AI Fraud Monitoring in Payments: Helix + Sardine

A lot of fintech teams still treat fraud and compliance as two separate queues: fraud sits with risk, BSA/AML sits with compliance, and the payments layer just ships transactions as fast as possible. That split worked when volumes were lower, rails were slower, and fraud patterns changed over months—not minutes.

But the last few years have forced a different operating model. Real-time payments, instant card funding, and always-on digital onboarding mean your risk decisioning has to happen at transaction speed. When it doesn’t, you get the worst of both worlds: higher losses and more false positives.

That’s why partnerships like Helix selecting Sardine for real-time transaction monitoring, fraud interdiction, and BSA/AML are worth paying attention to in the “AI in Payments & Fintech Infrastructure” series. Not because “vendor X chose vendor Y,” but because it signals a broader infrastructure shift: transaction security is moving into the core payments pipeline, powered by AI-driven signals and automation.

Real-time monitoring is now a payments infrastructure requirement

Real-time transaction monitoring isn’t a “nice-to-have” feature you bolt on after launch. It’s a core capability that determines whether your platform can safely scale.

Here’s the direct reason: modern fraud is iterative. A bad actor runs small tests, watches what passes, then changes tactics within hours. If your detection loop runs in batch (or relies on next-day case review), you’re effectively training fraudsters on what works.

What “real-time” actually means (and why teams get it wrong)

In practice, real-time monitoring means you can do three things before money moves or settles:

• Score the transaction using behavioral, device, identity, and network signals
• Interdict (block, step-up, or hold) based on dynamic risk
• Explain and log the decision for auditability and downstream investigations

Most companies get the first step halfway right (basic rules). The second step is where systems break: you can’t interdict reliably unless your fraud engine is integrated into the transaction path and can respond within tight latency budgets.

And the third step—explainability and recordkeeping—is where many “fast” systems fail compliance review. Real-time without defensible logs isn’t a win; it’s technical debt.

Snippet-worthy truth: If your fraud controls can’t act in-line with payments, you don’t have real-time monitoring—you have real-time alerts.

Why fraud interdiction beats fraud detection

Detection tells you something bad happened. Interdiction prevents it.

That shift matters because the cost curve is brutal: a prevented transaction might cost you a bit of conversion, but a successful fraud event often triggers losses, dispute ops, potential program scrutiny, and long-tail reputational damage.

Interdiction decisions you need at the moment of truth

In payment flows, “interdiction” isn’t only “decline.” Strong systems support a set of controls that map to user experience and risk appetite:

1. Hard block (clear fraud indicators)
2. Soft block / step-up (request additional verification, re-authentication, or manual review)
3. Hold and investigate (especially useful for ACH or internal transfers)
4. Allow but tag (monitor and tighten limits or require step-up on the next attempt)

A partnership positioned around fraud interdiction signals a more mature approach: the goal isn’t just to catch fraudsters after they’ve been paid—it’s to stop them before funds leave your ecosystem.

Where AI helps (and where it doesn’t)

AI performs best when it’s used to combine weak signals into a strong decision. In transaction risk, those weak signals might include:

• Device and session anomalies
• Velocity patterns (bursts across accounts, merchants, IP ranges)
• Identity mismatch patterns (name/address/email/phone inconsistencies)
• Behavioral biometrics signals (interaction timing, navigation patterns)
• Network relationships (shared devices, shared payout endpoints)

AI does not magically fix:

• Missing data from fragmented systems
• Poorly defined escalation workflows
• A product team that refuses to add step-up paths when risk calls for it

The highest ROI I’ve seen comes from pairing AI scoring with clear, opinionated decision policies and a case management loop that improves outcomes over time.

BSA/AML and fraud are converging—your stack should too

BSA/AML isn’t just “compliance paperwork.” It’s operationally tied to fraud because the same infrastructure that moves money also enables abuse: mule networks, synthetic identities, account takeovers, and layering behaviors.

When Helix selects a platform that covers BSA/AML plus transaction monitoring, it reflects a direction many payment programs are already taking: consolidate risk signals and actions in one place so you’re not reconciling two versions of the truth.

The practical overlap: the same events feed both mandates

A few examples of shared signals that matter to both fraud and AML operations:

• Rapid onboarding followed by high-velocity transfers
• Unusual counterparty patterns (many-to-one, one-to-many)
• Sudden changes in geography/device paired with payout activity
• Structuring-like behavior (repeated transactions just under internal thresholds)

A unified approach reduces gaps like:

• Fraud declines that never feed AML typologies
• AML alerts that arrive too late to prevent a loss event
• Separate teams duplicating investigations and documentation

Auditability is a product feature, not a compliance afterthought

If you’re building payments infrastructure in 2025, audit-ready logging is part of the product. For BSA/AML readiness, you need to show:

• What signals were used
• What policy was applied
• What action was taken
• Who reviewed it (if manual)
• What evidence supports disposition

AI-driven monitoring only helps if it’s paired with traceable decision records. Regulators and bank partners don’t want a black box; they want a system that makes consistent decisions and can explain them.

What this partnership signals for fintech infrastructure teams

This isn’t about one press release (and in this case, the original article content wasn’t accessible due to a security gate). The bigger story is the architectural pattern: platforms are embedding AI risk controls directly into the transaction layer.

Here’s what infrastructure leaders should take away.

1) Latency budgets are becoming risk budgets

If your fraud decision has 300–800ms to respond (common in real-time experiences), every additional data call matters. You’ll need to decide:

• Which signals are computed in real time vs. precomputed
• What your fallback decision is when enrichment services time out
• How to degrade gracefully without opening fraud loopholes

A strong architecture treats risk like an SLO-driven service: measured, monitored, and performance-tested.

2) “Single view of risk” reduces false positives

False positives are expensive: they hurt conversion, create support tickets, and train good customers to abandon your product.

Combining fraud + compliance signals in one monitoring layer makes it easier to:

• Avoid duplicate holds
• Apply consistent policies across rails (card, ACH, RTP)
• Create customer-friendly step-ups instead of blanket declines

The win isn’t only fewer losses—it’s better approval rates with the same (or lower) risk.

3) Case operations is where most programs leak money

Even with real-time interdiction, you’ll still have gray-area events that require investigation. Teams underestimate how much value is lost in:

• Alert floods without prioritization
• Slow disposition workflows
• Inconsistent investigator notes
• Limited feedback loops back into models and rules

If you’re evaluating AI transaction monitoring, ask how it improves operations, not just detection.

A practical checklist: what to ask when buying AI transaction monitoring

If you’re a fintech platform, sponsor bank, or program manager evaluating an AI-driven transaction monitoring and BSA/AML solution, these questions surface real capabilities fast.

Data and coverage

• Which rails are covered (card authorizations, ACH, instant payments, internal ledger transfers)?
• Can it monitor both on-us movements (internal ledger) and off-us transfers?
• How does it handle identity, device, and behavioral data ingestion?

Decisioning and interdiction

• Can you block/hold/step-up in-line without building custom plumbing?
• Can you apply different policies by customer segment, product, or risk tier?
• What’s the typical end-to-end decision latency under load?

BSA/AML readiness

• How are typologies defined and updated?
• Can you generate consistent audit trails for alerts, cases, and dispositions?
• How does it support SAR workflows and evidence collection (even if your team files outside the tool)?

Model governance and explainability

• Can investigators see the main drivers behind a score?
• How do you tune thresholds without causing approval-rate whiplash?
• What monitoring exists for drift, seasonality, and attack adaptation?

Opinion: If a vendor can’t explain decisions in plain language to your investigators, you’ll pay for it later—in disputes, partner reviews, and rework.

People also ask: quick, practical answers

Is AI transaction monitoring better than rules-based fraud detection?

AI is better at combining many weak signals and adapting faster, but rules still matter for explicit policy controls (known bad BIN ranges, blocked geos, velocity caps). The best setups use both.

How does real-time monitoring support BSA/AML compliance?

It shortens the window between suspicious behavior and action. That means fewer losses, faster investigations, and better documentation because evidence is captured as events occur.

What’s the biggest implementation mistake?

Treating monitoring as an “alerts dashboard” instead of wiring it into the transaction path. If you can’t interdict, you’re mostly documenting losses.

Where this fits in the “AI in Payments & Fintech Infrastructure” story

Across this series, the theme is consistent: AI is becoming part of the plumbing, not a layer you check after the fact. Fraud prevention, transaction routing, and compliance are converging into a shared decisioning fabric that sits next to the ledger and the payment rails.

Partnerships like Helix and Sardine highlight what modern programs are optimizing for: real-time controls, unified risk signals, and operational workflows that scale with volume.

If you’re planning your 2026 roadmap, here’s the forward-looking question worth debating internally: Which risk decisions must happen before funds move—and are you architected to make those decisions in time?