape of big data.

Over time, the credit card companies begin to employ big data technology to recognize the fraudulent transactions at the time of happening, not waiting for the verification with the user. At first, companies use typical consumer transactions to train their machine learning algorithms. After establishing the user’s normal transaction behavior, the algorithm then predicts the possibility that a particular transaction is fake. Companies set the specific thresholds for this, and in case the transaction is over the assigned value so, it will lead to its rejection.

Several factors go into these algorithms, including the customer’s shopping behavior, the device being used for the transaction amount, location, and time. Another factor is the location of the IP address. If the same consumer accounts show multiple IP addresses from all around the world, it is a sign that the account has been hacked.

Soon the credit card companies turned to the use of data processing algorithms to cope with the skills of their loss prevention departments.

Several business organizations are using analytics to combat identity theft. Different credit card processors like Visa are also using big data and machine learning to analyze the massive flow of transactional data in real-time.

The big data technology can also join hands with the advanced blockchain technology for securing the data of their users. Let’s suppose if you’re working in any forex company so, by integrating big data with blockchain, you can also become a successful crypto trader.

Algorithms determine the possibility of fraud by examining the user’s purchasing habits and comparing each transaction with what headed it. Every new algorithm builds on the last. If any transaction looks suspicious, like several cash advances within a day when you didn’t do that thing before, such advances might get denied.

Now, the digital wallet technology like the Apple Pay allows you to store your credit card information on your phone and pay through the Radio Frequency Near Field Communication (NFC). Relate this with the fact that your smartphone can access the location data via GPS.

The possibility of payment and fraud detection revolves around one device. If someone gets a hold on your credit card information and makes an effort to make a payment, the credit card company will automatically become informed because your phone is transmitting signals from somewhere else.

But because of big data in the cloud, the machine learning algorithms, along with the new payment technologies such as the digital wallet, the future of credit cards and fraud detection, might get located. The question here is that if this happens, then what will happen with an online transaction?

Undoubtedly, Ecommerce is a vast platform that presents its own set of data security challenges and issues. Only because of this, the analysts predict that cybersecurity expenditures will rise to$170 billion by 2020, and the cost of cybercrime will reach $2 trillion.

It shows the power of hackers in an era where it is tough to protect the data. Although organizations do spend a handsome amount of money on security, the cost of cybercrimes doesn’t show a fall.

The big data algorithms efficiently recognize if an online credit card payment is coming from a different IP address than the normal one. However, a sophisticated hacker does find many ways around it. Companies having massive troves of credit card data invest significantly in security protocols for the cloud, such as encryption and multi-factor authentication.

Here big data act as a double-edged sword. On one side, the algorithms analyze to examine the chances of fraud- the more data available, the more accurate the analysis will be. While on the other hand, the remains of credit card data are a treasure for cybercriminals.

The solution to credit card fraud is to make sure that every access point is secure and protected. Although it is possible, with so many organizations in possession of credit card data, as well as different varying levels of security, it is difficult to predict the end anytime soon.

There are plenty of models, resources, and tools made available for credit card companies to use, here a few issues that remain a hurdle in the field of credit card fraud detection.

The credit card transactions are incredibly private. The absence of a standard dataset makes it hard to compare different methods and techniques. As a result, there is no standard algorithm or any way that outperforms all others.

There are limited metrics to determine a fraud detection system’s accuracy and efficiency.

There are restricted adaptive fraud detection systems that can learn as transactions come in. Rather than most of the systems should be trained and can’t immediately integrate new fraud.

Another challenge that the credit card companies face comes in the shape of consumption patterns. When the user ends up changing their consumption patterns, many algorithms run the risk of deducing it as a sign of fraud and will send a warning that might end up negatively affecting the customers if the decision is made to block the card temporarily.

It also means that algorithms, along with the loss of prevention departments overseeing the need to reorient their operations. The human workers who look after these algorithms need to update the algorithm regularly to prepare them for this case and to make the programs more flexible so that the human computers and employees can appreciate each other’s specialties.

Therefore, if you’re part of any organization and want to improve fraud detection and avoid causing much discomfort for the users, make sure that your system is continuously in the process of learning regarding new data and discovering new patterns to deliver rich insights.

As various cyberattacks and data breaches continue to grow, credit card companies more often use the open-source technology of big data that can help them lead the game from the front.