ELVNFI
Risk-NGIn development

Risk, with context.

Open-source risk intelligence for financial institutions. Risk-NG detects fraud, monitors AML risk and explains suspicious behaviour across transactions, accounts, devices and financial networks.

Risk-NG/ transaction monitor
LIVE

Transaction risk

₦2,450,000NGN

Transfer · txn_8f21 · beneficiary age 3h

Risk score0 / 100
ALLOWREVIEWBLOCK

Risk signals

  • Velocity anomaly+28
  • New beneficiary+21
  • Device deviation+19
  • Unusual amount+17
  • Network exposure+9

Decision stream

LAST 60s
Rules fired
3
Model version
v0.4.2
Latency
18ms
Decision
REVIEW

Illustrative interface. Sample data.

The problem

Financial crime doesn’t look like a rule.

It looks like a pattern.

A transaction rarely tells the whole story. Risk emerges from the relationships between the entities around it.

Accounts
Transactions
Devices
Beneficiaries
Wallets
Merchants
Behaviour
Financial networks
Account HAccount EWallet FMerchant GAccount KWallet JAccount ADevice XAccount BBeneficiary CMerchant DSuspicious relationship detected

Swipe to explore →  ·  Illustrative graph. Sample entities.

The risk engine

One engine.Every signal.

Risk-NG combines deterministic rules, behavioural intelligence, machine learning and network analysis into a unified risk engine — one score, one explanation, one decision path.

  1. 01IngestStream and batch, from the rails you already run on.
  2. 02EnrichVelocity, baselines and graph features, computed in line.
  3. 03ScoreRules, models and network risk resolve to one number.
  4. 04DecideAllow, review or block — with the reasoning attached.

Input

Transactions

transfers · card · collections · payouts

Stage 01

Ingestion

stream + batch

Stage 02

Feature engineering

velocity · baselines · graph features

Deterministic

Rules

Probabilistic

ML models

Relational

Network risk

Unified

Risk engine

single score · full explanation

Allow
Review
Block
One path from raw transaction to decision. Rules, models and network risk resolve to a single score.

Rules × ML

Rules catch what you know.Machine learning finds what you don’t.

RulesDeterministic
# rule: large_transfer_to_fresh_beneficiary

IF
  transaction.amount > ₦5,000,000
AND
  beneficiary.age < 24h
THEN
  risk += 30

# fired on txn_8f21 → +30

Written, reviewed and versioned by your risk team. Every fire is traceable to a line of code.

Machine learningProbabilistic
  • Behaviour deviation87%
  • Velocity anomaly91%
  • Device deviation84%
  • Network exposure79%
ML risk score0.91

Learned from your own transaction history. Surfaces the shapes nobody wrote a rule for yet.

Deterministic when you need control. Probabilistic when patterns become complex.

Illustrative interface. Sample data.

Open source

Your risk infrastructure shouldn’t be a black box.

A risk decision you cannot explain is a risk decision you cannot defend — to your board, to your regulator, or to the customer whose transfer you just held. Risk-NG is open source.

Inspect the code.
Understand the models.
Build your own signals.
Deploy in your infrastructure.
Extend the platform.
Contribute back.
risk-ng — zsh

Developers

Built to be used, inspected and extended.

One endpoint to score a transaction. A signed decision, the signals behind it, and the model version that produced it — every time.

POST /v1/risk/scoreREQUEST
{
  "transaction_id": "txn_8f21",
  "amount": 2450000,
  "currency": "NGN",
  "beneficiary_age": "3h"
}
200 OKRESPONSE · 18ms
{
  "risk_score": 0.94,
  "decision": "review",
  "signals": [
    "velocity_anomaly",
    "new_beneficiary",
    "device_network_risk"
  ],
  "model_version": "v0.4.2"
}
API-first
Self-hostable
Open source
Machine learning
Event-driven

Illustrative API shape. Not a published contract.

Risk-NG waitlist

Be early.

Risk-NG is being built in the open. Join the waitlist to get early access, product updates and invitations to the first private releases.

No spam. Just product updates.