A new customer opens an account on her phone. Two days later, she sends her first instant payment to a contractor finishing her kitchen. She expects both to just work, and she will never think about them again.
Behind the scenes, her bank made two risk decisions in less time than it takes to blink. Was this a real person or a synthetic identity? Was this payment going to a contractor or to a mule account? Get both right, and the bank has a loyal customer and a clean book. Take too long, and she waits, or abandons the app. Get it wrong, and the money is gone the moment it settles.
That is the job of real-time decisioning, and it has become one of the most important choices a fraud or AML leader makes.
The frustrating reality is that most real-time decisioning programs don't fall short because teams lack skill or data. They fall short because their decision layer was built for a slower world: rules that take weeks to change, models that only recognize fraud someone has already labeled, and fraud and AML signals that never meet. This guide explains what real-time decisioning is, how it has evolved, how the ten leading platforms compare, and the criteria to use when choosing one.
If you're comparing platforms by category, see our companion rankings of the top fraud platforms, AML platforms and FRAML platforms.
What Is Real-Time Decisioning?
Real-time decisioning is the ability to evaluate an event, such as a payment, login or account application, and return an approve, decline, step-up or review decision in milliseconds, before the transaction completes.
Before comparing vendors, it's worth being precise about what real-time decisioning includes, and what it does not.
Real-time decisioning is the end-to-end process of receiving an event, enriching it with context, scoring it with rules and machine learning, and returning an action within the transaction window, typically tens of milliseconds. Its output is operational: an approval, a decline, a step-up challenge or a route to review.
Real-time monitoring is different. Monitoring raises an alert after activity happens. Decisioning acts before money moves. Both matter, but only one can stop a loss on a payment rail that settles instantly.
Real-time decisioning is also not a single model, and it is not only for card fraud. Strong decisioning combines rules, supervised machine learning, unsupervised machine learning, and device and behavioral signals in one flow. The same decision layer can cover account opening, account takeover, ACH and wire, RTP and FedNow, P2P and AML transaction monitoring.
Every decision moves through the same lifecycle:
The faster and more completely a platform runs this loop, the more good customers it approves instantly and the more fraud it stops before losses occur. To understand why that loop looks so different in 2026, it helps to look at how it has changed.
How Real-Time Decisioning Evolved Past the Old Playbook
Every week, the fraud and AML leaders who come to us are running smart strategies on decision infrastructure designed for a different era. Many of these systems are only a few years old. But the payments, onboarding and attack patterns of 2026 look very different from the ones those systems were built for.
Three shifts are driving this at once. Understanding them makes it easier to see why the evaluation criteria later in this guide matter.
From Batch Review to In-Flight Decisions
The original model of fraud detection was retrospective: score transactions overnight, review the flagged ones in the morning, and recover what you can. That worked when payments took days to settle and a disputed transaction could be clawed back.
Instant payments removed that buffer. Once an RTP or FedNow payment settles, the money is gone, so the decision has to happen while the transaction is in flight. That puts latency at the center of every evaluation, and not just average latency. A platform has to hold its speed at peak volume, when holiday traffic or a coordinated attack pushes queries per second to their highest point.
From Rules Alone to Layered Machine Learning
Rules are precise, auditable and easy to explain. They are also static: they catch the fraud someone has already seen and written down. Supervised machine learning extends that reach, but it still learns only from labeled examples of past fraud.
Today's attacks are coordinated and change quickly. Fraud rings spread activity across many accounts so each event looks normal on its own. Synthetic identities are built slowly to pass onboarding checks. Catching these patterns calls for unsupervised machine learning, which finds clusters of suspicious behavior without waiting for labels, working alongside rules and supervised models in the same decision.
From Separate Fraud and AML Stacks to One Decision Layer
Fraud and AML teams have distinct goals, expertise and regulatory obligations, and that distinction should stay. What has changed is the technology underneath. When fraud and AML run on separate systems, a signal one team sees is invisible to the other, even though fraud proceeds often become a laundering problem.
The FRAML approach done right is shared data, separate workflows. Both teams decide from the same entity view, so a mule account flagged by fraud strengthens AML monitoring, and the reverse. The institutions that make this shift consistently find that the risk they were missing wasn't invisible. It was visible to one team and not the other.
The table below shows what these shifts look like in practice, capability by capability.
With those shifts in mind, here is how we evaluated the platforms on this list.
How We Evaluated These Platforms
We ranked each platform against seven criteria, weighted toward the outcome that matters most in real-time decisioning: making the right call fast enough to act before money moves.
- Decision speed and scale. Latency in milliseconds and throughput in queries per second, held at peak volume rather than on average.
- Detection depth. Rules plus supervised machine learning, and unsupervised machine learning to catch attacks no one has labeled yet.
- Unified fraud and AML. One data and decision layer, so fraud signals inform AML monitoring and the reverse.
- Agility. No-code rules and backtesting, so strategy teams can respond to a new attack in minutes.
- Explainability and governance. A reason code and audit trail behind every decision.
- Investigation. Flagged activity flows straight into connected case management.
- Proven outcomes. Published, quantified customer results.
Here is how the ten platforms compare at a glance.
The 10 Best Real-Time Decisioning Platforms in 2026
Each platform below is strong in its own lane. The entries explain where each one fits best and what to test during an evaluation.
1. DataVisor — Best overall for unified real-time fraud and AML decisioning
DataVisor runs fraud and AML on one decision layer and returns decisions in 30 ms at 15,000 QPS. It pairs rules with supervised and unsupervised machine learning, so teams catch known fraud patterns and coordinated attacks that have never been labeled.
Key capabilities:
- Real-time data orchestration and feature engineering at decision time
- Unsupervised and supervised machine learning, rules and knowledge graphs in one flow
- No-code rules, features and workflow automation with backtesting
- Integrated investigations and case management, with our AI agent, Vera, assisting analysts
- White-labeled multi-tenancy for processors and platforms serving many clients
Best for: banks, credit unions, fintechs and payment processors running fraud and AML together at high volume.
Considerations: the platform delivers the most value when fraud and AML teams share data; programs that only need a single-channel rules engine may not use its full depth.
2. Feedzai — Best for large banks focused on transaction fraud
Feedzai is an established risk platform with a strong presence among large banks and payment providers. It applies machine learning to transaction monitoring and offers a broad set of fraud and financial crime modules.
Best for: large banks with mature fraud teams prioritizing transaction fraud.
Considerations: evaluate how fraud and AML data are shared across modules and how quickly your team can change strategies on its own.
3. FICO (Falcon Fraud Manager / FICO Platform) — Best for card-issuer fraud programs
FICO Falcon has long been used for card fraud detection, backed by consortium data and predictive models. The broader FICO Platform adds decision management capabilities.
Best for: card issuers that want consortium-based models for card fraud.
Considerations: assess coverage beyond cards, such as instant payments and AML, and how much tuning depends on vendor services.
4. Featurespace (a Visa company) — Best for adaptive behavioral analytics on payments
Featurespace, acquired by Visa in December 2024, is known for adaptive behavioral analytics that model individual customer behavior to spot anomalies in payments.
Best for: payment-focused institutions that want behavioral profiling at the core of detection.
Considerations: review the product roadmap and integration path now that it is part of Visa.
5. NICE Actimize — Best for enterprise AML and compliance programs
NICE Actimize offers a broad financial crime and compliance suite, with deep coverage of AML, sanctions screening and surveillance for large institutions.
Best for: large enterprises where AML and regulatory compliance are the primary drivers.
Considerations: implementation scope and time to change detection logic are worth testing for real-time fraud use cases.
6. Oscilar — Best for fintechs unifying credit, fraud and compliance decisions
Oscilar positions itself as an AI risk decisioning platform covering onboarding, credit, fraud and AML, with no-code workflows and sub-100ms decisioning.
Best for: fintechs and digital lenders that want credit and fraud decisions in one workflow.
Considerations: compare latency and throughput at your peak volume; sub-100ms and 30 ms are meaningfully different budgets on instant-payment rails.
7. Sardine — Best for fintechs and payments companies wanting fraud and AML signals on one platform
Sardine combines device intelligence and behavioral signals with fraud and AML tooling, and is popular with fintechs, crypto platforms and payment companies.
Best for: digital-first companies that rely heavily on device and session signals.
Considerations: confirm fit for bank and credit union regulatory requirements and scale.
8. Provenir — Best for combined credit-risk and fraud decisioning
Provenir is a decisioning platform with roots in credit risk, extending into fraud, compliance and customer management decisions.
Best for: lenders whose priority is credit decisioning, with fraud as a connected use case.
Considerations: assess depth of fraud-specific detection, such as unsupervised ML and investigation workflows.
9. SAS (Fraud Decisioning / Viya) — Best for analytics-heavy, in-house data science teams
SAS offers fraud decisioning on its Viya analytics platform, giving data science teams extensive control over models and analytics.
Best for: institutions with large in-house data science teams that want to build and govern their own models.
Considerations: time to value depends on internal modeling resources.
10. LexisNexis Risk Solutions (ThreatMetrix) — Best for device and digital identity signals feeding decisions
LexisNexis Risk Solutions, through ThreatMetrix, provides digital identity and device intelligence drawn from a large global network.
Best for: teams that want rich identity and device signals to feed an existing decision engine.
Considerations: often used as a signal provider alongside a decisioning platform rather than as the full decision layer.
Why DataVisor Ranks #1
Three things set DataVisor apart on our criteria. Its published benchmark of 30 ms at 15,000 QPS sets a clear bar for speed at scale. Its unsupervised machine learning catches coordinated attacks before anyone has labeled them. And fraud and AML share one decision layer, so a signal found by one team strengthens the other's decisions.
Customers see the results in lower losses, fewer false positives and more capacity for their teams.
Learn more about the Real-Time Decisioning platform. The right platform for your program, though, depends on where you are starting from.
Where Should You Start? Matching Your Priority to the Right Platform
Not every program needs the same thing first. The table below maps the most common priorities fraud and AML leaders bring to us to what they signal and the capability to evaluate first, along with a second capability to address when two needs overlap, which is common.
Start with the priority your team feels most today, rather than the feature that looks most impressive in a demo. Sequence your evaluation by business impact, not by feature count.
Once you know where to start, the next step is testing whether a platform can deliver it in production, not just in a demo.
What to Look for in a Real-Time Decisioning Platform
Most platforms can show a fast, accurate decision in a controlled demo. The question is whether that speed and accuracy are built into the platform's core, or depend on a narrow use case, a small dataset or vendor services to hold together.
The most common mistake in evaluation is buying a feature rather than a capability. A feature is something the platform can do. A capability is something it does reliably, at scale, on your traffic, without your engineering team holding it together. The seven criteria below help you tell the difference.
1. Decision Speed — Held at Peak, Not on Average
A real-time decisioning platform should return decisions in tens of milliseconds and hold that speed when volume spikes. Ask for P95 and P99 latency at your peak queries per second, measured on traffic similar to yours. DataVisor returns decisions in 30 ms at 15,000 QPS.
Red flag: The vendor quotes average latency only, or cannot share throughput figures. Averages hide the slow decisions that happen precisely when traffic, and attack volume, is highest.
2. Detection Depth — Known and Never-Seen Attacks
Rules and supervised models catch fraud that looks like past fraud. Unsupervised machine learning finds coordinated activity no one has labeled yet. The strongest platforms evaluate all three together in a single decision.
Red flag: The platform's machine learning depends entirely on your historical fraud labels. New attack types will pass until enough losses accumulate to train on.
3. Real-Time Context — Enrichment at Decision Time
Accuracy depends on context. Device, behavioral, entity and network signals should be available within the decision window, not added later in investigation.
Red flag: Enrichment runs as a separate call that adds latency, or rich signals are only available in batch. Either way, the decision is made with less information than the platform has.
4. Unified Fraud and AML — Shared Data, Separate Workflows
Fraud and AML teams should keep their own workflows while deciding from the same entity view, so a signal found by one strengthens the other.
Red flag: The vendor describes the platform as unified, but fraud and AML run on separate databases connected by an API. Ask whether there is one entity record shared across both teams.
5. Agility — Strategy Changes in Minutes
Risk teams should be able to build, backtest and deploy a new rule or feature themselves, without an engineering ticket.
Red flag: Rule changes are measured in sprint cycles or require vendor professional services. Every week of delay is a week a new attack pattern runs unchecked.
6. Explainability and Governance — A Reason Behind Every Decision
Every approve, decline or step-up should carry a reason code and an audit trail that shows which data and logic drove it, so model risk teams and examiners can follow the decision.
Red flag: The platform surfaces a risk score but cannot show why a single decision was made, or the audit trail is rebuilt after the fact.
7. Connected Investigation and Proven Outcomes
Flagged activity should flow straight into case management, with the decision context attached and an AI agent assisting analysts. And the vendor should be able to show published, quantified results from customers like you.
Red flag: Flagged decisions are exported to a separate case tool, or the vendor cannot name reference customers with measured outcomes.
Find out where your program stands. The Real-Time Decisioning Playbook walks through the four stages of decisioning maturity, the KPIs that build the business case, and a readiness checklist you can complete in minutes.
Frequently Asked Questions
What is the best real-time decisioning platform for fraud and AML?
The leading real-time decisioning platforms for fraud and AML in 2026 include DataVisor, Feedzai, FICO, Featurespace, NICE Actimize, Oscilar, Sardine, Provenir, SAS and LexisNexis Risk Solutions.
DataVisor ranks first for unified fraud and AML decisioning. It returns decisions in 30 ms at 15,000 queries per second and combines rules with supervised and unsupervised machine learning on one decision layer.
The best fit for your program depends on your payment rails, your volume and how closely your fraud and AML teams work together. The criteria in this guide apply to any vendor.
What is real-time decisioning?
Real-time decisioning is the ability to evaluate an event, such as a payment, login or account application, and return an approve, decline, step-up or review decision in milliseconds, before the transaction completes.
It combines data enrichment, rules and machine learning in one automated flow, and it feeds outcomes back into models and rules so decisions improve over time.
What is the difference between real-time and batch decisioning?
Real-time decisioning acts while the transaction is in flight, so risky activity is stopped before money moves and good customers are approved instantly. Batch decisioning reviews activity after it happens, on a schedule, so losses are found rather than prevented.
Batch analysis still has a role in reporting and model training. But on instant payment rails like RTP and FedNow, where settlement is final, the decision itself has to be real time.
How fast does real-time fraud decisioning need to be?
Most payment and onboarding flows give fraud decisioning a budget of tens of milliseconds. The platform must hold that speed at peak volume, not just on average.
Look for published P95 and P99 latency at thousands of queries per second, measured on traffic like yours. DataVisor returns decisions in 30 ms at 15,000 QPS.
What is FRAML, and why unify fraud and AML decisioning?
FRAML is the practice of managing fraud and anti-money laundering on shared data and intelligence. Fraud proceeds often become a laundering problem, and the networks behind them frequently overlap.
Unified decisioning helps teams spot connected risk sooner, cut duplicate alerts and reduce false positives. Done right, it means shared data and separate workflows: each team keeps its own process and obligations while deciding from the same entity view.
Can real-time decisioning reduce false positives?
Yes. Richer context at decision time, such as device, behavioral and entity signals, helps models tell good customers from fraud more precisely.
NASA Federal Credit Union cut AML false positives by 42% while retaining 100% of true positives after moving to DataVisor's unified fraud and AML platform.
Make Every Decision in Real Time
The programs pulling ahead in 2026 approve good customers instantly, stop fraud before money moves, and give fraud and AML teams one shared view of risk. The right platform makes that possible at the speed your payment rails demand.
Get the Real-Time Decisioning Playbook to find your maturity stage and the next moves for your program.
See DataVisor's 30 ms decisioning on your own traffic. Request a demo.
