Your Fintech Stack Is Now a Regulated System, Whether You Like It or Not

Table of Contents

Why this matters this week

Regulators and payment networks have quietly shifted posture over the last 6–12 months: they now treat your payments, KYC, and fraud stack as part of their risk surface, not just yours.

That shows up in three concrete ways:

Scheme and bank partners are pushing risk downstream
- Higher chargeback scrutiny, rolling reserves, and instant “program pause” levers.
- More detailed demands around transaction monitoring and merchant underwriting.
Regulators are enforcing on controls, not just outcomes
- It’s no longer enough that your fraud rate is low; you’re expected to have explainable models, audit trails, and governance for AML/KYC and sanctions.
- “We use a third‑party provider” is not a defense when that provider fails.
Open banking and API-first banking are creating new attack surfaces
- Account-to-account payments, embedded finance, and BaaS relationships mean your “app” is now part of a chain of regulated entities.
- Fraud and AML risk propagates across this chain; weakest link loses.

If you run payments, risk, or compliance engineering, the takeaway is simple: your infrastructure now is compliance infrastructure. You either design it that way, or you discover it during an incident call with your sponsor bank and a regulator on BCC.

What’s actually changed (not the press release)

Under the noise, several structural changes are forcing different technical decisions in fintech infrastructure:

Data expectations have flipped: “give us raw or else”
- Scheme and regulatory reviews increasingly ask for:
  - Raw transaction logs (not just aggregates).
  - Historical KYC decisions with full evidence.
  - Model feature inputs and decisions over time.
- This kills a whole class of “black-box vendor + thin logs” architectures.
Risk decisions are now time-bounded by regulation
- Instant payments, RTP rails, and open banking APIs mean:
  - Fraud / AML checks need to complete in subseconds, not minutes.
  - You can’t batch-post review queues overnight without accumulating uncapped liability.
- This forces:
  - In-memory feature stores.
  - Pre-computed risk scores.
  - Strong backpressure / fail-closed behavior under load.
BaaS and embedded finance are under real stress
- Sponsor banks are being questioned about:
  - Their monitoring of fintech partners.
  - “Program-by-program” risk segmentation.
- Consequences:
  - More frequent audits of your transaction monitoring.
  - Per-partner risk thresholds and dynamic controls (e.g., rolling limits).
Regtech is becoming “build + buy”, not “buy and forget”
- Off-the-shelf KYC/AML tools help with:
  - ID document verification.
  - Sanctions and PEP screening.
  - Basic rules engines.
- But:
  - You’re expected to tune scenarios to your product and geography.
  - You remain accountable for the gaps between the vendor’s model and your risk profile.
- This demands integration patterns that keep you in the loop:
  - First-party decisioning layer on top of vendor outputs.
  - Your own monitoring around their APIs and SLAs.
Attackers have automated the boring parts
- Common, observable patterns:
  - Large-scale identity fraud using synthetic identities across multiple neobanks.
  - Refund and dispute abuse coordinated across merchant platforms.
  - Faster testing of card BINs, stolen tokens, and compromised bank credentials.
- Defensive implication:
  - Static rules and single-provider checks degrade quickly.
  - Cross-product, cross-rail correlation is now table stakes.

None of this is in the marketing decks, but it absolutely shows up in implementation details: how you log, how you roll out models, how you wire payments to risk decisions.

How it works (simple mental model)

A workable mental model for modern fintech infrastructure:

You’re running a regulated, distributed system with three feedback loops: money, identity, and behavior.

Break it into five layers:

Perimeter: Identity & KYC
- Inputs: user-provided data, device fingerprints, documents, bank / card tokens.
- Outputs: “who this appears to be” + risk score + evidence bundle.
- Key decisions:
  - Onboard vs. reject vs. step-up (e.g., document vs. biometric).
  - Limits and capabilities at onboarding time.
Transaction Engine: Payments & Money Movement
- Inputs: payment requests, account states, network connectivity.
- Outputs: ledger updates, scheme messages, settlement instructions.
- Key decisions:
  - Which rails to use (card, ACH, RTP, open banking, internal transfer).
  - Synchronous vs. asynchronous execution.
  - Fail-open vs. fail-closed on downstream issues.
Risk & Fraud: Behavior Over Time
- Inputs: transactions, session data, devices, historical patterns.
- Outputs: approvals/declines, velocity thresholds, manual review queues.
- Key mechanisms:
  - Feature computation (per-user, per-device, per-merchant).
  - Rules engine + ML models + heuristics.
  - Feedback loop from confirmed fraud / chargebacks.
AML / Compliance: Pattern Detection & Reporting
- Inputs: customer profiles, transaction streams, external watchlists.
- Outputs: alerts, case files, SAR/STR reports, blocklists.
- Key mechanisms:
  - Scenarios (e.g., structuring, rapid movement, sanctions matches).
  - Case management workflows.
  - Retention and audit trails.
Governance & Observability: Auditability as a Feature
- Inputs: logs, metrics, config changes, model versions, playbooks.
- Outputs: dashboards, internal controls, external audit packages.
- Key mechanisms:
  - Versioned decision policies (rules+models+thresholds).
  - Reproducibility of historical decisions.
  - End-to-end traceability from user action to regulator-facing narrative.

The critical interactions:

Identity → Risk
Poor KYC signals force your fraud system to act as a backstop, usually late and expensive.
Risk → Payments
Risk decisions that aren’t in the critical path let bad transactions settle.
Risk/AML → Governance
If you can’t explain why something was allowed or blocked, you have a governance problem, not just a modeling problem.

Design as if an auditor will ask you: “Show me exactly why this payment at 13:42 on March 3rd was allowed, and what you knew about this customer at the time.”

Because they increasingly do.

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

These are recurring patterns across fintech and payment teams:

Black-box risk vendors with no decision traceability
- Symptom:
  - “VendorX said ‘approve=0.73’, we don’t know why.”
- Failure:
  - Auditors demand explanation → you have nothing credible.
  - Vendor changes model → your risk profile shifts overnight.
- Better:
  - Vendor score as one feature in your decisioning.
  - Store inputs and outputs; log model versions.
  - Build your own rules and overrides layer on top.
Risk decisions not truly in the payment path
- Symptom:
  - “We run fraud checks post-authorization but before settlement.”
- Failure:
  - Real-time rails (RTP, P2P, A2A) settle before you can react.
- Better:
  - Hard requirement: no irreversible money movement without a risk decision.
  - For rails with strict SLAs, pre-compute risk or use tiered limits.
Mixing business logic and compliance logic
- Symptom:
  - “Marketing wants higher limits for this campaign; we changed the same rules config that AML relies on.”
- Failure:
  - One set of changes affects unrelated scenarios.
  - Difficult to prove stable controls to auditors.
- Better:
  - Separate configs / policies for:
    - Product/business rules.
    - Fraud rules.
    - AML/sanctions rules.
  - Independent review/approval flows.
No robust case management
- Symptom:
  - Alerts triaged via spreadsheets, tickets, email.
- Failure:
  - Missed SLAs on suspicious activity reports.
  - Inconsistent handling of similar alerts.
- Better:
  - Central case system with:
    - Role-based access.
    - Evidence attachments.
    - Audit log of actions and decisions.
    - Metrics: age of alerts, backlog, time-to-decision.
Underestimating data retention and privacy interplay
- Symptom:
  - “We keep everything forever, it’s useful for models.”
- Failure:
  - Conflicts with GDPR/CCPA and data minimization expectations.
- Better:
  - Explicit retention policies per data class.
  - Pseudonymization where possible (especially for ML feature stores).
  - Ability to honor deletion while preserving risk aggregates.

Example patterns

Example 1: Card program frozen overnight
A card issuer relied on a third-party fraud engine with default settings. A sudden spike in card testing and card-not-present fraud triggered scheme thresholds. The network leaned on the sponsor bank, which paused the entire program. Root cause: no explicit monitoring of fraud KPIs vs. scheme thresholds, no ability to retune rules quickly.
Example 2: Open banking A2A product halted after AML review
A fintech launched instant account-to-account payouts with minimal additional AML logic, assuming existing card-based scenarios were enough. When regulators requested examples of monitoring for mule accounts and money-laundering typologies specific to A2A flows, they had none. Outcome: forced product pause until new scenarios and case management were implemented.
Example 3: KYC vendor outage turns into compliance incident
A KYC provider had a multi-hour API outage. The fintech failed open (“we’ll catch issues later”) to preserve conversion. Later review showed sanctioned individuals onboarded during the window. There was no playbook for degraded KYC, and no way to easily re-verify impacted users.

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

Focus on things that reduce blast radius without rewriting your stack.

Map your real critical path for money movement
- Whiteboard: from user action → money moved → settlement.
- Mark:
  - Where KYC happens.
  - Where fraud decisions happen.
  - Where AML checks happen.
- Question:
  - Is there any path to irreversible movement without passing a risk+AML checkpoint?
Instrument traceability for a small slice
- Pick one payment type (e.g., card-not-present purchase, A2A payout).
- For new transactions this week, ensure you log:
  - Inputs to risk/AML checks (features,