Top 10 Fraud Prevention Solutions in 2025

Fraud is becoming a serious threat to digital businesses in 2025. According to recent projections published by Forbes, cybercrime is expected to inflict a staggering $10.5 trillion in global damages this year, placing it behind only the United States and China in terms of economic impact. 

In the United States, the Federal Trade Commission has highlighted identity theft, imposter scams, and account takeovers among the most reported fraud categories. In the UK 41% of all reported crime now relates to fraud, with losses surpassing £1.17 billion in 2024 alone, according to Reuters. In parallel, a United Nations investigation reveals that the organized cyberscam industry, once concentrated in Southeast Asia, has now spread across continents, from South America to Eastern Europe, driven by large-scale identity abuse and synthetic accounts.

Everywhere in the world, credential stuffing, payment fraud, and unauthorized access attacks are all climbing. And while financial institutions have made progress in building fraud response teams and transaction monitoring systems, fraudsters are increasingly targeting vulnerabilities in digital user onboarding, login events, and account creation flows to fly under the radar.

In the realm of payment, digital safety experts are noting the continued rise of fraud. For instance, it was reported that 62 million Americans experienced credit card fraud in 2024. In addition, when it comes to bot attacks and malicious traffic, it is estimated that malicious bots now make up more than a third of total web traffic (Source). 

To fight back against this rising tide of fraud, companies have begun turning to modern fraud prevention solutions. No longer confined to analyzing transactions after these have taken place, these platforms now serve as sentinels at the edge of onboarding and throughout a user’s lifecycle. They aim to identify anomalies before the damage is done, spotting high-risk signals before access is granted, an account is created, or a transaction is processed. 

This article provides an overview of key criteria that buyers should consider when evaluating a fraud prevention solution, before highlighting ten leading platforms specialized in this area. 

What are fraud prevention solutions?

At their core, fraud prevention solutions are systems designed to identify and block suspicious or harmful behavior before it impacts users, operations, or revenue. These platforms analyze signals at key digital checkpoints, such as onboarding, login attempts, and transactional activity, to assess their related risk and, where relevant, trigger defensive mechanisms. This assessment is rarely based on a single factor. Instead, it draws from a network of indicators, ranging from behavioral patterns and device configurations to metadata from emails, phone numbers, IP addresses, or domains.

Different vendors approach the problem through different lenses. Some prioritize identity intelligence, gathering and analyzing information tied to a user’s digital footprint using open source intelligence. Others rely on behavioral profiling, watching for subtle shifts in session activity that indicate possible bot behavior or credential misuse. And finally, a few focus heavily on transaction monitoring, identifying irregularities in payment flows or spending habits.

Unlike fraud detection tools that flag issues after an event has taken place, prevention solutions aim to intervene in real time or just before a potentially harmful action is completed. They enable pre-screening of users before onboarding, evaluation of login attempts before granting access, and inspection of traffic before allowing harmful interactions.

There are also persistent misconceptions about what these systems do. Some equate fraud prevention exclusively with financial transactions or payment authorization. Others view it as interchangeable with AML or KYC compliance software. In reality, the best fraud prevention solutions provide a flexible framework for evaluating risk across the entire user journey. Whether it’s identifying synthetic identities at sign-up or spotting account takeover attempts during login, these platforms operate as part of a broader trust infrastructure.

In today’s threat environment, relying solely on traditional methods such as ID document verification (in the case of new account creation), two-factor authentication (to protect accounts from unauthorized access), or CAPTCHA-based verification (to block malicious bot traffic) often leaves gaps. As fraud tactics evolve to mimic real users more convincingly, organizations need prevention tools that can silently analyze subtle signals to reveal patterns of deception without adding unnecessary friction to users’ digital journeys.

How to evaluate a fraud prevention platform

Not all fraud prevention solutions have the same functionalities, nor the same level of flexibility. While most claim to stop bad actors, the real test lies in how well a platform adapts to your specific workflows, how transparently it operates, and how actionable its insights are.

For fraud & compliance teams comparing vendors, focusing on feature lists or demo interfaces is not enough. The criteria that matter most, in the long run, tend to reveal themselves during implementation, scale, and incident response.

Here are five key characteristics that should guide the evaluation of any fraud prevention platform:

Ease of customization
Fraud patterns are dynamic, so what works for one company may be irrelevant for another. A platform should allow risk teams to define and adapt their rules without depending on engineering cycles. This includes:

• Rule builders that support complex logic without requiring code
• Real-time dashboards where analysts can test or tune thresholds
• Clear workflows for introducing new signals or adjusting sensitivity levels

Solutions that require developer support for every policy update can delay response and create operational friction.

Explainability of risk decisions
If a platform labels a user as high-risk, can you explain why? This is a crucial aspect that affects regulatory compliance, user trust, and incident resolution. Black-box systems that rely on opaque scoring or AI-generated outputs without explanation often create more confusion than clarity.

• Look for models that provide traceable signal-level evidence
• Ensure your team can audit past decisions in case of disputes or false positives
• Favor vendors who prioritize interpretability and a white-box approach, especially in real-time risk scoring

A tool that cannot explain its conclusions should not be used to support fraud prevention decisions.

Scalability across volume and use cases
Growth brings complexity. Whether expanding into new markets, launching a new product line, or onboarding higher volumes of users, your fraud stack needs to keep up without performance issues or accuracy degradation. Consider whether the platform:

• Maintains response times under load
• Supports multi-region data operations
• Offers flexible data handling for different business units or geographies

A system that performs well at 1,000 checks per day may struggle at 100,000 unless designed for scale.

Integration and documentation
Speed of deployment is often underestimated in vendor comparisons. Extensive documentation and accessibility of support can make the difference between a weeks-long integration and a months-long struggle. You’ll want to confirm:

• API stability and versioning policies
• Availability of real-world code examples or sandbox environments
• Responsiveness of support teams or technical success managers

Solutions that look polished in sales decks can fall apart in real-world implementation if documentation is lacking.

Signal quality over signal count
It’s easy to be impressed by platforms claiming to analyze hundreds of data points per user. But volume means nothing without context or consistency. What matters more is whether the signals are well-maintained, relevant, and up-to-date. When reviewing signal intelligence:

• Ask how often data sources are updated and revalidated
• Test the accuracy of key signals for your specific use case, such as relevant connected accounts for users’ email addresses and phone numbers
• Avoid solutions that promise depth but rely on stale or unreliable enrichment APIs

Quality-driven signal frameworks are more effective than bloated lists of checks that contribute noise instead of clarity.

Fraud prevention is only as strong as the foundation it rests on. Vendors that offer flexibility, transparency, and reliability will almost always outperform those that chase features for the sake of market positioning. Ultimately, the right tool should support your team’s judgment, not make it harder for them to make a decision.

Top 10 fraud prevention solutions in 2025

Below is a quick guide to ten leading fraud prevention platforms that companies should know about in 2025. 

1. Trustfull

Trustfull offers a comprehensive fraud prevention platform primarily built on open source intelligence (OSINT). The platform analyzes hundreds of digital signals and enables financial institutions and digital businesses across payments, lending, travel, and iGaming to detect fraud and financial crime at every stage of the customer journey. 

More specifically, Trustfull focuses on performing silent checks on phone numbers, emails, IP addresses, devices, and web domains, powering pre-configured models and workflows that support better risk decisions without disrupting the user experience. Key use cases include account opening protection for both retail and business customers, account takeover prevention, and malicious traffic detection.

The main areas of focus of Trustfull's fraud prevention platform include:

• Account opening protection: Trustfull helps identify high-risk users at the earliest stage by analyzing digital signals before identity verification begins. This includes detecting synthetic identities, which are fake users constructed from real and fabricated data, through the analysis of phone and email reputation and IP consistency.
• Account takeover prevention: through its Login solution, Trustfull monitors behavioral patterns, device fingerprints, and signal mismatches to detect suspicious login attempts. It flags anomalies like non-human mouse movements, inconsistent typing patterns or sudden changes in IP geolocation, triggering authentication challenges when necessary.
• Malicious traffic detection: Trustfull accurately distinguishes legitimate users from malicious actors - both automated and humans - visiting any websites or platforms. This analysis does not rely on traditional and intrusive methods like CAPTCHA or preemptive authentication, but is based on silent checks of behavioral, network, and privacy signals.
• Deep online due diligence for business verification: Trustfull performs domain intelligence checks to assess the trustworthiness of online merchants and SMEs, analyzing their digital presence to validate their business operations. This enables financial institutions and platforms to reduce exposure to fake companies or fraudulent SME registrations during KYB checks and business onboarding.

Built for companies that need to evaluate user risk while maintaining a frictionless user experience, Trustfull delivers a silent, high-accuracy OSINT-based fraud prevention solution.

2. Sardine

Sardine focuses on real-time fraud prevention for fintech, payment platforms, and crypto businesses, blending identity, device, and behavioral signals to protect against account funding abuse and transactional fraud. Its strength lies in monitoring velocity and user behavior during the earliest phases of financial interaction.

• Designed for high-risk payment environments and crypto applications
• Combines device telemetry, user velocity tracking, and behavioral analytics
• Popular among startups and embedded finance platforms
• Limited in early identity intelligence and silent onboarding capabilities

3. Kount

A veteran in ecommerce fraud prevention, Kount provides a balance between machine learning and rule-based systems to detect risk during checkout and payment authorization. It's widely adopted in retail, especially for reducing chargebacks and false declines.

• Strong presence in card-not-present transaction protection
• Adaptive risk modeling using historical commerce data
• Reliable choice for retailers and digital marketplaces
• Less focused on identity pre-screening or login-stage risk analysis

4. LexisNexis Risk Solutions

LexisNexis brings extensive access to legal, financial, and public data sources, enabling deep entity-level analysis and identity validation. It excels in regulated environments where historical data depth and link analysis are vital to due diligence workflows.

• Effective for compliance-heavy use cases and risk profiling
• Offers detailed identity resolution and linkage across databases
• Preferred by insurers, lenders, and enterprises with large in-house fraud teams
• May require significant integration time and tuning for real-time decisions

5. Shield

Shield delivers fraud protection through device fingerprinting and behavioral biometrics, primarily focusing on detecting automated threats and session anomalies. It analyzes how users interact with devices, rather than who they are, to infer intent and risk.

• Specializing in blocking bot attacks, emulators, and session hijacking
• Uses interaction telemetry (mouse, scroll, click behavior) to build risk profiles
• Frequently layered into mobile app security stacks
• Less applicable for identity checks or organizational onboarding scenarios

6. Ekata

Ekata supports onboarding workflows with global identity verification tools, offering trust scores based on names, phone numbers, email addresses, and locations. Its Identity Graph product connects user attributes to identify suspicious patterns.

• Widely used for cross-border onboarding and account creation flows
• Offers risk scoring that reflects global phone and email trustworthiness
• Performance varies depending on region and data source availability
• No behavioral, device, or login-layer intelligence included

7. SEON

Originally focused on digital footprint enrichment, SEON has recently shifted its focus towards providing more comprehensive AML and risk management solutions. Its fraud API surfaces signals like email age, social media presence, and device reputation to determine user trustworthiness in real time.

• Popular among e-commerce merchants, lenders, and crypto exchanges
• Ranges from connected account analysis to transaction monitoring and AML screening
• Strong for lightweight, rule-based fraud filtering
• Signal reliability may fluctuate depending on user geography and setup

8. Telesign

Telesign provides communications-based fraud prevention, combining phone number intelligence, carrier data, and messaging infrastructure to evaluate user legitimacy. It’s often used to assess risk during account registration, verify phone ownership, and protect SMS-based communications.

• Strong in telecom data enrichment and phone-based reputation scoring
• Useful for platforms that rely on 2FA, OTPs, or SMS verification
• Limited support for broader digital signals, behavioral data, or non-telephony contexts
• Often integrated to prevent fake sign-ups and detect high-risk or disposable numbers

9. Feedzai

Feedzai is known for its application of machine learning to achieve transaction monitoring, particularly in the fight against card fraud, mule accounts, and anomalous fund movement. It’s trusted by global financial institutions that need high-throughput performance and real-time scoring.

• Optimized for high-volume, high-value financial transactions
• Capable of ingesting large-scale data pipelines for real-time analysis
• Strong modeling capabilities, especially for account and payment fraud
• Does not include early-stage trust signals or session behavior

10. Forter

Forter primarily helps e-commerce merchants strike a balance between fraud prevention and customer experience, using network intelligence to identify and authorize legitimate users at checkout. Its collective network data can be used to reduce false positives.

• Tailored for e-commerce fraud scenarios, especially at the final conversion step
• Known for minimizing checkout abandonment due to overzealous fraud rules
• Integrates behavioral signals with global merchant data
• Limited use beyond purchase flows — does not assess onboarding or login risk

Questions to ask a fraud vendor before you sign

Understanding how a fraud prevention solution works when it really counts is fundamental when selecting a fraud prevention vendor. To make a confident choice, teams need to go beyond polished demos and ask the kinds of questions that reveal real-world performance, flexibility, and long-term value. Here are some key questions worth asking during any vendor evaluation:

Do your signals offer real quality, or just volume?
Some platforms boast hundreds of checks, but many signals are static, irrelevant, or poorly maintained. Ask for examples of how signal freshness and source accuracy are monitored over time.

Can my team adapt rules and policies without engineering support?
Flexibility is vital. If every change requires developer time, your team will struggle to respond to emerging fraud patterns. Look for platforms with user-friendly policy editors, sandbox testing environments, and support for custom thresholds.

Are your risk decisions explainable and auditable?
Risk models should be clear enough for analysts, auditors, and even end users to understand. You should be able to trace back a decision to specific signals or thresholds. Avoid systems that rely on black-box outputs.

Is your pricing model scalable and transparent?
Hidden fees, lookup limits, or API throttling can create friction. Clarify pricing tiers, data volume caps, and how costs evolve as your business grows. A scalable solution should grow with you, not penalize you for success.

How do you ensure GDPR compliance when using OSINT or alternative data?
If a vendor uses open-source intelligence or third-party enrichment data, ensure their processing framework respects privacy regulations. Learn more about GDPR & OSINT risk assessment.

Do you offer blind proof-of-concept testing with historical data?
Ask whether the vendor supports a trial using anonymized historical cases to demonstrate signal accuracy and detection performance before committing to a full integration.

What level of tech support and documentation can we expect during implementation?
Reliable fraud prevention vendors should provide detailed API references, clear integration guides, and access to responsive technical support teams to ensure a smooth and timely deployment.

The best fraud prevention vendors will not hesitate to answer these questions directly, and the clarity of their responses will often tell you more than a quick look at their dashboard ever could.

Fraud prevention: what’s on the horizon?

The fraud landscape continues to shift, and so do the tools needed to defend against it. As fraud techniques evolve beyond traditional identity theft and stolen credentials, the next wave of prevention tools will need to meet fraudsters at new entry points, often before a form is even submitted.
Here are some of the key trends shaping fraud prevention in 2025 and beyond:

Silent, non-documentary verification will become the default
Not disrupting the user experience has become fundamental in the fight against fraud. Identity pre-screening through digital signals and behavioral patterns will increasingly precede traditional ID checks and document uploads. Platforms that can silently verify users before asking them to complete standard eKYC and IDV checks will optimize onboarding resources and costs while strengthening their legitimate user base as a result.

The rise of AI agents is challenging conventional bot detection
More users are beginning to rely on AI assistants to perform legitimate tasks on their behalf, booking travel, managing accounts, and even completing purchases. Platforms that treat all automation as hostile will struggle. Fraud solutions will need to distinguish between malicious bots and helpful automation. [See: Know Your Agent – Trustfull’s guide to identifying AI agents]

KYB enrichment is becoming critical for synthetic business detection
Fake companies are increasingly being used to launder money, access fintech services, or impersonate legitimate merchants. This shift demands tools that go beyond registry checks. Domain analysis, OSINT-based verification, and SME enrichment are emerging as the next step in business verification.

Sector-specific tools are outperforming one-size-fits-all platforms
A modular approach is proving more effective than monolithic suites. Whether you’re in crypto, neobanking, BNPL, or gig economy services, solutions tailored to your risk surface - be it sign-ups, sessions, or logins - deliver more accurate protection without compromising user experience.

Signal quality gets prioritized over signal count
Companies are learning that more isn’t always better. A long list of stale or poorly sourced signals can create noise and false positives. High-performing fraud teams are seeking fewer but more actionable indicators, ideally those updated continuously and validated across real-world events.

Fraud prevention is continually evolving, so having a clear picture of where the market is heading is just as important as knowing what vendors offer today.

Devising the best strategy against digital fraud

Which fraud prevention solution is right for you will depend on your business model, existing tech stack, and where you feel you are most exposed to risk, whether during onboarding, account access, merchant sign-up, or payment transactions.

But the fundamentals don’t change. Effective platforms give your team clarity instead of complexity. They offer explainable decisions, customizable risk logic, and signals that are not only accurate but continuously maintained. These solutions adapt as fraud evolves, without overcomplicating your architecture or overwhelming your operations. 

As you evaluate your next fraud prevention partner, ask the difficult questions. Push beyond the UI and feature lists. Focus on the quality of the insights, the depth of the signals, and the flexibility of the system.

And if you're looking for a solution that silently detects high-risk users, login attempts or web sessions in real time, Trustfull is already doing just that. Contact us to strengthen your business protection against fraud.

Disclaimer (June 2025): This content is provided for general informational purposes only and does not constitute legal advice. Trustfull accepts no responsibility for the use of the information provided and disclaims any liability for actions taken based on its content.

FAQs

1. What makes a fraud prevention solution effective in 2025?
An effective solution balances early risk detection with low user friction and offers explainable, high-quality signals that are continuously monitored. The ability to adapt to new fraud vectors, like AI-driven automation and synthetic businesses, is essential.

2. How is digital footprint analysis different from traditional identity verification?
Digital footprint analysis uses metadata from email, phone, device, and browser activity to assess trustworthiness before a user submits documents or completes a transaction. It's designed for silent, real-time decision-making rather than manual checks.

3. Why are behavioral biometrics important for detecting synthetic identities?
Synthetic profiles often mimic static identity data but fail to replicate genuine behavioral patterns like keystroke rhythm or mouse movement. Behavioral biometrics offer a subtle yet powerful way to flag non-human or high-risk activity during login or usage.

4. Can fraud prevention tools also support business verification (KYB)?
Yes, modern platforms are beginning to use domain intelligence and OSINT to assess the legitimacy of merchants and small businesses. This is especially important as fake companies are increasingly used in onboarding fraud and money laundering schemes.