As fraud accelerates faster than prevention budgets can keep up, the build-vs-buy decision has never been more critical.
Financial institutions will spend $39.1 billion on fraud detection and prevention before 2030, according to Juniper Research. These rising investments reflect the growing strain on fraud defenses as threats continue to expand.
This pressure raises a familiar dilemma: should institutions build their own fraud prevention systems, or rely on specialist vendors? How do the costs compare? What about long-term effectiveness? And where do you even start?
For financial institutions facing these questions, this article distills practical insights from the industry. It examines the pros and cons of both approaches and shows why fraud prevention is a continuous journey, not a fixed choice.
As Chen Zamir explained on Behind Enemy Lines, financial institutions should begin with their strategic goals and long-term vision. Fraud prevention is more of a chess game than a chase. You have to anticipate the opponent’s next move and design defenses accordingly.
In other words, the build-vs-buy decision shouldn’t hinge on “can we code this in six months,” but on whether the choice aligns with long-term strategic fit in a constantly moving landscape.
Still, misconceptions about what building vs buying really entails are common, and they can steer banks and credit unions off course. Here are the ones seen most often, along with the realities behind them.
Many institutions have excellent engineering talent—people they’ve invested in heavily—and it’s natural to assume that leveraging internal resources to build an in-house system is the logical next step. In theory, this approach can deliver tighter strategic alignment and potentially even better performance. That part is true.
However, here are the facts to consider:
Practically speaking, you might have strong engineers, but do you genuinely possess deep internal fraud expertise? The knowledge of attacker tactics, behavioral patterns, calibration, and tuning? Are your engineers experienced in designing real-time detection systems? And do you have verified internal workflows and human processes to support them?
It’s important to remember that fraud management always ends with a human making a decision. The tools you build must serve (and ideally strengthen) that decision-making process.
As Chen Zamir puts it: “If you don’t have the team that makes these decisions… and you’re not even aware which decisions you need to take and how… then how do you know what to build?”
A common belief is that building in-house is cheaper. The logic usually goes like this: institutions already have engineering teams, they know their systems and risk profile better than anyone else, and they assume they can build a leaner solution, focused only on the capabilities they truly need, without paying for a full vendor platform. This creates an impression of tighter control over scope and lower long-term cost.
On the surface, this sounds reasonable. However, here are the facts:
Put simply, committing to an in-house build means committing to a continual investment. Fraud prevention systems are never “finished,” because fraud tactics are never fixed. You have a live adversary that works tirelessly against you—so your detection must stay just as live and adaptable.
This is why maintenance and evolution end up far more expensive than most teams anticipate.
There is a common belief that having unique, proprietary data automatically leads to better outcomes, and that machine learning (ML) models trained solely on this data will outperform anything a vendor can offer. In some areas, that can be true. But in fraud prevention, the reality is more complex.
Here are the facts:
Your own data is crucial, but it can also become a trap. One of the most common pitfalls is assuming your dataset represents the whole world. It doesn’t. Fraud patterns emerge elsewhere first, evolve elsewhere first, and vendors often see them long before an individual institution does. Thinking only within the boundaries of your own data means missing the bigger picture.
While building is rarely the easiest or cheapest option, often leaving institutions investing far beyond the original plan with detection that never fully matures, buying is not a magic shortcut either. It comes with its own advantages and its own considerations. Understanding both sides is key to making the right long-term decision.
Why and When to Buy
Buying also comes with considerations. No vendor will ever understand your business, KPIs, customer behavior, and risk appetite as deeply as your internal teams do, and even the most flexible platform cannot be tailored one-to-one to every institution’s exact needs. These limitations don’t diminish the value of buying. Rather, they highlight that vendor solutions work best when paired with strong internal ownership and a clear fraud strategy.
→ If not, don’t build.
→ If the math only includes the initial build, it’s wrong.
→ If expertise is thin or centralized, building is a liability.
→ If you can’t confidently answer yes, buying is safer.
→ If speed matters, buy first.
There’s also one more crucial question: can a vendor platform be combined with your own logic or data? In most cases, the answer leads to a hybrid approach.
Most financial institutions already have parts of a working fraud prevention setup: internal rules, models, case management, or monitoring workflows. What they often lack are specific capabilities a vendor can provide, or support for emerging fraud vectors that are difficult to address in-house. A hybrid path allows institutions to keep what works and strengthen what doesn’t.
A common example is a bank that has strong defenses against unauthorized fraud (e.g., solid controls around credential misuse, device risk, and transactional anomalies) but still struggles with emerging threats.
In these cases, a hybrid approach works best: the institution keeps the parts that already perform well and brings in a vendor to cover the gaps that require specialized capabilities.
ThreatMark’s fraud disruption model is built to slot into different maturity levels and operating models. Institutions that need to buy can adopt ready-to-use capabilities, such as behavioral intelligence or early-stage phishing site monitoring that provide immediate coverage where internal systems are weakest.
For institutions already building in-house, ThreatMark operates as a complementary layer, extending detection into areas that are difficult to cover internally.
Build vs buy is a spectrum, not a binary choice. Early on, institutions typically rely more on vendor capabilities; with greater maturity, some functions naturally shift in-house. But a degree of outsourcing almost always remains. What matters is choosing the mix that supports your strategy and strengthens your defenses across the entire fraud lifecycle.