PayFlux — Payment Traffic Observability Buffer

What PayFlux is:

PayFlux ingests, orders, and normalizes payment events in real time, providing early visibility into risky patterns—retry storms, processor degradation, geographic anomalies, failure clustering—before they escalate into rate limits, account flags, or lost revenue.

It buffers payment events via Redis Streams and exports them (stdout or file) for downstream analysis, alerting, and archival by external systems (Vector, Fluent Bit, data warehouses).

What PayFlux is NOT:

• Not a payment processor (does not handle money or PCI-regulated data)
• Not a compliance audit log (export is best-effort after ACK by design)
• Not a replacement for durable storage (external log shippers with retries handle durability)

Target users:

Teams operating real payment systems who need observability buffers for traffic analysis, early warning signals, and anomaly detection. You should be comfortable running Redis, configuring log shippers, and operating infrastructure.

Key design tradeoff:

Events are acknowledged (ACK) in Redis before export. Export is best-effort and non-blocking. For durable storage, use PAYFLUX_EXPORT_MODE=stdout piped to a log shipper with disk buffering. This architecture favors throughput and operational simplicity over in-app durability guarantees.

⸻

Warning

This is NOT a managed service.

PayFlux requires you to operate Redis, collect logs, and monitor metrics yourself. There is no hosted dashboard, no support SLA, and no magic.

If you want a hosted version, request early access instead.

License: PayFlux is offered under a commercial early-access license. See LICENSE.md for details.

⸻

Deploy PayFlux in 10 Minutes (Checklist)

Prerequisites (install these first):

• Docker — runs PayFlux and Redis in containers (install)
• curl — sends test requests (pre-installed on macOS/Linux)
• Git — clones this repo

Note

What is Redis? Redis is a fast in-memory database. PayFlux uses it to queue and order payment events. You don't need to configure it—Docker handles everything.

---

✅ Step 1: Clone the Repository

git clone https://github.com/Blight-East/PayFlux.git
cd PayFlux

Expected: You see a deploy/ folder and main.go file.

Common failure: git: command not found → Install Git: brew install git (macOS) or apt install git (Linux).

---

✅ Step 2: Configure Your API Keys

Option A (Recommended): Copy the example environment file:

cd deploy
cp .env.example .env
# Edit .env with your real keys

Note

Windows users: PowerShell supports cp (alias for Copy-Item). In Command Prompt, use copy .env.example .env instead.

Option B: Edit deploy/docker-compose.yml directly and replace the placeholder values.

Your keys:

• PAYFLUX_API_KEY — Any secret string you choose
• STRIPE_API_KEY — From Stripe dashboard (test mode sk_test_... is fine)

Expected: File saved with your real keys.

Common failure: Forgot to save → PayFlux will reject requests with 401 unauthorized.

---

✅ Step 3: Start Everything

cd deploy
docker compose up --build

Note

The first run builds PayFlux from source (~30 seconds). Subsequent runs start instantly.

Expected: You see logs like:

payflux-1  | {"level":"INFO","msg":"server_listening","addr":":8080"}
redis-1    | Ready to accept connections

Common failure: port 8080 already in use → Stop whatever is using port 8080, or change the port in docker-compose.yml.

---

✅ Step 4: Verify Health

Open a new terminal and run:

curl http://localhost:8080/health

Expected: {"status":"ok"}

Common failure: Connection refused → PayFlux isn't running. Check the Docker logs.

Note

Windows users: PowerShell's curl is an alias for Invoke-WebRequest. Use one of these instead:

Invoke-WebRequest http://localhost:8080/health
# Or install GNU curl and use:
curl.exe http://localhost:8080/health

---

✅ Step 5: Send a Test Event

Important

The payload below is the minimal valid example. Missing or modified fields may result in a 400 validation error. Use this exact payload for your first test.

curl -X POST http://localhost:8080/v1/events/payment_exhaust \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "event_type": "payment_failed",
    "event_timestamp": "2026-01-09T12:00:00Z",
    "event_id": "550e8400-e29b-41d4-a716-446655440000",
    "processor": "stripe",
    "merchant_id_hash": "abc123",
    "payment_intent_id_hash": "pi_abc",
    "failure_category": "card_declined",
    "retry_count": 0,
    "geo_bucket": "US"
  }'

Note

Windows users: The multiline syntax above is bash-only. Use this single-line command instead:

curl.exe -X POST http://localhost:8080/v1/events/payment_exhaust -H "Authorization: Bearer your-secret-api-key" -H "Content-Type: application/json" -d "{\"event_type\":\"payment_failed\",\"event_timestamp\":\"2026-01-09T12:00:00Z\",\"event_id\":\"550e8400-e29b-41d4-a716-446655440000\",\"processor\":\"stripe\",\"merchant_id_hash\":\"abc123\",\"payment_intent_id_hash\":\"pi_abc\",\"failure_category\":\"card_declined\",\"retry_count\":0,\"geo_bucket\":\"US\"}"

Expected: HTTP 202 (empty response body).

Verification: Run curl -s localhost:8080/metrics | grep payflux_ingest_accepted and confirm the counter incremented. If using PAYFLUX_EXPORT_MODE=stdout, you should also see a JSON export line in the payflux container logs.

Common failure: 401 unauthorized → Your Authorization header doesn't match PAYFLUX_API_KEY in docker-compose.yml.

---

✅ Step 6: Check Metrics

curl -s http://localhost:8080/metrics | grep payflux_ingest_accepted

Expected: payflux_ingest_accepted_total 1

Note

Windows users: grep is not available by default. Use findstr instead:

curl.exe -s http://localhost:8080/metrics | findstr payflux_ingest_accepted

Common failure: Metric is 0 → Your test event was rejected. Check the logs for validation errors.

---

Did It Work?

If you completed all steps, you should see:

Check	Command	Expected
Health	curl localhost:8080/health	{"status":"ok"}
Metrics	curl -s localhost:8080/metrics | grep accepted	payflux_ingest_accepted_total 1
Export	payflux container logs (stdout mode)	JSON export line with event_id

Congratulations! PayFlux is running. Events are being buffered in Redis and exported to stdout.

---

What's Next?

• Add Vector (optional): Run docker compose --profile full up --build to include Vector for durable log shipping. Configure deploy/vector.toml to send events to S3, Elasticsearch, Datadog, etc.
• Monitor with Prometheus: Scrape http://localhost:8080/metrics for alerting.
• Read the Runbook: See RUNBOOK.md for operational guidance.

---

Advanced Deployment (Optional)

For production environments, see the reference configs below. These require more infrastructure knowledge.

systemd (deploy/systemd/payflux.service) — For bare-metal Linux servers.

Kubernetes (deploy/k8s/payflux.yaml) — Single-file manifest for K8s clusters.

---

Why PayFlux Exists

Payment systems fail silently.

Processors make decisions in real time based on traffic patterns, retries, and failure clustering—while most merchants only see delayed dashboards or logs. By the time an issue is visible, the damage is already done.

PayFlux gives teams timely visibility and control over payment behavior before processors escalate risk actions.

---

Core Architecture

[Producers]
   |
   |  HTTP JSON (stateless)
   v
[ PayFlux Node ]
   |
   |  Redis Streams (durable, ordered)
   v
[ Consumers ]
  ├─ Risk / Alerting
  ├─ Analytics
  ├─ Retry Optimization
  └─ Exports (Kafka, Webhooks, Warehouses)

Design Principles

• Producers never block on downstream systems
• Ordering is guaranteed per stream
• Backpressure handled natively by Redis
• Consumers scale independently
• Failure domains are isolated

---

Failure Behavior

PayFlux operates passively and out of band. If PayFlux is unavailable, payment processing continues normally. Failure results in reduced visibility only—never blocked transactions, retries, or processor interaction.

---

Key Features

• High-throughput HTTP ingestion
• Redis Streams for durability and ordering
• Consumer groups for parallel processing
• Crash-safe processing with pending reclaim
• Dead-letter queue (DLQ) support
• Prometheus-compatible metrics
• Health checks for orchestration systems

---

API Overview

Ingest Event

POST /v1/events/payment_exhaust

{
  "event_type": "payment_failed",
  "event_timestamp": "2026-01-06T00:00:00Z",
  "event_id": "550e8400-e29b-41d4-a716-446655440000",
  "merchant_id_hash": "abc123",
  "payment_intent_id_hash": "xyz456",
  "processor": "stripe",
  "failure_category": "processor_timeout",
  "retry_count": 0,
  "geo_bucket": "US",
  "amount_bucket": "50-200",
  "system_source": "checkout_api",
  "payment_method_bucket": "credit_card",
  "channel": "web",
  "retry_result": "failed",
  "failure_origin": "processor"
}

Response: 202 Accepted

---

Configuration

All configuration is via environment variables.

Logging:

• Startup logs use structured JSON format (slog) for easy parsing
• All other logs remain as log.Printf for now

Required:

Variable	Description
PAYFLUX_API_KEY	Single API key for auth (used if PAYFLUX_API_KEYS not set)
PAYFLUX_API_KEYS	Comma-separated list of valid API keys (preferred for rotation)
STRIPE_API_KEY	Stripe secret key (must start with sk_)

Optional:

Variable	Default	Description
REDIS_ADDR	localhost:6379	Redis server address
HTTP_ADDR	:8080	HTTP listen address
PAYFLUX_CONSUMER_NAME	auto-generated	Consumer name (hostname-pid-random if unset)
PAYFLUX_RATELIMIT_RPS	100	Rate limit: requests per second per key
PAYFLUX_RATELIMIT_BURST	500	Rate limit: burst allowance
PAYFLUX_STREAM_MAXLEN	200000	Max stream length (0 = no trimming)
PAYFLUX_PANIC_MODE	crash	Panic handling: crash (exit) or recover (restart loop)
PAYFLUX_EXPORT_MODE	stdout	Event export: stdout, file, or both
PAYFLUX_EXPORT_FILE	(none)	Export file path (required for file or both mode)

Checkout (Optional / Experimental):

Variable	Default	Description
PRICE_CENTS	9900	Checkout price in cents
PRODUCT_NAME	PayFlux Early Access	Checkout product name
SITE_URL	https://payflux.dev	Site URL for checkout redirects

---

Processor Risk Score (beta)

PayFlux enrichment provides a deterministic, explainable, and processor-aligned risk signal computed O(1) in-memory during export. It uses behavioral proximity—not ML—to help infrastructure teams distinguish between nominal jitter and catastrophic failure patterns.

Important

This is a behavioral early-warning signal, NOT a compliance, fraud-prevention, or audit guarantee.

Recommended Prometheus Alerts

# Alert on sustained high risk
- alert: ProcessorRiskHigh
  expr: payflux_processor_risk_score_last > 0.6
  for: 5m
  labels:
    severity: warning
  annotations:
    summary: "Processor {{ $labels.processor }} risk is elevated"

# Alert on critical spikes
- alert: ProcessorRiskCritical
  expr: payflux_processor_risk_score_last >= 0.8
  for: 1m
  labels:
    severity: critical

Configuration

Variable	Default	Description
PAYFLUX_RISK_SCORE_ENABLED	true	Enable/disable risk enrichment
PAYFLUX_RISK_SCORE_WINDOW_SEC	300	Sliding window for metrics (default 5m)
PAYFLUX_RISK_SCORE_THRESHOLDS	0.3,0.6,0.8	Cutoffs for elevated, high, and critical bands
PAYFLUX_TIER	tier1	Export tier: tier1 (detection only) or tier2 (adds interpretation)

Tier Behavior

Tier 1 (default): Detection only—score, band, drivers. No interpretation.

Tier 2: Adds processor_playbook_context (how processors typically interpret this) and risk_trajectory (acceleration vs baseline).

Note

Source of truth: Tier is currently set via PAYFLUX_TIER environment variable. License-derived tiering is a planned future feature (not yet implemented).

Tier Intent (Important)

Tier	Audience	Purpose
Tier 1	Risk & fraud teams	Detection + proof of value. See what's happening.
Tier 2	Payments & revenue owners	Interpretation + momentum + optional alerts. Understand why it matters and how fast.

Note

Alerting (Tier 2 only): The optional Alert Router sidecar delivers warnings to Slack or webhooks when enabled. It requires PAYFLUX_TIER=tier2 and PAYFLUX_PILOT_MODE=true because it reads from the /pilot/warnings endpoint. Alert Router is disabled by default and does not modify PayFlux core behavior.

See docs/TIER_GATING.md for full tier rules and language constraints.

Export Example (Tier 1)

{
  "event_id": "550e8400-e29b-41d4-a716-446655440000",
  "processor": "stripe",
  "processor_risk_score": 0.72,
  "processor_risk_band": "high",
  "processor_risk_drivers": ["high_failure_rate", "retry_pressure_spike"]
}

Export Example (Tier 2)

{
  "event_id": "550e8400-e29b-41d4-a716-446655440000",
  "processor": "stripe",
  "processor_risk_score": 0.72,
  "processor_risk_band": "high",
  "processor_risk_drivers": ["high_failure_rate", "retry_pressure_spike"],
  "processor_playbook_context": "This pattern typically correlates with processor-side monitoring escalation.",
  "risk_trajectory": "Pattern accelerating: ~3.2× above baseline over the last 5 minutes."
}

---

Pilot Mode (v0.2.3+)

Enable pilot mode to capture "warning → outcome" proof during a 90-day pilot:

PAYFLUX_PILOT_MODE=true

When enabled:

• Warnings are created for elevated+ risk events
• /pilot/dashboard provides a minimal outcome annotation UI
• Outcomes are emitted to stdout for log pipeline persistence

Annotate an outcome:

curl -X POST http://localhost:8080/pilot/warnings/{warning_id}/outcome \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"outcome_type": "throttle", "observed_at": "2026-01-11T08:30:00Z"}'

See docs/PILOT_PROOF.md for full pilot mode documentation.

---

Stripe Integration (Optional)

PayFlux can optionally ingest Stripe webhook events to detect when Stripe takes enforcement actions such as throttles, reviews, or rate limits. This allows PayFlux to correlate its warnings with observed Stripe actions and measure the time between warning and enforcement during pilots.

Key Benefits for Pilots:

• Outcome Correlation: Measure the effectiveness of PayFlux warnings by correlating them with actual processor outcomes.
• Zero Integration Effort: No changes to your checkout or payment flow are required.
• PCI Safe: Metadata only; no PAN or cardholder data is touched.
• Quick Setup: Typically ~15 minutes using our reference implementation.

See the Stripe Webhook Forwarder Example for a reference implementation for pilot use.

---

API Key Rotation:

To rotate keys without downtime:

1. Add the new key to PAYFLUX_API_KEYS alongside the old key: old-key,new-key
2. Deploy and verify clients can use the new key
3. Update clients to use the new key
4. Remove the old key from the list and redeploy

---

Operational Maturity

Runbook (RUNBOOK.md)

• SOPs for stuck messages, Redis outages, and export failures.
• Tuning guide for rate limits and memory pressure.

Performance & Smoke Tests (scripts/smoke_load.sh)

• Reproducible load test (10k events) to verify capacity.
• main_bench_test.go for in-process latency tracking.

---

Event Export

After successful processing, PayFlux exports events as line-delimited JSON to stdout and/or file.

Why JSON export?

• Standards-compliant: Works with Datadog, Fluent Bit, Vector, Filebeat, CloudWatch, etc.
• Simple piping: ./payflux > events.jsonl or systemd journal capture
• No vendor lock-in: Pure JSON, no proprietary formats

Export modes:

• stdout (default): Write to stdout for log collection
• file: Append to PAYFLUX_EXPORT_FILE
• both: Write to both stdout and file

Export is best-effort after ACK; if export fails, events are still acknowledged.

Example exported event:

{"event_id":"550e8400-e29b-41d4-a716-446655440000","event_type":"payment_failed","event_timestamp":"2026-01-09T12:00:00Z","processor":"stripe","stream_message_id":"1767957855596-0","consumer_name":"host-1234-abc","processed_at":"2026-01-09T17:00:00Z"}

Integrations:

Stdout (default) works with log aggregators:

# Datadog Agent (via journald or file)
./payflux | tee -a /var/log/payflux/events.jsonl

# Vector
./payflux | vector --config vector.toml

# Fluent Bit
./payflux 2>&1 | fluent-bit -c fluent-bit.conf

# CloudWatch Logs (via awslogs driver or agent)
docker run --log-driver=awslogs ... payflux

File mode for persistent local export:

PAYFLUX_EXPORT_MODE=file PAYFLUX_EXPORT_FILE=/var/log/payflux/exports.jsonl ./payflux

Monitor export health:

curl http://localhost:8080/export/health
# Returns per-destination status with timestamps:
# {"enabled":true,"export_mode":"stdout","destinations":{"stdout":{"enabled":true,...}}}

Prometheus metrics for export observability:

payflux_events_exported_total{destination="stdout|file"}
payflux_export_errors_total{destination="stdout|file",reason="write|marshal|flush"}
payflux_exports_last_success_timestamp_seconds{destination="stdout|file"}

File Rotation:

PayFlux export is designed for external rotation via standard system tools:

# logrotate (/etc/logrotate.d/payflux)
/var/log/payflux/exports.jsonl {
    daily
    rotate 7
    compress
    delaycompress
    missingok
    notifempty
    postrotate
        killall -SIGHUP payflux 2>/dev/null || true
    endscript
}

# systemd with journald (stdout capture)
systemctl cat payflux.service
[Service]
StandardOutput=journal
# Access via: journalctl -u payflux -f

# Docker logging with rotation
docker run -d \
  --log-driver json-file \
  --log-opt max-size=50m \
  --log-opt max-file=10 \
  payflux

# Log shippers (Vector, Fluent Bit, Filebeat) handle rotation automatically

PayFlux intentionally does not implement in-app rotation. External tooling is more reliable, configurable, and battle-tested.

What this is NOT:

• Not a replacement for analytics databases (use downstream exporters)
• Not a compliance archive (export to S3, warehouse, etc.)
• This is a starter interface for observability, not the final data layer

---

Observability & Integrations

Available Today

• /metrics — Prometheus format (Grafana, Datadog compatible)
• /health — readiness / liveness checks
• Structured JSON startup logs (slog) + plain runtime logs
• Redis Streams as the event backbone

Export Model

PayFlux does not force a dashboard. Instead:

• All events land in Redis Streams
• Consumers attach to the stream
• Each consumer implements a single responsibility

This makes PayFlux compatible with:

• Datadog
• Grafana
• Kafka / Redpanda
• Webhooks
• Data warehouses (Snowflake, BigQuery)

Planned Exporters

• Kafka / Redpanda exporter
• Webhook exporter
• Warehouse batch exporter

Exporters are implemented as consumers—no changes to ingestion required.

---

Performance (Local Proof)

• Sustained 40k+ events/sec on a laptop
• Zero pending messages under load
• Consumer lag drains to zero after spikes
• No producer backpressure

Raw Redis output and load test commands are included in PROOF-load-test.md.

---

Operational Defaults

Redis Durability

PayFlux relies on Redis for event buffering and stream durability. Redis persistence is the operator's responsibility.

Recommended Redis configuration for production:

• Enable AOF (Append Only File): appendonly yes
• Use appendfsync everysec for a good balance of durability and performance
• Consider RDB snapshots as a secondary backup

PayFlux assumes Redis is configured for production-safe durability. Without proper persistence, events may be lost on Redis restart.

Stream Retention

PayFlux intentionally bounds stream size using XTRIM MAXLEN ~ (approximate trimming).

• Default retention: PAYFLUX_STREAM_MAXLEN=200000 messages
• Set to 0 to disable trimming (not recommended for production)

This is a deliberate design decision:

• PayFlux is an observability and alerting buffer, not a long-term data store
• Events are retained long enough for monitoring, alerting, and short-term replay
• For compliance archiving or long-term analytics, export events downstream (data warehouse, Kafka, S3)

Replay and Reprocessing

Consumers can replay events within the retention window:

• Use XREAD or XREADGROUP with specific message IDs to replay
• Old events are trimmed by design after reaching PAYFLUX_STREAM_MAXLEN

For long-term replay requirements:

• Attach a downstream exporter (Kafka, warehouse, log sink)
• Exporters are implemented as consumers—no changes to PayFlux required

Data Retention & Minimization

PayFlux is designed with built-in data minimization by default.

Raw processor event payloads are retained for a maximum of 7 days and are automatically purged on a rolling basis. After this window, PayFlux persists only normalized warnings and aggregated metrics, which do not contain raw transaction payloads or sensitive processor data.

This approach reduces long-term data exposure while preserving operational visibility, detection accuracy, and historical performance insights.

PayFlux does not block payments or interfere with processor decisioning. It surfaces signals and operational context only.

Variable	Default	Description
PAYFLUX_RAW_EVENT_TTL_DAYS	7	Days to retain raw events before auto-delete (0 to disable)

---

Production Readiness Notes

PayFlux v0.2.x is safe for early access and pilot deployments.

Assumptions:

• Redis is configured with AOF persistence (appendfsync everysec or stricter)
• PayFlux runs under a process supervisor (systemd, Docker, Kubernetes) for automatic restarts
• External monitoring is in place (Prometheus scraping /metrics, alerting on payflux_pending_messages)
• TLS termination is handled by a load balancer or reverse proxy

What PayFlux handles:

• Authentication and rate limiting
• Idempotency and deduplication
• Graceful shutdown with in-flight request completion
• Dead-letter queue for failed messages
• Panic recovery (configurable)

What PayFlux does NOT handle:

• Redis persistence configuration (operator responsibility)
• TLS termination (use a reverse proxy)
• Long-term event archival (use downstream exporters)
• Horizontal scaling coordination (each instance is independent)

---

Trust & Safety

For details on PayFlux's security posture and data handling:

• Data Flow Overview — What PayFlux processes and what it does not
• Security Posture — Pilot-level security controls
• Incident Response — Security contact and incident handling

---

Risk Infrastructure Documentation

Tip

Start Here: View the full Documentation Index for a categorized guide to all 60+ risk and observability pages.

• How Payment Risk Scoring Works — Factual overview of scoring models and signals.
• Stripe Applied Views — 12+ guides for Stripe-specific observability (Connect, SaaS, BNPL).
• How Chargebacks Propagate Through Payment Networks — Structural analysis of the dispute lifecycle.
• How KYC and Underwriting Reviews Work — Infrastructure perspective on compliance and identity verification.
• How Payout Delays Work — Understanding settlement timing and risk holds.
• Why Processors Request Additional Documents — Identity verification and regulatory compliance logic.
• How Risk Threshold Events Work — Automated enforcement of risk policy boundaries.
• How Refunds and Reversals Propagate — Multi-stage flows and balance impact.
• How Shadow Risk Accumulates — Unsettled visibility and pending exposure.
• How Retry Amplification Increases Exposure — Determining the multiplier effect of aggressive retries.
• How Fraud Model Drift Occurs — Behavioral change vs static risk rules.
• How Compliance Timing Gaps Form — AML triage lag and exposure windows.
• How Network vs Processor Authority Works — Hierarchy of enforcement and policy propagation.
• How Rolling Risk Windows Work — Trailing averages and delayed recovery.
• How Negative Balance Cascades Form — Refund spirals and delayed settlement gaps.
• How Dispute-Reserve Feedback Loops Work — Circular causality and liquidity crunches.
• How Multi-Signal Correlation Affects Risk — Collapsing probability and compounded severity.
• How Liability Horizons Affect Payouts — Tail risk exposure and settlement holds.
• How Refund Abuse Patterns Work — Refund cycling, arbitrage abuse, and masking.
• How Geo-Velocity Affects Risk — Cross-border spikes and IP triangulation.
• How MCC Drift Affects Underwriting — Business model divergence and code misuse.
• How Network Monitoring Programs Work — VAMP, ECP, and penalty thresholds.
• How Reserve Release Logic Works — Liability decay and rolling windows.
• How Risk Model Retraining Lag Works — Concept drift and delayed adaptation.
• How BIN–Country Mismatch Affects Payment Risk — Geographic triangulation and issuer mismatch.
• How Retry Logic Affects Risk — Balancing recovery rates with network compliance.
• How Dispute Aging Curves Work — Vintage analysis and time-based risk accumulation.
• Understanding Decline Reason Codes — Category taxonomy and issuer behavior.

---

How It Works

• How Payment Infrastructure Detects Risk Events — Understanding threshold evaluation and state transitions.
• How Payment Settlements Work — Batch cycles, clearing timelines, and funding gaps.
• How Settlement Batching Works — Ledger gaps and reconciliation lag.
• How Risk Threshold Hysteresis Works — Penalty states and recovery buffers.
• How Card Network Rule Changes Affect Merchants — Policy updates and compliance enforcement.
• Why Risk Events Often Follow Business Growth — Underwriting drift and velocity thresholds.
• How Retry Storms Form in Payment Systems — Timeout loops and feedback amplification.

---

Verticals

• Payment Risk Observability for Marketplaces — Multi-seller attribution and platform exposure.
• Payment Risk Observability for Marketplaces with Escrow — Held funds and release triggers.
• Payment Risk Observability for BNPL Providers — Settlement lag and dispute exposure tracking.
• Payment Risk Observability for PSPs — Portfolio-wide dispute aggregation and settlement float.
• Payment Risk Observability for Subscription Businesses — Churn recovery and retry storm monitoring.
• Payment Risk Observability for High-Risk Merchants — Volume volatility and reserve monitoring.
• Payment Risk Observability for Aggregators — Sub-merchant pooling and liability tiers.
• Payment Risk Observability for SaaS Platforms — Recurring billing friction and soft decline recovery.

---

Use Cases

• Detecting Stripe Payment Incidents — Monitoring API error rates and account status changes.
• Monitoring Payout Delays — Detecting liquidity gaps and schedule deviations.
• Handling Dispute Surges — Velocity monitoring and threshold projection.
• Monitoring Manual Review Backlogs — Queue depth and operational capacity.
• Detecting Card Testing Attacks — Identifying velocity spikes and BIN anomalies.
• Differentiating Card Testing from Velocity Fraud — Pattern shape and authorization behavior.
• Monitoring Payment Reserves — Tracking balance segmentation and release schedules.
• Monitoring Negative Balances — Tracking debt exposure and refund liability.
• Monitoring Dispute Ratios — Network threshold forecasting and volume normalization.
• Monitoring Settlement Failures — Bank rejects and ledger reconciliation.
• Monitoring Issuer Decline Spikes — Identifying BIN attacks and regional blocks.
• Monitoring Marketplace Payout Failures — KYC holds and banking rail blocks.
• Detecting Cross-PSP Payment Failures — Correlated outages and issuer blocks.

---

Infrastructure Pillars

• Payment Risk Events — Automating risk state detection and recovery.
• Payment System Observability — Measuring system-level behavior across networks.
• Dispute Infrastructure — Procedural governance of evidence and arbitration.

---

Compliance Infrastructure Documentation

• How Transaction Monitoring Works — Operational analysis of fraud detection and behavioral alerts.
• How AML Screening Works — Infrastructure perspective on sanctions, PEP, and adverse media screening.
• How Merchant Underwriting Works — Structural breakdown of business risk assessment and tiering.

---

Dispute Infrastructure Documentation

• How Dispute Evidence Works — Operational overview of evidence categories and submission mechanics.
• How Card Networks Handle Disputes — Procedural analysis of the dispute lifecycle and liability frameworks.
• Why Dispute Win Rates Vary — Structural analysis of outcome variance and success drivers.

---

Operational Risk Documentation

• Why Payment Processors Freeze Funds — Understanding risk control mechanisms and fund freezes.
• What Is a Payment Reserve — Understanding capital control mechanisms and risk buffers.
• Why Stripe, PayPal, and Adyen Freeze Funds — Shared risk models and structural behavior of major providers.

---

Data Handling

PayFlux processes pseudonymous operational event data provided by customers for the sole purpose of detecting and reporting payment risk patterns. PayFlux does not process or store cardholder data (PAN), CVV, or personal customer information. All identifiers are hashed or tokenized prior to ingestion.

---

Data Protection Roles

PayFlux acts as a data processor. Customers remain the data controller and determine what data is sent to PayFlux and how it is used.

---

Licensing

PayFlux is offered under a commercial early-access license.

• Self-hosted
• Annual license
• Pricing based on deployment size and event volume
• Custom enterprise agreements available

See LICENSE.md for details.

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Roadmap (Non-Binding)

• Multi-consumer examples
• Kafka exporter
• Managed hosted offering
• Per-merchant stream partitioning
• SLA tooling for processors and PSPs

⸻

Contact

📧 [email protected] 🌐 https://payflux.dev