Know how machine learning may be the key to balancing customer experience and risk management when it comes to payments fraud protection.
Fraud is one of the biggest threats to companies today. The Association of Certified Fraud Examiners estimates that organizations lose 5% of their annual revenue to fraud each year. This is dangerous for everyone, but smaller businesses are particularly vulnerable to these losses. Then there’s the growth of online activity, which exacerbates the risk. Companies developing their digital channels may not know the right way to protect these new business areas. Meanwhile, fraudsters are hard at work, optimizing their attacks for a cyber environment.
The threat is clear, but payments companies don’t need to address it on their own. Leveraging technology is critical in today’s digital landscape, due to the sheer volume of online activity. It can intimidate fraud prevention teams — there are just too many transactions to assess and approve. Until now, these teams have often turned to rules-based software systems to help identify fraudulent activity. While useful to a degree, these require manual adjustments to catch new styles of attack. This is becoming unsustainable, as cyberattacks are constantly evolving and the software can’t keep up.
What companies need is a tool that can adapt alongside the fraud it’s fighting. Fortunately, it is now possible to use machine learning to create flexible fraud detection policies.
Machine learning (ML) is a subset of artificial intelligence that uses data to continually improve and refine its programming. Today’s companies constantly generate data, but not all platforms are extracting value from this unending stream of information. Machine learning analyzes data sets and identifies behavioral trends, to establish what is normal activity — and what is not. The more information it receives, the more detailed and accurate this understanding becomes.
This is particularly useful as it lets human employees focus their attention on more active work. Data can be automatically fed through the ML program, ensuring that it is up-to-date and recording the latest changes in customer behavior. The employee then can look at the analysis and take immediate action, rather than manually sifting through the data. This ensures a more cost-effective operations team that is supported by better insights.
There are many use cases for machine learning, from refining marketing promotions to optimizing inventory management. Its use in fraud prevention is where it really shines.
Fraudsters do their best to slip under the radar, but there are usually some clues left behind. With machine learning, the smallest deviation will be registered as the program will be able to reference it against millions of other data points. A human employee may not be able to detect such a minor error at first, which leaves the company vulnerable. Acting quickly is one of the most important features of successful fraud prevention.
This ability to spot outliers makes ML a great device for flagging fraudulent activity as soon as it happens. Yet the real value of this technology is its flexibility. Machine learning defines “normal” by looking at a lot of information; by sharing the company’s own data, rather than a generic sample, it is possible to train the ML to respond specifically to the unique characteristics of their customers. This is the kind of personalized solution that cannot be obtained elsewhere.
This degree of nuance is particularly important when handling payments. Customers do not want their accounts to be frozen for a false alarm, because they depend on access to their finances. On the other hand, they do want a quick response to real threats so that any damage can be minimized. This requires the payments company to walk a tightrope, balancing customer experience with risk management.
For instance, if a payments company is known for supporting a lot of cross-border transactions, machine learning can learn not to flag every international transfer. Instead, it may recognize that payments above or below a certain threshold are uncommon for that particular company — and raise the alarm accordingly. If a lot of data is fed into the program at the beginning, it will be able to begin detecting anomalies almost immediately and much more quickly than if done manually.
With so much at stake, companies must be willing to invest in fraud prevention. This can be tricky, due to the shifting nature of digital attacks: What works today may not work tomorrow. Machine learning makes that concern redundant. Investing in an ML program is a strategic long-term choice as you only need to set up the program once and it will optimize itself over time. Cyberattackers are always looking for new ways to threaten businesses and their customers. ML makes it possible to catch these in real-time and adapt for the future.
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