As a data scientist who's spent six years working specifically on fraud prevention and detection techniques, I've thought a lot about how financial organisations build effective fraud prevention systems. Whether you're a neobank startup or a cryptocurrency platform, the challenge is real. Fraudsters are becoming increasingly sophisticated, and they’ll continue to evolve even as you try to build your compliance capabilities from scratch.
Here's what I've learned about the most critical steps every small financial organisation needs to take to protect itself — and why building an entire fraud prevention infrastructure in-house might not be the answer.
Step 1: Hire people with fraud prevention experience
This might seem obvious, but to even reach the point where you're discussing fraud prevention seriously, you need to have people on board that have done this type of work before.
More specifically, you should have somebody on board with deep experience and knowledge on the topic of fraud prevention — and that’s just to help you identify the weak points of your organisation. Without this expertise, you may struggle to pinpoint how exactly to defend yourself from all the ways in which fraudsters can attack your systems.
Hire some smart data scientists, and pair them with domain experts who can help them extract the most out of the data that's available. Of course, your data may not be in a good state to begin with, so your new hires' first action item may be to design and implement a suitable data model that allows you to collect, transform, save, and make data available across your organisation.
Don't make the mistake of thinking you can figure this out on your own as you go. Fraud prevention isn't something you learn through trial and error, because the cost of a single error can be high.
Step 2: Train employees across your organisation
Regular, consistent employee training also plays a huge role in a comprehensive fraud prevention strategy. Yes, your employees should be on the same page regarding your company’s mission, values, and methods. But on the more practical side, training helps them put these values into practice and is one of the best ways to spread knowledge across a company.
Training may not look the same for everyone. Analysts may be trained in how to use the results of new AI models to tackle certain types of fraud. AI engineers may learn about new fraud trends and how to develop appropriate responses. And everyone can benefit from opportunities for personal learning and development, such as attending conferences that introduce them to seemingly unrelated fields and help to shed new light on old problems.
Step 3: Understand that everything online is a target
If you're building a digital-first financial service, it's important to understand the extent of what you're up against. Business models or services that operate strictly online present a major opportunity for fraudsters.
Before digital banking, many types of fraud required more on-the-ground effort. Don't get me wrong — people still tried to commit fraud. It just looked a bit different.
For example, a fraudster may have tried to collect the pension for a woman they claimed to be their dead “grandmother” decades after she died. This was no easy task — the effort required going to a physical office with a forged bill, faking a signature, and building a whole backstory strong enough to convince a real-life person. Not impossible, maybe, but certainly something that requires a bit more hustle.
These days, many financial services have moved online and adopted processes that, to some extent, make fraud easier to attempt with lower effort. This fraud may have a relatively low success rate, but all it takes is one person or one organisation. That's why we need to up our game on the security side of things and take evolving digital threats seriously.
This is especially true for newer organisations. Fraudsters often target newer companies, betting on the odds that they probably haven’t yet hired enough people in their compliance department or taken the time to think through fraud prevention practices. Don't prove them right.
Step 4: Make the ‘build vs. buy' decision
Once you've identified your weak points, it's time to make a critical decision: Do you build fraud prevention capabilities internally, or do you outsource that service to specialists?
This is the classic “build vs. buy" dilemma, and my recommendation would generally be to buy. Almost every company — from the smallest startup to more established organisations — can benefit from strategic outsourcing.
Why? Well, it’s the same reason that any company outsources anything. Think about what you typically outsource. These tend to be services and capabilities that don’t relate to the core of your business. Your expertise may be financial services, payments, or cryptocurrency trading. But fraud prevention requires additional expertise that may fall outside your scope — for example, sophisticated AI models, dynamic workflows that can accommodate KYC regulations across different jurisdictions, and constantly updated knowledge of evolving fraud techniques.
Software is a major component of effective fraud prevention. And if that's not your expertise — which, as a neobank, cryptocurrency platform, or embedded finance business, it’s probably not — you stand to benefit from outsourcing.
Even large traditional banks could benefit from this approach. Yes, many of these banks have huge armies of operations departments that manually process a lot of work that could otherwise be done automatically. Yes, they can afford to hire and maintain these large, in-house teams. And yes, they claim that doing everything in-house gives them the certainty that they're doing a good job — and, sure, they probably are.
But nowadays, with the level of sophistication we’re seeing in digital fraud, it can make more sense to automate processes and not rely so much on manual checks that are vulnerable to human error. And the best automated security and compliance solutions are built by the kind of experts you’d be hard-pressed to find and hire on your own.
Step 5: Stop looking for ‘smoking guns’
If you're looking for smoking guns in fraud, you will generally be disappointed.
Modern fraud detection isn't about finding simple, obvious signs of fraud. Very rarely can you say, “Oh, look at this single data point! This means it must be fraud!”.
Instead, fraud is about patterns and context. All data points are what we call “weak predictors.” But when combined, they can tell you a more convincing story. This is why machine learning has become so powerful in fraud prevention — it can process and find patterns in dozens or hundreds of variables simultaneously.
This is crucial, because even seemingly reliable indicators can be misleading.
For example, take face similarity checks, which compare a selfie of a client with the portrait on their ID document to confirm that they’re the document holder. The greater the similarity, the more confident you should be that it's the same person, right? Wrong. Sometimes face similarity can be suspiciously high. This, combined with other patterns, can be the hallmark of some types of fraud.
Step 6: Accept that quality comes at a cost — but find the right balance
Here's the hard truth: There is no KYC solution, whether internal or third-party, that can guarantee excellent quality at the minimum cost. As the quality of the solution increases, the costs may also become more significant.
The key is finding the right balance for your organisation's risk appetite. You need to understand where you fall on the spectrum from “I don't care if we onboard a million fraudsters as long as it's done for 5 cents per case” to “I don't care how much it costs—not one fraud gets onboarded.” Most organisations fall somewhere between those two extremes.
Remember, cost isn't just about the provider — it's also about friction. The more friction you create in your funnel, the more people will drop off. You need to balance fraud prevention with customer experience, and the balance you ultimately strike will be unique to your business model and risk tolerance.
The bottom line
Fraud prevention for financial organisations isn't about having the biggest team or the most sophisticated in-house technology. It's about making smart decisions early, leveraging expertise where it exists, and understanding that this is an ongoing arms race rather than a one-time fix.
The good news? The tools exist to protect your organisation effectively — but only if you approach the problem with the right strategy from the start.
As I tell people, you can either learn these lessons the easy way (by planning ahead) or the hard way (by dealing with the consequences of fraud). I'd recommend the easy way.
To learn more about how Fourthline's solutions can help, get in touch with our team today.
Konstantinos Leventis is a Data Scientist at Fourthline. As a software engineer, Konstantinos focuses on machine learning and scientific computing. He holds a PhD in Astrophysics from the University of Amsterdam.
This article is for informational purposes only and does not constitute legal advice.
