Fraud is getting smarter, but so is the technology fighting it. Let’s look at the Best fraud detection RegTech companies and see how they are helping businesses spot trouble before it gets worse.

It is becoming increasingly difficult to detect frauds, as a large number of payments and banking activities now take place online. From fake identities and account takeovers to payment scams, fraudsters are finding new ways to commit fraud and get away with it.

The Best fraud detection RegTech companies are using AI, machine learning, behavioral data, and real-time monitoring to help businesses spot suspicious activity earlier. In this Business Fortune article, we look at leading RegTech companies based on their technology, reported detection results, scale, and market adoption. We will also explain how RegTech detects fraud, why businesses are adopting it, and what companies should consider when choosing fraud prevention technology.

How does RegTech detect fraud?

The aim is not only to prevent as many transactions as possible. However, a system should not stop real customers from using their cards since there will be numerous false positives. This is the reason why accuracy, false positives, speed, scalability, and adoption are all important factors for comparison of anti-fraud systems.

RegTech uses technology to monitor financial transactions and find any suspicious behavior. Traditional systems often depend heavily on fixed rules. For example, a bank may create a rule that flags an unusually large payment or activity from a new location.

Modern automated fraud detection systems can go further. Such models can analyze normal customer behavior and detect any abnormal changes. Moreover, some platforms are capable of analyzing behavior on larger networks to determine any connections among accounts, devices, merchants, and transactions.

The third key technology that is worth discussing is behavioral technology. According to the developers of BioCatch, their platform examines thousands of behavioral and device parameters, such as how users interact with online banking systems. However, unusual behavior during the session may provide warning signal.

Leading RegTech companies in fraud detection

Different companies focus on different fraud types, including card fraud, payment fraud, scams, account takeover, identity fraud, and financial crime. The following companies stand out because of their reported technology capabilities, customer adoption, scale, and documented results.

Feedzai

Feedzai is one of the major names in AI-powered fraud prevention. Its platform combines transaction information with behavioral and other data to assess payment risk in real time. The company says its technology protects more than 1 billion consumers worldwide and processes around $9 trillion in payments each year. It also reports that more than 1,000 U.S. financial institutions use its risk score.

Feedzai's customer examples show how its technology can be used across different financial services. Reported cases include fraud reductions for banks, payment companies, and card issuers. One useful feature is the focus on reducing false declines. A fraud system is not very helpful if it blocks too many genuine transactions. Feedzai therefore focuses on balancing fraud prevention with customer approval rates.

Featurespace

Featurespace has built its fraud platform around adaptive behavioral analytics. Instead of relying only on known fraud patterns, its technology studies customer behavior and can adjust as behavior changes. The company says its platform protects 500 million consumers from risk and processes 50.4 billion events each year. It also reports a 75% reduction in false-positive alerts across its platform.

One interesting example is Enfuce, a European card issuer and payment processor. Featurespace reports a 98.16% fraud detection rate in that deployment, alongside more than 11 million authorizations scored each month. These figures should be viewed as individual customer results rather than a universal accuracy score for every Featurespace deployment. Featurespace also works across payment fraud, card fraud, application fraud, merchant acquiring, and check fraud, giving financial institutions a broader set of tools.

BioCatch

BioCatch takes a different approach by focusing strongly on behavioral intelligence and device information. Its systems study how customers interact with digital banking services. This can include typing patterns, mouse movements, touchscreen activity, device characteristics, and other signals.

BioCatch reported that it had more than 340 financial institution customers at the end of 2025 and protected more than 660 million people. It also said it analyzed more than 17 billion user sessions each month. By 2026, the company reported more than 350 banking customers and said its technology assessed $17.2 trillion in transactions during 2025.

Its approach is especially relevant as scams become more focused on manipulating genuine customers. Instead of only asking whether a transaction looks suspicious, behavioral systems can look for signs that a customer may be under pressure or being guided by a scammer.

NICE Actimize

NICE Actimize is another major name in fraud and financial crime technology. Its products cover fraud detection, investigations, anti-money laundering, and related risk areas.

The company uses large transaction datasets and collective intelligence to improve detection. In its 2025 Fraud Insights Report, NICE Actimize said its analysis covered billions of banking and payment transactions.

In 2026, NICE Actimize introduced its Insights Network to provide financial institutions with real-time information about counterparty risk. The aim is to help banks identify suspicious recipients before money moves.

This type of network approach is becoming increasingly important because fraud rarely happens in isolation. The same accounts, devices, identities, or payment destinations can appear across several incidents.

What is the most accurate fraud detection software?

No one software program can be considered to be the most precise in all business cases because of the following factors: type of fraud, the level of quality of company data, market environment, transaction volume, and configuration of the system.

Example, Featurespace claims 98.16% fraud detection rate in the case of Enfuce. Other vendors publish different measures, such as fraud prevented, false-positive reduction, transactions analyzed, or customer coverage.

Therefore, businesses should not compare percentages without checking what each number actually measures. A platform that performs strongly against card fraud may not produce the same results against authorized payment scams or synthetic identity fraud.

So, which RegTech has the highest fraud detection accuracy? The answer depends on the specific use case and the measurement used. For instance, Featurespace has published customer results showing a 98.16% fraud detection rate for Enfuce. It has also reported that a customer identified more than 90% of check fraud in a specific deployment.

BioCatch publishes behavioral detection and fraud-prevention results, while Feedzai and NICE Actimize publish large-scale transaction and customer outcomes.

Can RegTech prevent financial fraud?

Modern fraud prevention solutions can also work before a payment takes place. For example, network intelligence can help identify risky recipient accounts, while behavioral technology can identify suspicious activity during a digital session.

BioCatch's Trust network is designed to share financial-crime intelligence between participating institutions so that suspicious receiving accounts can be identified before funds move. Human oversight remains important. Fraud changes constantly, and organizations need analysts, clear processes, customer communication, and strong security controls alongside technology.

Why are companies adopting RegTech for fraud prevention?

The biggest reason is speed. Manual teams cannot review every transaction in real time when a financial institution handles millions of payments. Automated systems can analyze large volumes of activity within seconds and send the most important cases to investigators.

There is also a customer experience issue. Businesses want to stop fraud without repeatedly asking genuine customers to prove that they are legitimate.

Modern systems can help by using more signals instead of relying on a single rule.

Cost is another factor. Better automation can reduce the number of unnecessary alerts and allow fraud teams to spend more time investigating serious cases.

Finally, fraud itself is becoming more complex. AI-assisted scams, account takeovers, mule accounts, synthetic identities, and social engineering require tools that can learn from changing patterns.

What businesses should check before choosing a RegTech platform

A strong fraud platform is not simply the one with the biggest accuracy percentage. Businesses should look at several factors like,

Detection accuracy: How does the vendor determine accuracy? Are their results relevant for the company's unique type of fraud?

False positives: Determine the percentage of false alerts for legitimate customers.

Speed: Real-time transactions require real-time decisioning.

Scalability: The system must be able to handle increasing volumes of transactions without slowing down.

Signals: Increased amount of data could give additional insight into risks for the fraud team.

Integration: The system should integrate with other banking, payments, identity, and case management systems.

Investigations: The fraud team needs to be able to investigate findings.

Track record: Customer volumes, transaction volumes, and length of deployment time can provide valuable information.

However, the future of fraud prevention technology lies with solutions that use the combination of transactional, behavioral, identity, network, and AI data.

Rather than analyzing just one transaction, modern systems analyze the process of transactions before, during, and after the transactions. Thus, the best fraud detection RegTech providers of 2026 compete not merely in terms of the volume of alerts generated by them. What matters is their ability to detect genuine risks, their speed of reaction, their protection of genuine customers, and integration of their technology into the whole fraud detection process.

And for the financial institutions, the right choice will depend on the type of fraud they face, their data, transaction volume, customer base, and risk strategy.

FAQs

What is RegTech in fraud detection?

RegTech uses technologies such as AI, machine learning, behavioral analytics, automation, and data analysis to help financial companies identify and manage fraud risks.

Are automated fraud detection systems better than manual checks?

They can process far more transactions at much higher speed. However, human investigators remain important for complex cases, unusual situations, and final decisions.

Which fraud types can RegTech detect?

Depending on the platform, RegTech can help detect payment fraud, card fraud, account takeover, identity fraud, application fraud, scams, mule accounts, and money laundering activity.

Why is false-positive reduction important?

A false positive happens when a legitimate transaction is incorrectly treated as suspicious. Too many false alerts can frustrate customers and create unnecessary work for fraud teams.

How should a company measure fraud detection accuracy?

Companies should look beyond one percentage. They should compare detection rates, false positives, fraud losses, investigation time, customer friction, processing speed, and results for the specific fraud types they need to manage.