Korvena Shield · AI-enabled

Fraud detection that decides in real time, and explains itself

Score every transaction as it happens with AI models built for fraud detection, combined with rules and anomaly, behavioural and network signals tuned to African payment patterns, and hand your fraud desk decisions with the evidence attached.

Request a Demo

The problem

Fraud moves faster than rulebooks

Instant transfers settle in seconds, mule networks recycle accounts within hours, and every new channel opens a new angle of attack. Rules written after the last incident catch the last incident. Meanwhile analysts work queues with no owner and no evidence, and the regulator still expects a filed report.

Rules alone fall behind

Static thresholds miss new typologies and flag good customers, so teams loosen them and fraud walks through.

Alerts without owners

Flagged transactions land in a shared queue where holds expire and blocks are lifted without a decision on record.

No feedback loop

Confirmed fraud never reaches the model, so the same pattern is caught late again next month.

Capabilities

What Korvena Shield does

Real-time decisions: every transaction is scored as it happens and returned as allow, hold or block with the reasons behind the decision, so channels and core systems can act on it.
AI models plus fused signals: AI fraud-detection models, anomaly detection, behavioural and time-series analysis, network analysis and your rules combine into one calibrated score, so no single method is a blind spot.
Case management: every hold or block becomes a case assigned to an analyst, with queues by role, takeover and reassignment, and reminders that escalate when a case is left unattended.
Analyst feedback: confirmed and cleared cases are recorded as labels that sharpen detection, with controls on who can change a label once it is set.
Reporting and notices: suspicious-transaction reports prepared from the case for NFIU submission with four-eyes approval, and approved notices to other institutions’ fraud desks when funds have moved.
Governance and audit: decisions, actions and configuration changes are kept in append-only ledgers, model releases are signed and validated before they serve traffic, and any decision can be replayed.

AI in Shield

Detection built on models, governed like software

Shield is machine learning at its core, fused with rules and network analysis, and every model is validated, signed and explainable before it touches a transaction.

Fused machine learning

Supervised models, anomaly detection, behavioural and time-series models and network analysis combine into one calibrated score, so no single method is a blind spot.

Explainable in real time

Each decision returns the signals that drove it, so channels, analysts and examiners see why a transaction was held or blocked.

Learns under control

Analyst outcomes retrain detection, but a model serves traffic only after validation and signing, and every version can be replayed and audited.

How it works

From transaction to decision in four steps

Connect

Channels, switch or core systems send transactions by API, in real time or in batches, using one transaction model.

Score

AI models trained on transaction behaviour, together with anomaly detection, network analysis and your rules, produce a calibrated fraud score with the reasons that contributed to it.

Act

Allow, hold or block is returned to the channel. Holds and blocks open a case with an owner, evidence and a deadline.

Learn

Analyst outcomes feed back into detection, and reports and notices go out with approval and a full trail.

Where it fits

Typical fraud programmes institutions run on Korvena Shield

Programme	What Shield provides
Instant transfers and payouts	Real-time scoring before release, mule and velocity signals, holds with evidence
Card and wallet transactions	Behavioural profiling per customer and device, anomaly detection on spend patterns
Agent and merchant networks	Network analysis across accounts, agents and merchants to surface collusion rings
Account takeover	Deviations from established behaviour, new-beneficiary and channel-switch signals
Fraud desk operations	Case queues, escalation, reporting workflow for NFIU submission, inter-bank notices

Bring a month of transactions to the demo

The fastest way to evaluate Shield is to replay your own transaction history through it. We will show you what it would have flagged, how cases would have been worked, and what your analysts would have seen.

Request a Demo