A fraud prevention platform is software that detects, blocks, and investigates fraudulent activity across digital channels, combining transactional antifraud, device intelligence, behavioral analytics, machine learning, and threat intelligence.
In banking and fintech, this typically means countering account takeover, payment fraud, APP (authorized push payment) scams, synthetic identity fraud, and money mule operations; in e-commerce and digital-first environments, the focus shifts toward payment fraud, chargeback abuse, and account fraud at checkout.
The top fraud prevention platforms for banks and fintechs in 2026 combine real-time session analytics, device fingerprinting, and behavioral biometrics to stop fraudulent transactions before they’re authorized. Leading platforms include Group-IB Fraud Protection, Feedzai, Sift, DataVisor, and Kount, each built for a different part of the fraud landscape, from core banking to digital-first fintech and marketplace risk.
The right fraud prevention platform in 2026 has to close both ends of the problem: stop more fraud and approve more genuine customers. Not one or the other. This guide covers the platforms that actually do that, what sets them apart, and how to choose the right one for your institution.
| Key Takeaways |
| Most fraud platforms only see a threat once it becomes a transaction. Group-IB’s research puts the average gap between cyber visibility and fraud detection at 23 days, the time a campaign spends staging infrastructure and warming up mule accounts before a payment ever reaches your rails. |
| Mule networks and APP scam rings often operate across multiple institutions simultaneously, staying below the detection threshold at any single one. Closing that gap requires visibility beyond a single institution’s own data, something most fraud stacks aren’t built for today. |
| Group-IB Fraud Protection combines device intelligence, behavioral biometrics, advanced anti-bot technology, and Group-IB’s proprietary Threat Intelligence and Digital Risk Protection into a unified platform. For institutions that need visibility beyond their own walls, |
What the fraud threat landscape looks like for banks and fintechs in 2026
Before you evaluate a platform, you need to understand what you are defending against. The threat mix shapes everything: which capabilities actually matter, which ones are nice-to-have, and which gaps will cost you.
Four vectors shape the threat landscape in 2026.
Synthetic identity fraud is the highest-volume fraud type by case count. Criminals blend real and fabricated information, a legitimate Social Security number, a fake name, and a constructed credit history to build ghost profiles that age naturally for months before busting out.
APP scams are the most costly type of fraud per incident. 29% of fraud events are attributed to customers who were coerced or scammed into authorizing transfers themselves. Because the customer initiates the payment, systems that only monitor unauthorized transactions miss it entirely. The authorization is real; however, the context is criminal.
Account takeover is scaling through automation. Attackers run credential-stuffing campaigns that test millions of stolen login combinations per hour across multiple institutions simultaneously. One successful login can unlock a saved payment method, a stored balance, or a transfer limit set for a legitimate customer.
Mule network abuse is invisible to any single institution. Coordinated rings move stolen funds through chains of recruited or synthetic accounts, staying below detection thresholds at every institution they touch. The full picture only emerges when you can see across the network.
That is the environment your platform needs to operate in.
What to look for when evaluating a fraud prevention platform
Here’s what to look for when evaluating a fraud prevention platform, because not all of them are built for the same problem, and the wrong choice costs more than the platform itself.
1. How it detects fraud
Fraud detection systems differ not only in accuracy but in the types of signals they analyze. Device signals, transaction monitoring, and behavioral biometrics each cover a different part of the attack surface, and none is sufficient on its own.
Because behavioral signals are difficult for fraudsters to replicate, behavioral intelligence provides deeper insight, particularly for detecting account takeover, session hijacking, and social engineering. The right platform combines all three layers in a single risk engine.
Group-IB Fraud Protection combines device intelligence, behavioral biometrics, and session analytics, and adds proprietary Threat Intelligence and Digital Risk Protection on top, feeding criminal marketplace signals directly into fraud scoring before attackers deploy them. A financial services firm using Group-IB reduced false positives by 20%, detected 10-20% more fraud, and cut OTP reliance by 30%.
2. Whether it was built for your sector
Many fraud detection tools were originally built for e-commerce. They often fall short in the context of modern banking, where the threats are session hijacking, APP fraud, mule networks, and coordinated credential abuse, not abandoned checkouts.
Group-IB Fraud Protection is purpose-built for financial institutions: it monitors the full banking session, integrates Threat Intelligence and Digital Risk Protection natively, and detects the fraud patterns specific to banking – session hijacking, mule activity, account takeover, synthetic identity fraud, and scam-call-driven or social-engineering-driven APP scams.
KuppingerCole independently evaluated Group-IB Fraud Protection specifically for financial institutions in its 2026 Buyer’s Compass for Fraud Reduction Intelligence Platforms (Finance), highlighting its investigative interface as ‘top-notch,’ with intuitive map and graph views.
3. Whether it catches AI-generated and bot-driven fraud
Most detection tools were built to catch scripts, not reasoning agents. As AI agents get better at completing flows built for people, checking for “human vs. bot” alone isn’t enough; a platform needs to score intent across all three categories, on every request, across web, mobile, and API.
Group-IB Fraud Protection verifies the intent behind each interaction- human, bot, or AI agent- through behavioral analysis that compares legitimate user patterns against automated and AI-driven ones, paired with device fingerprinting that flags emulators and substituted device parameters. Because these checks run passively, genuine users aren’t interrupted with CAPTCHAs or repeat login prompts just to prove they’re not a bot.
4. How fast it adapts when attack patterns shift
Static rules fail the moment an attacker changes their approach. Financial institutions should prioritize solutions that combine behavioral, transactional, and contextual signals fed by threat intelligence and digital risk protection into a unified risk engine that updates as threats evolve.
Group-IB systems monitor underground forums for new fraud toolkits and DarkLLMs, feeding live threat intelligence into Fraud Protection’s detection models before new attack methods reach your customers. The Weaponized AI 2026 report documents exactly how that pipeline works.
5. Whether it reduces false positives without sacrificing detection
Every false positive costs you: a legitimate customer declined, locked out, or over-frictioned. The best platforms solve for both sides simultaneously.
Group-IB Fraud Protection cross-references session signals, device intelligence, and behavioral baselines to distinguish genuine users from fraudulent sessions with high precision.
This includes detecting anti-detect browsers and virtual cameras used in deepfake identity spoofing, capabilities KuppingerCole specifically highlighted and most competitors don’t address at all.
A Forrester Total Economic Impact study commissioned by Group-IB found a 130% ROI over three years, with a payback period under six months, driven largely by reduced false positives.
6. Integration and time to value
A platform that takes twelve months to integrate won’t protect you today. Group-IB Fraud Protection deploys on AWS, Group-IB cloud, your own cloud, on-prem, or hybrid, and is available on AWS Marketplace, integrating with existing fraud management systems without requiring a full-stack replacement.
7. Pricing model and coverage
Per-API-call and per-transaction pricing forces a trade-off: institutions monitor only the sessions and pages they can afford to cover, leaving gaps that fraudsters learn to exploit.
Group-IB Fraud Protection is licensed per active user, not per API call or transaction. Every page, every session, every user journey is protected from login to logout, with no coverage gaps and no cost surprises as usage scales.
Transaction, session, and Cyber-Fraud Fusion: the three layers of modern fraud detection
Transaction intelligence still matters, but it was never designed to catch what account takeover, APP scams, automated abuse, and coordinated mule activity actually look like; none of those show up as a suspicious transaction until it’s often too late. That’s what pushed session and device intelligence into the picture: by watching behavior, device identity, navigation patterns, and session anomalies, a platform can flag a threat before authorization ever happens, not after.
Cyber-Fraud Fusion pushes that timeline back even further. Instead of waiting for something to happen inside the institution, it pulls in what’s happening outside it- phishing infrastructure, compromised credentials, criminal marketplaces, known fraud infrastructure, criminal payment details- and correlates it with the institution’s own activity in real time.
The best platform depends on the institution. Large banks may prioritize broad fraud and AML operations; digital-first companies may prioritize behavioral and device intelligence; institutions focused on pre-transaction disruption may place more weight on adversary visibility and session-level context.
Group-IB differentiates by combining session and device intelligence with native Threat Intelligence and Digital Risk Protection, allowing fraud teams to connect activity inside the banking session with signals observed outside the bank.
Top 5 Fraud Prevention Platforms for Banks and Fintechs in 2026
Based on those criteria, here are the five platforms that hold up best for banks and fintechs in 2026, starting with the one built to catch fraud before it becomes a transaction.
1. Group-IB Fraud Protection
Best for: banks, fintechs, payment providers, and iGaming platforms needing intelligence-led fraud prevention at scale
Group-IB Fraud Protection is a real-time fraud detection and prevention platform that combines device intelligence, behavioral biometrics, patented anti-bot technology, and Threat Intelligence and Digital Risk Protection to stop fraud across login, payment, and transfer flows, without adding friction for genuine customers.
Most fraud platforms only see a threat once it becomes a transaction. Group-IB’s research puts the average gap between cyber visibility and fraud detection at 23 days, the time a campaign spends staging infrastructure and warming up mule accounts before a payment ever reaches your rails. The Before Fraud Transacts whitepaper lays out how to move detection back to that point in the attacker’s timeline instead of scoring the transaction itself.
What separates Group-IB from most fraud platforms is what sits underneath the detection. Group-IB’s own Threat Intelligence and Digital Risk Protection operations monitor criminal marketplaces and phishing infrastructure where stolen credentials, device fingerprints, and account access listings surface, feeding that intelligence directly into fraud scoring.
Threat Intelligence’s Suspicious Payment Details module tracks the IBANs, card numbers, and crypto wallets tied to confirmed mule networks and criminal payment infrastructure, and cross-references them against outbound transactions in real time, before a transfer completes. Group-IB detects the precursor activity before it reaches your institution.
| Pros | Cons |
| Strong session-level fraud prevention across major fraud types, including social engineering, account takeover, payment fraud, bot activity, malware-related fraud, APP scams, and credit fraud. | A few reviewers suggested small improvements around reporting, admin panel visibility, and behavior analytics. |
| Real-time, explainable protection across digital channels, powered by behavioral analytics and machine learning models that adapt as attacker behavior shifts, rather than relying on static rules. | |
| Cyber-Fraud Fusion approach connects cyber and fraud signals in one investigation workflow, rather than treating compromised sessions, suspicious devices, mule activity, and payment risk as separate problems. | |
| Enriched with Group-IB Threat Intelligence and Digital Risk Protection, helping fraud teams connect in-session behavior with external criminal infrastructure, compromised credentials, phishing activity, mule indicators, and attacker preparation. | |
| Useful investigation features and real-time explainability, including device fingerprinting, Global ID technology, graph analysis, behavioral analytics, and adaptive machine learning models that help analysts understand why a session is risky. |
For institutions that want to extend that capability across a network, Group-IB also offers the Cyber Fraud Intelligence Platform, a separate, system-agnostic product. Using patented Distributed Tokenization, validated by Bureau Veritas for GDPR compliance, it enables real-time signal sharing between institutions without any raw personal data leaving each institution’s environment.
Group-IB Fraud Protection is trusted by over 500 million users across banking, payment, and e-commerce applications globally.
→ Group-IB Fraud Protection · Cyber Fraud Intelligence Platform
2. Feedzai
Best for: large financial institutions and regulated fintechs needing unified fraud and AML coverage
Feedzai is positioned as an AI-native RiskOps platform that integrates fraud detection, AML monitoring, onboarding, and case management into a single architecture. Its strength lies in combining adaptive AI with user-defined rules, flexible enough for large banks and modern enough for fintech adoption.
Its prebuilt fraud scenarios cover scams, APP fraud, and mule activity out of the box, reducing deployment time for institutions with common fraud patterns. For teams that need fraud and AML under one roof without building the integration themselves, Feedzai removes a significant operational burden.
The trade-off is complexity. Smaller institutions may struggle to use it fully, and premium pricing reflects its enterprise positioning. Fintechs evaluating Feedzai should pressure-test the implementation timeline and internal resource requirements before committing.
| Pros | Cons |
| RiskOps platform for banks, PSPs, and payment networks, covering the customer journey from onboarding to payments. | Best used with strong internal governance, as the platform’s flexibility requires careful configuration and ongoing management. |
| Uses full user context, including login history, device signals, timing, and transaction behavior, instead of looking at transactions in isolation. | Support depth can improve in more complex or time-sensitive situations, based on reviewer feedback. |
| Machine learning and automation help reduce manual work, improve explainability, and support large-scale fraud detection. | Advanced setup may take time for teams without mature fraud operations or data science resources. |
3. Sift
Best for: digital-first fintechs, marketplaces, and payment platforms needing behavioral fraud prevention
Sift is built around behavioral analytics and device intelligence for digital-first environments. It prevents payment fraud, account takeover, and fake content through device intelligence, behavioral scoring, and chargeback protection.
Sift performs strongly in high-transaction-volume environments where chargeback exposure is the primary concern and user experience friction must stay low. Its models are trained on a broad global signal network across its customer base.
It is not built for regulated banking. Institutions with AML requirements or complex compliance reporting will need to pair Sift with additional tooling; the platform’s architecture is optimized for digital commerce, not financial services compliance.
| Pros | Cons |
| Fraud detection and real-time risk insights, helping teams identify suspicious activity faster and make more informed decisions. | Can generate false positives, which may require extra review time for fraud teams. |
| User-friendly interface with strong analytics, making it easier to review users, orders, linked accounts, and suspicious behavior. | Can feel noisy or overwhelming when it shows many signals at once. |
| Global data network and scalable platform, supporting fraud prevention for 700+ global brands and over one trillion annual events. | Some advanced workflows may need refinement, including deeper customization, clearer model reasoning, and more flexible reporting. |
4. DataVisor
Best for: financial institutions needing unsupervised machine learning to catch novel fraud patterns before they generate labeled training data
DataVisor’s core differentiator is its unsupervised ML engine. It combines unsupervised machine learning, supervised models, link analysis, and agentic AI that automates investigations and rule tuning, enabling organizations to adapt to emerging fraud tactics without relying solely on historical labels or rigid rules.
Most fraud platforms need labeled examples of a fraud pattern before they can detect it. DataVisor surfaces patterns before they generate enough history to train against, which matters when attackers are running schemes your institution has never seen before.
Best suited to institutions dealing with high volumes of coordinated fraud, where pattern recognition across accounts is the priority. Its unified architecture covers fraud, AML, KYC/KYB, and case management on a single platform.
| Pros | Cons |
| Control over scoring logic, allowing teams to build, tune, and A/B test fraud strategies quickly. | Setup can take time, especially with legacy systems or custom environments. |
| Good for complex fraud networks, with ML models that help detect fraud rings, crime networks, and nuanced attack patterns. | Learning curve can be steep because of the platform’s depth and feature range. |
| Flexible data and feature platform, useful for teams that want to add new fields, connect data sources, and create real-time features. | Documentation and UI could be clearer, especially for new or non-technical users. |
5. Kount (Equifax)
Best for: fintechs and mid-sized platforms needing pre-authorization identity and device-based fraud prevention
Kount focuses on identity trust and device analytics, stopping fraudulent transactions pre-authorization. Backed by Equifax’s identity data network, Kount has broad coverage for identity-based fraud, including account takeover, synthetic identity, and new account fraud.
Its primary strength is the pre-authorization layer. Kount performs best at identifying fraudulent intent before a transaction is approved, using device signals, identity graph data, and behavioral analytics.
Institutions needing post-authorization monitoring, AML coverage, or cross-institution signal sharing will need to supplement with additional tooling. Kount is a strong component of a fraud stack, but not a complete one for institutions with complex financial crime requirements.
| Pros | Cons |
| Fraud detection, with accurate scoring, real-time detection, and reduced fraud losses. | Setup and configuration can be complex, especially for custom integrations or merchants outside recommended payment processors. |
| Clear dashboard and risk scores, helping teams approve, decline, or review transactions quickly. | Pricing may feel high for smaller businesses, especially micro-enterprises or teams with limited fraud budgets. |
| Customizable rules and workflows, allowing teams to adapt fraud controls and reduce false declines. | Some reporting and UI areas could be more flexible, including clearer terminology, easier information retrieval, and faster support during busy periods. |
How to match a fraud prevention platform to your institution type
The platform that works for a challenger bank will not work for a large retail bank. Threat profiles differ, regulatory obligations differ, and detection logic that catches one type of fraud will miss another entirely. Match the platform to your actual exposure and not the most impressive demo.
| Institution type | Primary fraud risk | Platform priority |
| Retail bank – large | APP scams, mule networks, AML overlap | Session-level detection + cross-institution intelligence sharing, working alongside a dedicated AML system |
| Neobank / challenger | Account takeover, synthetic identity at onboarding | Behavioral biometrics + device intelligence, working alongside a dedicated identity verification vendor |
| Payment provider | Card testing, carding, chargebacks | Session analytics + velocity controls |
| Fintech | Bust-out fraud, first-party fraud | Session-level detection + investigative graph analysis, working alongside a dedicated identity verification vendor |
| iGaming / crypto | Bonus abuse, account farming, ATO | Bot detection + device fingerprinting |
Not sure which row fits your institution?
Walk through your specific fraud exposure with a Group-IB specialist and get a straight answer, not a sales pitch.
Why investigative depth is the capability most fraud stacks are missing
Most platforms are good at raising a flag. Far fewer show an analyst why the flag matters and what it’s connected to. A suspicious login, an unusual transfer, and a newly added beneficiary can each look low-risk on their own. The pattern only becomes obvious once someone can see how they relate.
Group-IB Fraud Protection includes an investigative interface that KuppingerCole rated ‘top-notch,’ with graph visualization mapping relationships between users, devices, IPs, and fraud infrastructure. Instead of reviewing isolated alerts one at a time, analysts can trace how a single device or credential connects to other accounts, sessions, and known fraud patterns, surfacing the coordinated activity that mule networks and fraud rings are designed to hide.
For institutions handling APP scams and organized fraud, this shortens the distance between ‘something looks wrong’ and ‘here’s the network behind it,’ reducing investigation time and helping analysts act on evidence rather than guesswork.
Choosing a fraud prevention platform is a strategic decision, not a software purchase
The platform you choose will shape your fraud operations for years. The wrong one, optimized for a threat profile that does not match your actual exposure, or too slow to adapt when attack patterns shift, costs more in fraud losses and false declines than it saves.
Three questions should anchor every evaluation:
- Does the platform see the full session or just the transaction?
- Does it turn a new fraud pattern into a live rule in minutes, or wait for the next release cycle?
- Does it integrate threat intelligence from outside your network, from the criminal marketplaces where the next attack is already being planned?
Your next three steps:
- Map your actual fraud exposure. Before you evaluate any platform, know what you are defending against. Account takeover, APP scams, synthetic identity, and mule networks each require different detection logic. Start with the threat profile, then find the platform that matches it.
- Test your current stack against the questions above. Ask your existing provider whether they see session-level signals or only transaction data. Ask how long it takes them to turn a new fraud pattern into a live rule: minutes, or only at the next release cycle. Ask where their intelligence comes from. The answers will tell you where your gaps are faster than any RFP.
- Talk to Group-IB. Group-IB monitors the full session, not just the transaction, combining device intelligence, behavioral biometrics, anti-detect browser, deepfake detection, Threat Intelligence, and Digital Risk Protection into a unified platform built for the fraud landscape of 2026. Trusted by 500 million+ users globally, recognized by KuppingerCole as an Overall Leader in its Leadership Compass for Fraud Reduction Intelligence Platforms (eCommerce), and independently evaluated in KuppingerCole’s 2026 Buyer’s Compass for Fraud Reduction Intelligence Platforms (Finance).
The next attack is already being planned. The question is whether your platform will see it coming.
Talk to a Group-IB fraud specialist →
Frequently Asked Questions
1. What is a fraud prevention platform, and how does it work?
A fraud prevention platform detects and stops suspicious activity before it completes a transaction. It analyzes session behavior, device intelligence, identity signals, network indicators, and transaction context in real time to identify account takeover, payment fraud, social engineering, mule activity, and other fraud patterns.
2. How does Group-IB Fraud Protection differ from Feedzai or Sift?
Group-IB Fraud Protection focuses on real-time digital risk detection at the session layer, combining device intelligence, behavioral analytics, threat intelligence, Digital Risk Protection, and fraud investigation capabilities. While platforms like Feedzai and Sift are widely known for transaction monitoring, risk scoring, and machine-learning-based fraud detection, Group-IB places greater emphasis on detecting fraud before authorization by identifying compromised sessions, suspicious devices, mule-linked behavior, and attacker infrastructure.
3. What are the first steps when a bank detects a coordinated fraud ring?
The coordinated fraud ring response should start with rapid containment and shared-pattern analysis.
- Freeze risky activity across linked accounts, devices, and payment routes.
- Map common indicators such as IPs, devices, mule accounts, and beneficiaries.
- Preserve evidence, including session logs, transaction records, and device data.
- Separate victims from mules to guide response and reporting.
- Update controls and escalate to AML, regulators, or law enforcement where needed.
4. How does cross-institution fraud intelligence sharing work?
Cross-institution fraud intelligence sharing tokenizes device, account, and behavioral risk signals at each institution, without exposing raw personal data. The network compares these tokenized signals in milliseconds and returns intelligence that the institution’s own fraud system can use to block suspicious transactions before funds move.
5. What is the difference between fraud prevention and AML, and do I need both?
Fraud prevention stops unauthorized or deceptive transactions as they happen, while AML detects and reports the movement of illicit funds. Both are needed because organized fraud rings often use the same infrastructure for fraud and money laundering, making unified detection more effective.
6. How do fraud prevention platforms reduce false positives without missing real fraud?
Fraud prevention platforms reduce false positives by analyzing session-level context, including device consistency, behavior, navigation patterns, and network signals. This helps identify real user intent more accurately than transaction data alone.
7. What should a fintech look for in a fraud prevention platform?
Fintechs should look for real-time session-layer decisioning, behavioral biometrics, API-first integration, threat intelligence, and Digital Risk Protection. These capabilities help stop fraud before authorization while adapting to fast-moving attack patterns
