AI Transaction Monitoring: What Helix + Sardine Signals

Fraud doesn’t wait for your batch jobs. It hits at checkout, during payout runs, and right when you’re scaling into new corridors—usually on a Friday afternoon when your risk team is already stretched.

That’s why the news that Helix selected Sardine for real-time transaction monitoring, fraud interdiction, and BSA/AML matters beyond the press-release headline. It’s a clean example of where the market is going in our AI in Payments & Fintech Infrastructure series: risk and compliance are becoming real-time infrastructure decisions, not quarterly “risk tool” projects.

Here’s the stance I’ll take: most fintechs still treat fraud detection and AML compliance as two parallel tracks—different vendors, different rules, different teams, different “truth.” That separation is expensive, slow, and increasingly hard to defend with regulators and bank partners. Partnerships like Helix + Sardine point to a better operating model.

Real-time transaction monitoring is now table stakes

Real-time transaction monitoring isn’t a luxury anymore; it’s the only way to keep fraud losses and compliance exposure from compounding at scale. If your stack can’t score activity as it happens, you’re effectively choosing to react after money has moved.

Two trends are colliding in late 2025:

1. Instant rails and faster payouts are normal. Customers expect speed; fraudsters love speed.
2. Regulatory expectations keep rising, especially around demonstrating effective controls—not just having policies.

When a platform like Helix—whose business depends on reliability and trust—chooses a specialist like Sardine, it signals that embedded risk capabilities are becoming part of what modern fintech infrastructure providers must offer by default.

Why “real-time” changes the math

A lot of teams say they do real-time monitoring, but what they really mean is “near-real-time alerts.” True real-time implies you can do all three of these before authorization, settlement, or payout completes:

• Detect suspicious patterns (behavioral anomalies, device signals, network patterns)
• Decide with consistent policy (risk thresholds, compliance flags, exception handling)
• Interdict (step up, hold, block, or route for review)

That last step—interdiction—is where value is created. Alerts alone don’t stop loss.

Fraud interdiction isn’t magic—it’s disciplined machine learning

Fraud interdiction works when models and controls are operationalized inside the transaction path. Not bolted on afterward.

Machine learning earns its keep in payments when it can combine signals that rules engines struggle with, such as:

• Identity and device consistency over time (account age, device fingerprint stability, velocity across identities)
• Behavioral drift (a merchant or user suddenly changing typical patterns)
• Graph relationships (shared devices, emails, bank accounts, or payment instruments across “different” users)
• Contextual risk (payout destination risk, corridor risk, time-of-day anomalies)

Rules are still useful—especially for clear policy constraints—but rules alone create two predictable problems:

1. Fraud adapts faster than rules. Static thresholds get learned and bypassed.
2. False positives pile up. You end up blocking good customers in your busiest periods (holiday peaks, year-end bonus payouts, seasonal promotions).

The practical goal is straightforward: reduce fraud and chargebacks without turning your onboarding and payments funnel into a friction trap. AI helps you get there, but only if it’s implemented with clean decisioning, feedback loops, and measurable outcomes.

A simple operating model that actually works

If you’re evaluating AI fraud detection or AI transaction monitoring, use this model:

1. Score every event (auth, refund, payout, profile change) with a consistent risk score
2. Attach reasons (top contributing factors) so risk ops can act quickly
3. Choose a control: allow, deny, step-up, hold, or manual review
4. Close the loop with outcomes: confirmed fraud, chargeback, SAR filed, false positive

A risk model that can’t learn from outcomes turns into a fancy rules engine.

BSA/AML and fraud should share a brain (even if teams don’t)

BSA/AML compliance is often treated as “the reporting side” of the house, while fraud is “the loss side.” In reality, the behaviors overlap more than most org charts admit.

A modern compliance program needs to detect and document patterns like:

• Rapid movement of funds through accounts (layering)
• Unusual inbound/outbound flows for a profile
• Structuring behavior (transactions broken into thresholds)
• Mule activity and synthetic identity behavior

Fraud tooling can detect much of the same behavior—especially when it’s powered by entity resolution and behavioral analytics. The win is when one monitoring layer supports:

• Fraud interdiction controls (prevent loss in the moment)
• BSA/AML investigations (document what happened and why)
• Audit-ready evidence (decisions, reasons, overrides, outcomes)

Helix’s selection of Sardine explicitly spanning real-time monitoring, fraud interdiction, and BSA/AML points to this convergence: one platform approach to risk, multiple stakeholders served.

“Compliance-ready” is different from “compliance-themed”

A lot of fintechs buy a tool that looks compliant—dashboards, case management, templated reports—and still fail partner bank reviews because they can’t answer simple questions:

• What controls stopped suspicious transactions before funds moved?
• What percentage of alerts were true positives vs noise?
• How long do investigations take end-to-end?
• Can you show model governance (changes, approvals, drift monitoring)?

AI-driven analytics only helps if your program can prove it’s effective.

What this partnership says about modern fintech stacks

The subtext of Helix + Sardine is architectural: fintech infrastructure is becoming modular, and risk is increasingly purchased as an embedded capability rather than built as a bespoke internal system.

That’s not “outsourcing risk.” It’s recognizing that the hard part isn’t writing a model—it’s maintaining a full risk production system:

• Real-time scoring at scale
• Latency budgets that don’t harm authorization rates
• Model monitoring and drift detection
• Investigator tooling and workflow
• Explainability and audit trails
• Policy controls by product, corridor, or customer segment

If your core platform is meant to help other fintechs ship products quickly, your risk layer has to be just as production-grade as your ledgering, routing, or card processing.

A checklist for evaluating AI-powered transaction monitoring

If you’re a fintech, sponsor bank program, or platform provider evaluating vendors, I’d pressure-test these areas before you sign:

1. Interdiction controls: Can you block/hold/step-up in real time, or only alert?
2. Data coverage: Can it ingest auth events, payouts, refunds, disputes, KYC/KYB updates, device signals, and CRM notes?
3. Entity resolution: Does it link identities across devices, bank accounts, and instruments reliably?
4. Case management: Can investigators move fast with clear reason codes and evidence?
5. Model governance: Do you get versioning, change logs, drift monitoring, and approval workflows?
6. Tuning without chaos: Can you adjust thresholds by segment without breaking everything?
7. Outcomes loop: Can you feed back chargebacks, returns, SAR outcomes, and confirmed fraud?
8. Operational metrics: Are you measuring alert-to-case rate, case closure time, false positive rate, and prevented loss?

If a vendor can’t answer most of these crisply, the “AI” label won’t save the program.

Implementation: how to get value in the first 60–90 days

Most teams don’t fail because the models are bad. They fail because they try to implement everything at once.

Here’s the phased approach I’ve seen work best for AI fraud detection and AML monitoring:

Phase 1: Observe (Weeks 1–3)

Start in “shadow mode” where you score traffic and generate alerts without blocking.

• Validate data feeds and event consistency
• Establish baseline metrics (current fraud rate, chargebacks, manual review volume)
• Identify the top 3–5 fraud patterns by product line

Phase 2: Interdict the obvious (Weeks 4–8)

Turn on real interdiction for the highest-confidence patterns.

• Hold or step-up on the riskiest payout destinations
• Deny high-risk device/account combinations
• Rate-limit velocity attacks

This is where prevented loss shows up quickly.

Phase 3: Tune for growth (Weeks 9–12)

Now focus on reducing false positives and friction.

• Segment policies (new users vs tenured users, high-risk corridors vs low-risk)
• Optimize review queues so humans spend time on the right cases
• Add feedback loops from disputes, returns, and investigator outcomes

The best risk teams treat models like products: monitor them, iterate, and measure.

People also ask: practical questions fintech teams bring up

Do we really need AI for transaction monitoring?

You need automation that adapts. If your fraud patterns are stable and your volume is low, rules can be enough. If you’re scaling, adding products, or operating across multiple rails, AI-based monitoring becomes the only sustainable way to keep noise down while catching novel attacks.

Will AI increase false positives?

Badly implemented AI will. Good AI lowers false positives because it can incorporate more context than rigid thresholds. The deciding factor is whether you run a feedback loop and measure precision/recall (or equivalent operational metrics like review hit-rate).

How do we reconcile fraud actions with AML obligations?

You reconcile them through consistent decisioning and documentation. Blocking a transaction for suspected fraud isn’t the same as escalating for AML, but the underlying evidence can overlap. Your system should capture decision reasons, investigator notes, and outcomes so both teams work from the same record.

Where this is heading in 2026: risk becomes a product feature

AI in payments isn’t just about better detection. It’s about making secure throughput a competitive advantage: higher approval rates for good users, lower loss rates, and fewer ugly surprises in partner bank exams.

Helix choosing Sardine for real-time transaction monitoring, fraud interdiction, and BSA/AML is a strong signal that platforms want a unified risk layer that scales with their customers. If you’re building on top of fintech infrastructure—or providing it—this is the bar you’re going to be measured against.

If you’re planning your 2026 roadmap, here’s the question I’d put on the first slide: What would it take for our fraud and AML controls to act in real time, with audit-ready evidence, without slowing down growth?