Real-Time Fraud Monitoring in Fintech: A Helix Playbook

Fraud doesn’t wait for your batch jobs.

Every December, payment volumes spike—gift cards, instant payouts, last-minute shopping, end-of-year billing runs. That’s great for revenue. It’s also prime time for account takeover, synthetic identities, mule activity, and “friendly fraud” that turns into chargebacks in January. The fintechs that struggle aren’t the ones without smart people. They’re the ones still trying to manage modern attack patterns with delayed signals.

That’s why the news that Helix selected Sardine for real-time transaction monitoring, fraud interdiction, and BSA/AML is more than a vendor update. Read it as a case study in where AI in payments and fintech infrastructure is heading: continuous, real-time decisioning that unifies fraud and compliance workflows instead of forcing teams to juggle disconnected tools.

Why real-time transaction monitoring is becoming the default

Real-time monitoring is replacing “review later” because payment risk now unfolds in minutes, not days. When funds move via instant rails, card push-to-debit, RTP-style flows, or rapid ACH, the window to stop loss is tiny.

Historically, many programs leaned on:

• Post-transaction reviews
• Static rules (velocity checks, blocklists)
• Periodic AML tuning
• Manual case queues that grow faster than headcount

That model breaks when adversaries automate. Fraud rings test your limits, probe your controls, and adapt quickly—especially during seasonal peaks like December.

Real-time monitoring changes the operational posture:

• You block or step-up risky activity before funds leave.
• You see patterns across accounts and channels while they’re forming.
• You reduce downstream costs (chargebacks, recovery, investigations).

Here’s the stance I’ll defend: if your fraud stack can’t respond in real time, you’re not “behind”—you’re operating a different era’s control system.

Interdiction vs detection: the difference that matters

A lot of platforms “detect” fraud. Fewer can interdict it.

• Detection means you identify suspicious behavior.
• Interdiction means you can take an action in the moment—deny, hold, step-up verification, reroute, or require additional authentication.

That’s why vendor selection in this category isn’t just about accuracy metrics. It’s about whether the tooling sits close enough to the money movement to act fast, and whether it gives teams confidence to automate actions without triggering customer pain.

The Helix–Sardine signal: fraud and BSA/AML are converging

The most important trend hiding in plain sight is the convergence of fraud controls and BSA/AML controls. Helix’s selection of a solution that explicitly covers both fraud interdiction and BSA/AML reflects what many operators already feel:

• Fraud patterns often look like AML typologies (mules, structuring-like behavior, layering via multiple accounts).
• AML investigations often depend on fraud-grade signals (device, session, behavioral anomalies).
• Teams are tired of duplicative casework and inconsistent decisions across tools.

When companies unify monitoring and workflows, they typically see three practical benefits:

1. Fewer conflicting decisions (fraud tool approves what AML tool flags).
2. Faster investigations because analysts work from a shared timeline.
3. Cleaner audit narratives because decisions are explainable end-to-end.

A useful rule: if fraud and compliance don’t share the same “customer story,” you’ll pay for it—in loss, in ops cost, or in regulator conversations.

What “AI-driven monitoring” should mean (and what it shouldn’t)

AI in payments gets marketed as magic. Most companies get this wrong by focusing on the model label instead of the outcomes.

AI-driven transaction monitoring should mean:

• Scores that update as new signals arrive (not once per day)
• Models that learn from confirmed outcomes and analyst feedback
• Entity resolution across identities, devices, funding sources, and beneficiaries
• Decisioning that supports action policies (deny/hold/step-up) with confidence controls

It should not mean:

• A black box that can’t explain why it flagged a payment
• A single score that ignores context (customer history, channel, device, beneficiary risk)
• A tool that improves “alerts per day” instead of reducing true loss and case time

What changes when monitoring is real-time (operationally)

Real-time monitoring isn’t a dashboard upgrade; it’s an operating model upgrade. If you adopt it and keep the same processes, you’ll underperform and blame the tool.

1) You shift from queues to policies

Batch-era programs rely on analysts to “catch up” with alerts. Real-time programs rely on policies that define what happens at score thresholds.

A practical pattern:

• Low risk: approve automatically
• Medium risk: step-up (OTP, KBA, doc check, biometric, or additional verification)
• High risk: hold/deny; open a case automatically

The payoff is compounding: fewer manual touches means analysts spend time on genuinely complex investigations.

2) You need strong feedback loops (or your models drift)

Fraud changes weekly. Your controls must too.

Good feedback loops include:

• Confirmed fraud labels (chargebacks, disputes, internal reports)
• Customer-confirmed ATO signals
• Analyst dispositions tied to evidence
• Outcomes from step-up flows (pass/fail)

If you can’t feed outcomes back, your “AI” will slowly become a static scoring tool.

3) You reduce false positives by adding context, not by loosening rules

Many teams fight false positives by turning knobs down. That increases fraud.

A better approach: add context.

Examples of context that reduces false positives without raising loss:

• Device and session continuity (same device, same behavior) vs. sudden anomalies
• Beneficiary reputation and network relationships
• Velocity patterns normalized to customer segment (SMB payroll vs. consumer P2P)
• Time-of-day behavior and channel mixing

How AI strengthens BSA/AML without drowning teams in alerts

The biggest BSA/AML problem at fast-growing fintechs is alert volume that scales faster than the compliance team. That creates backlog risk and inconsistent investigations.

AI helps when it’s used to:

• Prioritize alerts with risk scoring and entity linking
• Reduce duplicates by clustering related activity into a single case
• Surface narratives (timeline views) that analysts can quickly validate
• Standardize decisioning with playbooks and evidence requirements

“People also ask” questions (answered plainly)

Is fraud monitoring the same as AML transaction monitoring? No. Fraud focuses on preventing unauthorized or deceptive transactions and loss. AML focuses on detecting suspicious activity related to money laundering or financial crime. The overlap is large enough that shared signals and workflows now make sense.

Will real-time monitoring increase customer friction? Only if it’s poorly implemented. The goal is targeted friction—step-up checks only when risk is elevated, while low-risk customers see faster approvals.

What should auditors and regulators expect from AI-based monitoring? Clear governance: documented models, explainable factors, tested thresholds, monitoring for bias and drift, and an investigation record that shows why actions were taken.

A practical implementation checklist (what I’d do first)

If you’re evaluating real-time fraud interdiction and BSA/AML tooling, start with the integration and operating model—not the demo. Here’s a checklist I’ve found works.

Define your decision points (where you can actually interdict)

List your flows and where actions are possible:

• Account opening / onboarding
• Login and session behavior
• Pay-in events (card/ACH/wire)
• Pay-out events (instant payouts, ACH, wires)
• Beneficiary creation and edits
• Limits changes

If you can’t intervene at the right points, “real-time” becomes “real-time alerting,” which is less valuable.

Choose your action toolkit (deny/hold/step-up)

Document your allowable actions by product and rail:

• Hard deny vs. soft hold
• Dynamic limits
• Step-up verification options
• Manual review routing rules

Then map actions to risk tiers with measurable objectives.

Build for measurable outcomes (not vanity metrics)

The metrics that matter in a Helix-style deployment are concrete:

• Fraud loss rate (basis points) by product/rail
• Chargeback rate and dispute outcomes
• Time-to-decision (p95) for risk checks
• Alert-to-case conversion rate
• Analyst minutes per case
• Backlog aging for AML queues

If you can’t measure these, you can’t manage them.

Put model governance on rails

AI in fintech infrastructure needs governance that’s boring and consistent:

• Who can change thresholds and when
• Required testing before production changes
• Drift monitoring cadence
• Documentation standards for audit readiness

Boring is good here. Regulators like boring.

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

This Helix–Sardine announcement is one more proof point that payments infrastructure is becoming intelligent infrastructure. Real-time transaction monitoring isn’t a “nice to have” layered on top of payment processing—it’s increasingly part of the processing fabric.

If you’re building or operating a fintech program in 2026 planning season, the question isn’t whether to add AI. It’s whether your fraud detection, transaction monitoring, and BSA/AML compliance can operate at the speed of your payment rails.

If you’re exploring upgrades, start by mapping your highest-loss flows and your highest-backlog compliance queues. Then evaluate whether real-time interdiction plus unified case management would reduce loss and operational drag at the same time.

What would you change if you could confidently stop risky transactions in under 200 milliseconds—without punishing your good customers?