See the connections. Detect the risk.

Uncover hidden connections between people, devices, merchants and agents across transactions to detect and block fraud and strengthen anti-money laundering investigations.

Actual KER interface for transaction risk oversight
Actual KER screenSynthetic data
Available today

Transaction fraud and AML for payment service providers across WAEMU.

Phase 2 or partnership

KYC and credit scoring capabilities can be brought forward with a partner organisation.

Block certain signals. Review the rest.

Once enabled by the institution, KER automatically holds a transaction when business rules or the score enriched with graph variables cross the authorised confidence threshold. Uncertain signals become prioritised cases.

01

Detect

KER checks the transaction against rules, the model and its relationship context.

02

Hold

A temporary measure applies to high-confidence detections within defined limits.

03

Review

An analyst investigates the case and a risk manager approves sensitive decisions.

Actual KER case queue ordered by risk, status and deadline

Uncertain cases follow a clear path.

The queue orders cases by risk and deadline. Analysts review facts and relationships, attach evidence and escalate to the risk manager when required.

Every decision reaches the right level.

Fraud analyst

Takes the case, explores relationships, gathers evidence and proposes a decision.

Risk manager

Monitors exposure, resolves escalations and controls review capacity.

KER administrator

Defines policies, thresholds, guardrails and access.

Relationships reveal what isolated data hides.

KER complements business rules and conventional models with graph variables. This context helps uncover patterns hidden by a single transaction and distinguish community solidarity chains from mule networks. GNN architectures are being evaluated for a later phase.

Shared context

Connected accounts, devices, agents and beneficiaries.

Collective behaviour

Recurrence, circulation of funds and coordination.

Business reading

Familiar solidarity, isolated anomaly or organised network.

Patterns grounded in transaction operations.

These examples come from the demonstration environment. Typologies, thresholds and measures are agreed with each institution.

Mule networkAML structuringAccount takeoverAgent collusionCircular flowsShared identity

Decisions remain governed.

KER separates policies, autonomy limits and approvals. The institution chooses what may be alerted, held or escalated.

Policies

Define scope, threshold and authorised action for each typology.

Guardrails

Cap temporary measures and define their expiry.

Dual control

Submit sensitive decisions to separate approval.

Traceability

Review connections, changes, decisions and data operations.

Start in an isolated sandbox.

The pilot uses a simulated model and synthetic data. Scope, access, success criteria and exit terms are agreed before evaluation.

Priority case

Select a fraud or AML journey that matters to the teams.

Authorised data

Limit fields and flows to the approved need.

Verifiable measures

Compare alert quality, review time and operational load.

Controlled access

Show the public sign in only after the agreed environment is deployed.

See KER on your priority journey.

The demonstration focuses on your decisions and operating constraints.

Book a demo