It took a bit before the mainstream became accustomed to online payments, but it did. In 2023, over 70% of American adults used PayPal, and that number was significantly larger for younger generations and in geographies like Germany, UK, and France.
It’s a great evolution of tech – simple, convenient, and mutually beneficial for consumers and merchants when done right. But, with every great tech evolution, fraudsters are never far behind. Juniper Research reported that cumulative merchant losses due to online payment fraud from 2023 to 2027 would surpass $343 billion. The good news is that the industry is getting smarter about counterbalancing fraudsters and even stopping them before they attack, and AI is a big part of the solution.
Figuring out the best ways to apply AI and the right partners to do it with can feel complex, so I shared some thoughts here.
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The Rules of Picking the Right Tech Partner Still Apply
Most e-commerce businesses operate at the center – and as the integrator – of an ecosystem of tech tools. It helps to think of AI as just another tool in the ecosystem.
We always start with identifying the problem we want to solve. In this case, it’s ensuring secure transactions. There are the obvious questions first:
- Does the potential vendor have a strong track record? Don’t just look through analyst reports. Find someone you know who has firsthand experience, whether they’ve collaborated with the provider or worked on a related project.
- Do they offer a free trial? If they don’t, give them real-world challenges during demos. It’s easy to hide behind templated demos.
- What are their security and compliance standards? If they’re not up to your standards, walk the other way.
- Can they integrate seamlessly and grow as you grow and as technology evolves? That means, no lengthy integration timelines. I think we can all admit that we’re not sure what this will all look like one year from now. So, your partners should prioritize innovation and flexibility.
And then there are the fraud-specific considerations. Most importantly, do they offer hands-on monitoring with a responsive team that can act quickly based on merchant input? You want your antifraud provider to react swiftly whenever fraud rates rise. Playing catch-up isn’t a smart strategy—delays only amplify the financial losses businesses face from fraud.
Look for Holistic AI Backed Secure Payment Solutions
Once you’ve assessed the basics of a good tech vendor, figure out how they specifically use AI to make your site more secure. Make sure every decision is transparent; understand the logic behind their AI-backed fraud detection norms and that they align with your corporate philosophies and regulatory requirements.
I already hit on the importance of real-time monitoring. This is the key factor in assessing risk and detecting and preventing fraud before it happens.
At G2A.COM, we take a close look at AI solutions that support the effectiveness of anti-fraud systems as we like to think of AI as the first line of defense – and we expect our partners to think the same way. Algorithms can analyze vast amounts of data in real-time to:
- Detect suspicious activity. Anomalies within behavior patterns, such as sudden increases in the frequency of transactions from an account or payments from unexpected geographies, might indicate the presence of fraudsters. AI algorithms also help monitor things like typing speed and mouse movement to create unique IDs for each user and make it easier to catch when those IDs are at risk.
- Stop the fraud from happening. AI systems can halt or delay transactions to give time for further investigation if needed. If the system detects possible fraud, it can also trigger additional authentication measures, such as security questions.
- Continuously learn and adapt. What AI systems learn today should become historical data that fuels more intelligent decision-making in the future.
Businesses should also consider where they currently operate and how that might scale over time and pick AI partners prepared to meet those expectations.
For example, we operate in 180 countries and chose Forter as a partner because they met the basic requirements outlined above and also because they house, monitor, and analyze a large amount of global transaction data. Because of this, they are smart about how different cultures and geographies behave and can reduce false positives.
Let’s face it. Sometimes, AI systems can be wrong. They have more of a chance of being wrong if their inputs don’t represent the relevant data. That’s why the human eye is indispensable, AI tools achieve their best results when paired with human insight—it’s a partnership, not a replacement.
Keep the Customer at the Center
In the end, we’re all here to serve the customer. Our ultimate goal—for businesses and their partners alike—should be a seamless and secure experience for the end user. AI systems work well when they are largely invisible to the customer, which is why false positives are such a problem.
When a legitimate transaction is canceled, it’s not only very visible; it’s annoying to customers and even potentially harms the brand’s reputation. Some reports even say false positives are a bigger problem than credit card fraud itself because of the additional staff it takes to implement manual reviews and the lost revenue when customers abandon carts and choose competitors.
AI-backed systems have significantly improved fraud detection accuracy. Mastercard is frequently touted as an example of a brand having great success in this – up to 200% reduction in false positives. Your fraud partner should understand this and work as hard to allow legitimate transactions as they do to prevent bad actors.
Finally, before implementing AI systems with an external partner, ensure your internal team is also part of the process. At G2A.COM, our internal cyber defense team improves the existing AI solutions, and our external partners provide the tools to protect the user throughout the customer journey. This leads to every department adopting at least one AI tool quarterly and evaluating its effectiveness.
The competitive landscape has never been more intense, and it’s not just the tech systems that should be continuously learning; the humans behind those systems should be at it, too.
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