Your Card Flows Are Quietly Becoming Regulatory Systems

Why this matters this week

If you run payments or banking infrastructure, you’re no longer “just” moving money. You’re now:

• A data broker for regulators (KYC/AML, sanctions, travel rules)
• A risk engine for your partners (chargebacks, fraud, credit risk)
• An interface to open banking ecosystems (account data, payment initiation)

The line between “product” and “regulatory infrastructure” has blurred. That’s fine when you’re small; it becomes existential once:

• You cross specific volume or customer-count thresholds (regulators start paying attention)
• You expand geographies (new AML/KYC regimes, PSD2/PSD3, instant payment schemes)
• You onboard partners/merchants who themselves are regulated

Three concrete shifts that are biting teams this year:

1. Regulators expect real-time-ish controls, not nightly batch files.
   AML/KYC, sanctions, and transaction monitoring are moving toward continuous assessment, especially in instant payment rails.

2. Risk & compliance costs are starting to dominate unit economics.
   For many consumer fintechs, fraud/chargebacks + KYC/AML/Ops are 20–40% of variable cost per active user.

3. Open banking is becoming a compliance dependency, not just a feature.
   If you rely on bank connectivity for balance checks, income verification, or account funding, your compliance posture is now partially outsourced.

If your architecture still treats fraud, AML, and KYC as “edge boxes” bolted onto a payments pipeline, you’ll feel this as:

• Slowing feature velocity (“We can’t ship until legal signs off.”)
• Increasing operational burden (manual reviews, recon, exception handling)
• Regulator and partner scrutiny that your systems weren’t designed to withstand

What’s actually changed (not the press release)

A few pragmatic drivers behind the marketing noise in fintech infrastructure, fraud prevention, regtech, and open banking:

1. Real-time expectations

Payments and compliance are converging in time:

• Instant rails (e.g., RTP, FedNow, Faster Payments, PIX equivalents) force:
  • Pre-transaction AML/sanctions checks
  • Real-time fraud scoring
• Card networks are tightening dispute windows and data requirements.
• Regulators are increasingly asking: “Why did you allow this transaction if the risk signals were visible at the time?”

Net effect: The “monitor later and file SARs” model is not sufficient for high-velocity products.

2. Data exhaust is now a regulatory artifact

Your logs and event streams are:

• Evidence of your risk-based approach
• Inputs to explain decisions (why you blocked or allowed)
• Audit trails for suspicious activity reports

Teams that historically treated logs as “for debugging only” are getting hammered in exams and partner reviews.

3. Outsourcing is under stress

A lot of fintech stacks lean heavily on:

• Third-party KYC vendors
• Third-party transaction monitoring / fraud platforms
• Open banking aggregators

The change: regulators and banks now examine your outsourcing risk management. It’s not enough to say “Vendor X handles that.” They ask:

• How do you monitor vendor performance and drift?
• What’s your fallback when the vendor is down or de-risking certain segments?
• How do you prevent your vendor’s model biases from violating your own policies?

4. “Risk as product” is finally a C-suite topic

As rates rose and easy growth money dried up, CFOs and boards started caring about:

• Loss ratios (fraud, chargebacks, write-offs)
• Cost per approval vs. approval rate
• CAC wasted on users who never pass KYC/AML

This is pushing teams to treat fraud, KYC, AML, and dispute handling as core product levers, not compliance overhead.

How it works (simple mental model)

A practical way to think about modern fintech infrastructure:

You are building a stateful graph of “counterparty trust” that must satisfy regulators, networks, and your own P&L, in more-or-less real time.

Break it into four layers:

1. Identity & entitlement layer (KYC/KYB)

Questions:

• Who is this person/business?
• Are we allowed to do business with them?
• What are they allowed to do in our system?

Mechanisms:

• KYC/KYB onboarding:
  • Document verification
  • Database checks (CIP, sanctions, PEPs, watchlists)
  • Business verification (beneficial owners, industry codes, registration)
• Ongoing due diligence:
  • Periodic refreshes
  • Trigger-based reviews (behavior change, volume change, new geos)
• Entitlements:
  • Limits (per transaction, per day, per counterparty)
  • Feature flags (international, high-risk rails, higher limits)

2. Transaction pipeline (payments engine)

Questions:

• What kind of movement is this (card, ACH, instant, wire, wallet)?
• What’s the lifecycle (auth, capture, settlement, refund, chargeback)?
• What constraints apply (cutoff times, scheme rules, limits)?

Mechanisms:

• Orchestration:
  • Routing across PSPs/acquirers/rails
  • Fallback paths
• Ledger:
  • Double-entry or equivalent
  • Clear separation of authorization vs. posting vs. settlement
• Idempotency & reconciliation:
  • External transaction IDs, internal event IDs
  • State machines for each transaction

3. Risk & compliance decision layer (fraud, AML, sanctions, credit)

Questions:

• Should we allow/hold/block this event?
• Do we need to file or escalate anything?
• Does this change the user’s risk profile?

Mechanisms:

• Real-time scoring:
  • Rules + models combining identity, device, behavioral, and transaction data
• Case management:
  • Queues for manual review
  • Investigation tooling (graph views of counterparties, historical activity)
• AML/compliance:
  • Scenario-based monitoring (smurfing, structuring, mule behavior)
  • SAR/STR workflows and audit trails

4. Governance & observability layer

Questions:

• Can we explain decisions?
• Can we tune the system without breaking production?
• Are we compliant when rules or partners change?

Mechanisms:

• Feature flags / config-driven policies
• Versioned rules/models with rollback
• Audit logs of changes + their impact
• Dashboards & alerts for:
  • Approval rates
  • Fraud rates
  • False positive rates
  • Vendor health

This 4-layer model lets you decide:

• What you build vs. buy
• Where to centralize logic vs. keep per-product nuances
• How to phase in more sophistication without rewriting everything

Where teams get burned (failure modes + anti-patterns)

A few anonymised patterns that recur:

1. “Fraud = one box after auth”

Anti-pattern:

• All fraud/AML/sanctions logic is a single service called after the payment is already “committed” in business logic.

Impact:

• You “approve” transactions you can’t easily stop.
• Rollbacks and compensating transactions become complex.
• Regulators ask why you didn’t check earlier.

Better:

• Build a clear phase:
  • Pre-commit risk decision (block/allow/review)
  • Post-commit monitoring (patterns over time)

2. Overfitting to vendor defaults

Pattern:

• Team integrates a KYC/fraud vendor and adopts:
  • Default rulesets
  • Default thresholds
• Nobody owns tuning, drift monitoring, or business-specific logic.

Impact:

• High false positives → user churn, manual review overload
• Blind spots in segments your vendor under-trains on (e.g. certain geos or industries)

Better:

• Assign explicit ownership:
  • One lead for “Risk & Decisioning”
• Put KPIs around:
  • Approval rate
  • Loss rate
  • Manual review rate
• Demand explainability and exportability from vendors.

3. Logging that’s useless for regulators

Pattern:

• Logs focus on technical states (“HTTP 500 from PSP”) but don’t capture:
  • Risk scores
  • Rule/model versions
  • Rationale for decisions
  • Who/what changed the configuration

Impact:

• During audits or partner reviews, you can’t reconstruct:
  • Why you allowed a suspicious series of payments
  • Whether rules were in place at the time

Better:

• Treat decision logs as first-class domain events:
  • risk_decision_made
  • attributes: actor, inputs, score, rules triggered, final outcome, version

4. Siloed AML vs. fraud

Pattern:

• AML team and fraud team run separate systems, with no shared view of:
  • Customer behavior
  • Cross-product flows (e.g., card-to-ACH to crypto)

Impact:

• Money mules and layering schemes slip through.
• Conflicting actions (fraud blocks what AML wants to monitor, or vice versa).

Better:

• Shared entity resolution:
  • One “customer profile” and “counterparty graph”
• A common event bus:
  • AML and fraud subscribe, but share a base timeline of events.

5. Open banking as a blind trust layer

Pattern:

• Team uses an open banking API for:
  • Balance checks for funding
  • Account holder verification
• Assumes provider’s risk/AML controls are enough.

Impact:

• When a bank or aggregator changes data quality or coverage, your risk logic quietly degrades.
• No monitoring of mismatch between reported and observed behavior.

Better:

• Treat open banking data as “evidence,” not truth:
  • Cross-check with your own behavioral and transactional data.
• Monitor per-connector data quality:
  • Missing fields, stale balances, error rates.

Practical playbook (what to do in the next 7 days)

You can’t rebuild your payments and risk platform in a week, but you can de-risk the next 12–18 months.

1. Draw the actual flow of a $1 transaction

Do this with engineering + risk + ops in a 60–90 minute working session:

• From user intent → authorization → ledger move → settlement → possible refund/chargeback.
• Mark:
  • Where risk/AML/KYC decisions occur
  • Which services/vendors are involved
  • Where manual work enters (ops queues, spreadsheets)

Output: One diagram with clear entry points for risk & compliance logic.

2. Identify your “irreversible points”

For each rail:

• Where is it hard or impossible to claw back funds?
• Where do scheme rules make you liable?

Verify that:

• You have mandatory risk checks before those points.
• There are metrics on what’s being allowed at those points.

3. Baseline three metrics

You need a minimum viable risk dashboard:

• Approval rate (by product, rail, segment)
• Fraud/loss rate as % of processed volume
• Manual review rate (and SLA