Worldwide losses from credit card fraud are counted in the tens of billions of dollars.
In 2021, payment card fraud exceeded $30 billion. In the coming years, expect that number to climb to $40 billion or $50 billion.
These are the stakes not just for banks and financial institutions, but for merchants and consumers, as well. Credit card fraud causes financial damage up and down the payment value chain. That’s why companies like ours work so hard to detect and prevent fraud.
Chris Dorrington, Featurespace Subject Matter Expert, outlines why machine learning is useful for spotting card fraud and how businesses can leverage the technology’s power to prevent card fraud losses.
What is credit card fraud detection?
Credit card fraud detection is the process of identifying patterns of anomalous card transactions and consumer behaviors that indicate fraudulent activity is taking place. The goal, ideally, is to stop card fraud before it can happen to ensure no money is stolen.
Who is at risk of credit card fraud?
Everyone in the payment value chain is affected when a fraudulent transaction takes place:
- Consumers, who may have had their financial privacy breached in the scam.
- Merchants, who have delivered products or services and expect fair payment for doing so.
- Merchant acquirers and payment processors, who manage payment fraud risk and actively take steps to mitigate it.
- Issuing banks, whose reserves back up the payment card networks.
- Card providers, who connect the acquirer and the issuing bank.
Whenever a fraudulent transaction occurs, multiple parties from that list above must spend time, money and resources to recoup the fraud loss. It’s worth noting that it’s not always possible to recover the fraud loss and this will result in a write off for one of the parties above.
How does credit card fraud occur?
Credit card fraud covers a variety of scams, including:
- Chargeback fraud, in which the scammer requests a chargeback for a purchase or service they claim was not delivered or received but was.
- Account takeovers, in which the criminal gets access to a victim’s banking account and makes fraudulent purchases on the victim’s bank card.
- Card theft, in which a criminal steals a victim’s card and makes fraudulent purchases.
- Card not present (CNP) fraud, in which a criminal gets access to someone’s card payment details and makes purchases without even having the card physically present.
- Identity theft, in which criminals use stolen personal data to apply for credit cards in the victim’s name.
Detecting credit card fraud is therefore more complex because there are so many avenues for the fraud to occur. All the while, these scams take emotional and financial tolls on their victims.
Machine learning’s role in card fraud detection
It’s useful to imagine financial crime as a contagion because, since 2020, its rate of growth in many parts of the world charts a similar trajectory, as does its evolution.
With credit card fraud, imagine the scams described above — chargeback fraud, CNP fraud, identity theft — as variants of the same basic contagion. As new vectors emerge (e.g., new online shopping features), certain variants will seek those vectors out and try to find room to thrive.
We consider machine learning (ML) to be the antidote to financial crime. A tool like ARIC™ Risk Hub is designed and built to detect pathogens in the financial system. It can do this at scale, and in many cases, it can do this quickly enough that financial institutions can spot fraud and intervene before money leaves a customer’s bank account.
Machine learning does this via anomaly detection, specifically in detecting anomalies in the behaviors of cardholders. When the model detects a pattern of transaction behavior that seems suspicious, it can flag that behavior for fraud analysts.
Importantly, the model learns over time. With more data and more transactions to learn from, the machine learning model gets more accurate at predicting the activities could be fraudulent, and which transactions don’t need to be flagged.
In a sector rife with contagions, machine learning helps financial institutions produce antibodies to protect the system against card fraud.
What is the difference between conventional fraud detection and machine learning fraud detection?
Machine learning has ushered in a revolution in fraud detection. However, to those who don’t work with machine learning every day, it can be difficult to understand exactly what it does differently from older technologies.
In conventional fraud detection, the software requires a set of rules that are programmed manually. If the goal is to detect transactions above $10,000, for example, that rule has to be written in. And when scammers start limiting their transactions to $9,999, the software must await new programming before it can be effective at spotting new cases of fraud.
By contrast, machine learning teaches itself. There’s no manual re-programing or re-tuning necessary. Instead, the model can automatically detect patterns of fraud, even as new variants emerge. It can do this at scale and in real time, too, so that analysts can be proactive in preventing card fraud. Further, newer machine learning models — like deep learning neural networks — are proving excellent at discovering hidden correlations in the data.
All of this benefits everyone in the payment value chain:
- Customers can shop more securely and have fewer legitimate transactions flagged.
- Merchants are more protected from fraud loss.
- Acquirers, issuing banks and card providers all have less risk to bear when facilitating card payments.
How to keep your business safe from credit card fraud
For any kind of financial institution, then, the key to mitigating credit card fraud loss is to invest in its prevention.
Featurespace’s ARIC™ Risk Hub for Card Fraud Prevention is specifically designed to detect and prevent both card-present and card-not-present fraud in real time. This ensures:
- Higher levels of customer acceptance for merchants and acquirers.
- Minimal friction for cardholders.
- Vigilance against new attacks as they emerge.
In 2018, Contis, Europe’s leading banking-as-a-service provider, began working with Featurespace to build a solution that would give its financial crime teams a 360-degree view of customer transactional behavior.
The solution took just 90 days to deploy, and it had an immediate impact on Contis’ ability to combat card fraud. Specifically, ARIC Risk Hub allowed Contis to:
- Block bots that are programmed to try thousands of small transactions every minute in an attempt to verify stolen credit card information through brute force.
- Recognize chargeback fraud more easily and intervene.
Today, Contis boasts one of the highest rates of fraud detection in the industry, as well as one of the lowest rates of chargebacks relating to fraud. Overall fraud reduction saved the company more than £900,000 in losses annually.
To learn more, have a look at our video below with Contis’:
Fortify your organization’s card fraud detection capabilities