The 2020 Fight Against Payments Fraud

Last Updated on August 2, 2023 by Nikhil

As technology continues to push the boundaries of what is possible in business, payment fraud prevention will become more complicated. Merchants and financial institutions will constantly be challenged by the increase of payment methods — prompting more advanced fraud techniques. While there have been numerous studies that provide insight into this growing problem, more needs to be done to protect against fraudulent transactions.

Current State of Fraud

According to the 2019 AFP Payments Fraud & Control Survey, a staggering 82% of businesses reported some type of payment fraud in 2018. In fact, large organizations who have more than $1 billion in revenue seem more susceptible to fraud, since 87% of them have reported some type of fraud attempt, an increase of 7% each year. 

Given the growth of ecommerce sales in the U.S. — currently represents $4.19 billion in 2020 and is expected to grow by 9.3% each year — merchants and financial institutions can become liable for more fraudulent activity. Considering merchants consider card-not-present (CNP) fraud is their main type of fraudulent activity and in-person card fraud as their second biggest, according to a Federal Reserve Bank of Minneapolis study, organizations need to step up in order to solve a growing problem.

Concerns in Mitigating Fraud

In the same study conducted by the Federal Reserve Bank of Minneapolis, almost half of all retailers are concerned about how their systems can handle increased fraud due to data breaches. Given large institutions such as Capital One facing a major data breach back in July 2019, this concern will most likely increase — over 75% of retailers expect fraud to happen within the next six to 12 months. 

Aside from data breaches and the growth in ecommerce, targeted attacks is also another main driver of fraud. Knowing this, merchants and financial institutions have continued to integrate older mitigation techniques to fight fraud while incorporating emerging ones. Trends show that fraud attacks are becoming increasingly dynamic, meaning that organizations need to continue to implement secure technology without sacrificing usability. 

How Merchants and Financial Institutions Can Fight Fraud

As it stands right now, fraud mitigation tools such as security code and shipping verification still have their place in helping to fight fraud. Other tools that merchants have found the most effective are enhanced cardholder verification during registration, out of band using one-time password applications and customized proprietary fraud models. 

Trends are also showing that merchants plan on adopting tools such as purchase velocity checks, 3D Secure or similar systems and geolocation in order to identify anomalous transactions. Behavioral biometrics are also highly effective, according to larger retailers. 

Even with the implementation of new tools, merchants need to continually analyze and adjust fraud rules in real-time to keep up with changing conditions. For example, when losses are growing in a fraud loss cycle, merchants typically implement more stringent fraud rules to reduce its financial impact. The issue is that these rules can backfire, leading to declining fraudulent transactions. What’s more, having rigid fraud rules in place could mean scammers can find workarounds or loopholes.  

That’s why it’s key for organizations to work with machine learning in order to eliminate the fraud loss cycle. It’s an agile solution since machine learning can automatically respond to such factors as variations in behaviors, data, and trends. 

Since the effectiveness of machine learning relies on a large pool of transactional data, organizations can help each other by participating in an information-sharing partnership. Doing so can help to identify current fraud attacks as well as exchanging current threat information. The machine learning platform can then use it to create predictive behavior models and adjust in real-time when new fraud trends arise. 

For example, fintech company Feedzai claims a well thought out machine learning tools can detect up to 95% of all fraud. Capgemini, a technology consultancy, reports that machine learning used in fraud detection systems can improve detection accuracy by 90% and minimize fraud investigation time by 70%.

Organizations that adopt machine learning fraud systems can better break the cycle of fraud loss while being able to increase profits. At the same time, organizations can feel good knowing they can better assist their customers to enjoy the convenient and seamless experience of new ecommerce technologies such as digital assistant and mobile purchasing. 

Given the excitement in machine learning, most fraud experts will tell you that any solution will be foolproof on mitigating risk. That’s not to say organizations shouldn’t embrace new technology or find new approaches that work. Investing in cutting edge expertise is no longer an option if you want to ensure your business is profitable for years to come — it’s become a necessity.

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