Traditional AI fraud protection can miss small but meaningful changes, much as antivirus tools miss polymorphic malware that mutates with tiny code changes. Overly strict AI also raises false declines, which cost merchants far more than actual fraud. A hybrid model that combines deep learning with human analysts detects threats from partial data and resists subtle attacks, with some companies cutting their false-alarm rate in half.
Unfortunately, the answer is likely “No.” Here’s why.
Small changes equal big opportunities for fraud
A common misconception is that once virus protection companies find and target a specific malware, it’s rendered useless. Unfortunately, that’s not the case.
Malware is continuously mutating and evolving, frequently with tiny changes in the coding that render the virus virtually undetectable by IT security and antivirus software.
“More than 99% of new threats and malware are actually very small mutations of previously existing ones. And even that 1% of supposedly brand-new malware are rather substantial mutations of existing malicious threats and concepts,” according to Guy Caspi, a leading mathematician and a data scientist global expert.
On the surface, each mutation looks like the original. But lurking just beneath, simple changes (like compression, encryption, or file names) make the new code difficult to detect. Called polymorphic malware, this mutated virus becomes the perfect vehicle for a determined cyberattacker.
Gaps when using AI in the fight against fraud
Companies have long relied on AI (also called “machine learning”) to quickly analyze transactional behaviors to help security personnel proactively prevent card fraud and the associated losses.
AI leverages mathematical algorithms and data to identify major trends and fraudulent patterns. But traditional AI anti-malware programs typically struggle to detect emerging fraud strategies that may start out with scattered, small-scale attacks before ramping up to something more damaging.
This gap can leave you exposed and unable to identify all truly fraudulent activity.
Unfortunately, the flip side isn’t much better. AI approaches that are too stringent risk increasing your false decline rate by rejecting transactions that are actually bona fide. This can translate into big money left on the table, according to MasterCard and Javelin research:
- The losses associated with false declines—$118 billion for merchants—are far higher than those associated with actual fraud ($9 billion).
- 32% of customers who experienced a false transaction decline never shopped with that merchant again.
Either way, the result is a vulnerable system. Merchants are left wanting—and needing—more.
Artificial + Human Intelligence = Increased security, reduced risk
How can you keep your data safe, if traditional AI isn’t enough? Consider a hybrid solution: data collected by human analysts and combined with an AI approach called “deep learning.”
These specialized algorithms operate much the same way our brains identify objects by analyzing and processing high-level features from raw data.
One of the miracles of our brains is that we don’t always need to see an object in its entirety to identify it. For example, we can see a tusk of ivory and immediately identify the object as an elephant.
Deep learning operates in much the same way—which means it can identify a threat even with partial data. And this in turn means deep learning is resistant to the small changes in cyberattacks that most AI methods struggle to pick up on.
When layered with expert staff dedicated to fraud validation and transaction investigation, the system becomes nearly fool-proof. The analysts add value by adding data points, transaction results and incident details into the AI system, making it smarter and more effective.
This unique combination of man and machine provides a true advantage over traditional fraud-detection systems. In fact, some companies employing this strategy have cut their false-alarm rate in half and freed their staff to identify new patterns of fraud, which are then fed back into the system.
Incorporate a blended approach for comprehensive protection
The data is in—and it’s clear that algorithms alone aren’t enough to defend against the threat of fraud. Instead, they must be part of a blended approach that combines the best of both the human and the AI worlds.
At Clearsale, we combine big data analytics, statistical intelligence and human expertise to deliver the optimal balance between fraud protection and maximized sales. Contact our fraud prevention analysts today to learn more about our unique approach.
Frequently Asked Questions
Why can AI fraud protection miss small details?
Most new threats are tiny mutations of existing ones, with simple changes like compression, encryption, or file names making them hard to detect, similar to polymorphic malware. Traditional AI also struggles with emerging fraud that begins as scattered, small-scale attacks before scaling up.
How much do false declines cost compared to actual fraud?
According to MasterCard and Javelin research cited in the article, false declines cost merchants $118 billion, far higher than the $9 billion associated with actual fraud. In addition, 32% of customers who experienced a false decline never shopped with that merchant again.
What is polymorphic malware and why is it relevant to fraud?
Polymorphic malware is code that mutates with small changes so it evades antivirus detection while looking like the original on the surface. The article uses it as an analogy for how minor variations in fraud attacks slip past traditional AI that hunts for known patterns.
How does deep learning improve fraud detection?
Deep learning analyzes high-level features from raw data, much like the brain identifying an elephant from a single tusk. Because it can recognize a threat from partial data, it is more resistant to the small changes in cyberattacks that most AI methods miss.
Why combine human analysts with AI?
Human analysts add data points, transaction results, and incident details into the AI system, making it smarter and more effective. Layering expert fraud validation with deep learning makes the system nearly fool-proof against threats that algorithms alone would miss.
What results can a blended human-plus-AI approach deliver?
A blended approach balances fraud protection with maximized sales. Some companies using this strategy have cut their false-alarm rate in half and freed staff to identify new fraud patterns, which are then fed back into the system to improve it further.
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