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Protect Pillar

"The Risk Stops Here"

Proprietary trigonometric fraud detection, a 5-state maker-checker workflow, RBAC with 25+ permission categories, and an immutable audit trail — all enforced at the database layer, not the application layer.

Enterprise-Grade Security
at Every Layer

Protect is the security and risk layer of LedgerFlow — enforced at the database level so it cannot be bypassed by any application, API call, or direct SQL injection.

Proprietary Fraud Detection
Trigonometric circular statistics model 24-hour behavioural patterns per account. 8-flag composite scorer. 90-day rolling statistical baseline. Flags anomalies in real time.
Visa Network Intelligence
Cross-merchant, network-level fraud signals from the Visa payment network — intelligence unavailable to any individual PSP operating in isolation.
RBAC with 25+ Permissions
Role-based access enforced at database level. 25+ granular permission categories. OIDC/Keycloak SSO. Session-based and JWT auth simultaneously supported.
Immutable Audit Trail
Every table change is captured by a PostgreSQL trigger: user, role, client IP, action, before/after records as JSON diffs. Stored in security.system_audit — non-bypassable by any route.
Maker-Checker Workflow
5-state approval workflow. Maker and checker identities are server-side JWT-derived — they cannot be forged by any client-side manipulation. Required separation of duties for FICA compliance.
Vendor-Pinned Supply Chain
All browser libraries self-hosted, version-pinned, and SHA-256 recorded. No third-party CDN contacted at runtime. Immune to polyfill.io-class supply chain attacks.

Trigonometric Fraud Detection.
Unique in the SME Tier.

Most fraud detection is simple threshold-based alerting. LedgerFlow uses circular statistics to model behavioural patterns around the 24-hour clock.

How It Works

Each account builds a 90-day statistical baseline across three dimensions: day-of-week (DOW), day-of-month (DOM), and hour-of-day. The algorithm converts time of day to a circular angle and uses sin, cos, and atan2d to compute the mean direction and concentration of that account's transaction activity.

When a new transaction arrives, the algorithm calculates how far the current moment is from the account's historical mean direction. Transactions that occur at times outside the account's normal pattern are flagged — even if the amount is within normal range.

-- Circular mean direction per account mean_angle = atan2d(avg(sin(hour * 15)), avg(cos(hour * 15))) -- Angular deviation from mean deviation = |current_angle - mean_angle| -- Combined with 7 other flags → composite score fraud_score = weighted_sum(deviation, amount_z, velocity, ...)
8 Fraud Flags — Composite Scorer
1
Time-of-Day Anomaly
Transaction outside the account's historical 24-hour pattern (circular statistics)
2
Day-of-Week Anomaly
Transaction on an atypical day-of-week for this account
3
Amount Z-Score
Transaction amount beyond 2σ from 90-day mean for this account
4
Velocity Breach
Transaction count in window exceeds the account's configured velocity limit
5
Balance Threshold
Post-transaction balance would exceed the account's configured maximum or fall below minimum
6
Counterparty Risk
Destination account profile carries a fraud-risk flag or has a recent adverse event
7
Network Signal
Visa network intelligence flag from cross-merchant fraud data
8
Pattern Deviation
Transaction breaks the account's established DOW × DOM × hour-of-day composite pattern

5-State Separation of Duties.
Non-Forgeable JWT Identity.

PENDING
Maker creates batch
PRE-AUTH
Submitted for review
AUTHORISED
Checker approves
PROCESSED
Posted to ledger
REJECTED
Checker rejects — full audit

Maker and checker roles are JWT-derived server-side at the PostgREST layer — no client-side manipulation can forge or swap them. Every state transition is recorded in the immutable audit log.

Ready to protect your financial operations?
Fraud detection, maker-checker, and audit trail — live in a day.
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