Giving Fraudsters a Run for their Money
Giving Fraudsters a Run for their Money
The fraud landscape is changing, and we need to change with it.
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Every dollar lost to a fraudster is one too many.
For years, we've been stuck in an endless game of whack-a-mole, patching up leaks and reluctantly accepting these losses as the price of doing business.
But fraudsters have grown more cunning. They’re now armed with better tools, and are sharing their underhanded tactics in the shadowy corners of the web.
The old playbook simply isn't keeping pace.
In today’s newsletter, I’ll discuss the limits of the old playbook, and explore how new approaches can help us outsmart them at their own game.
Let’s dive in!
The Old Playbook and Its Shortcomings
Traditional fraud prevention methods rely on data about a user and their transactions, such as checking whether an account is real, whether a user is associated with previous fraudulent activity, or high velocities of transactions have been seen on the same card or account.
While these tactics are effective at catching repeat fraudsters, they struggle with detecting scams involving innocent users, which is the fastest growing risk facing FinTechs today.
Imposter and fake advisor scams are two common examples of this. For context, imposter scams totaled $2.6bn in 2022. Consumer “fake advisor” attacks led to more than $3bn in losses.
In the Fake Advisor Scam, fraudsters guide victims through KYC procedures at a brokerage or crypto exchange. After funding the account, the victim is directed to make a transaction.
In the Romance Scam, fraudsters create false personas to gain trust and manipulate victims. High-pressure tactics, frequent communication, and requests for screenshots under 'compliance evidence' are common.
Traditional methods often fail to detect such scams. To catch these types of fraud, you need tools that can detect signs of a scam in progress, allowing preemptive action before user impact.
Detecting scams before they happen
When it comes to fraud prevention, the ideal solution is to identify the signs a user is in the process of being scammed without them knowing it.
Let's take an example: An elderly gentleman, a slow typer who often forgets his password. One day, we notice he's suddenly using a Windows device, typing quickly, and for the first time in two years didn’t have to request a password reset. That's a significant shift in behavior that could indicate that something fishy is going on.
Modern tools that track device and behavior biometrics are effective at catching these patterns:
Remote screen sharing can suggest a scam
Discrepancies between user behavior and their profile data can indicate a takeover
Frequent screenshots during transactions can indicate system exploitation
Active calls during key financial actions can be a warning sign of coercion
While none of these signals in isolation confirms a scam, combining these signals can offer evidence of fraud. These signals enable FinTechs to be more strategic about how they introduce friction like warnings, extra authentication steps, or temporary holds on payments.
Using machine learning and AI to fight fraud
Machine learning and AI have become critical tools for fraud prevention. They can help you detect more fraud, reduce false positives, and flag connections between users and devices that can help you understand what might be happening on your platform.
If our slow-typing, password-forgetting user suddenly starts making suspiciously large transactions, your system can flag this as an uncharacteristic movement of funds.
Machine learning can also help you distinguish between suspicious and legitimate customer activities. When integrated with device and behavior signals, you can score the risk level of transactions with surprising accuracy and add logic to make decisions on your behalf.
For FinTechs, this is especially useful when it comes to authorization decisioning, because you can make smarter decisions about what transactions to approve or decline.
Embracing Fraud Data Sharing
Fraud data sharing is probably going to be the biggest game changer in fraud prevention.
The visibility gap between traditional banks, FinTechs, and new payment platforms has long been a hurdle in combating fraud. Each institution operates in its own silo and is oblivious to the fraudulent activities occurring elsewhere, which creates these cracks that fraudsters can exploit.
Just imagine a scenario where an individual's bank account shows suspicious, large transactions to a crypto exchange while their FinTech app reports multiple failed logins. Typically, these incidents would be treated separately, leaving each institution blind to the bigger picture.
This happens all the time.
Just as fraudsters share their tactics in the darker corners of the web, we can easily start plugging these visibility gaps by sharing fraud data with other players in the financial ecosystem.
We’ve recently launched a consortium that helps banks, FinTechs, and emerging platforms pool their data to get a better view of transaction risk. And there are others launching similar initiatives, which I think it’s indicative of a move toward more collaboration in fraud fighting.
—
The fraud landscape is changing and we need to change with it. The bad actors have armed themselves with better tools and are working together to exploit our systems. So we need to come up with new tools to solve new problems. And we need to start working together.
If we can embrace this new playbook, I think we have a good shot at giving these fraudsters a run for their money.
Cheers,
Soups Ranjan
Co-founder & CEO of Sardine, Empire Startups Contributor
The Empire Startups Contributor Model is a chance for writers and thought-leaders in the FinTech space to share their ideas and expertise with our ever-growing audience. Our model is is merit-based and does not offer monetary compensation.
You can check out more of our contributors here.
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