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Stop fraud before it clears. Not after.

Real-time AI that detects anomalous patterns, flags suspicious transactions and reduces false positives — so your risk team acts on signal, not noise.

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The problem

Rule-based controls can't catch adaptive fraud

  • Rule-based systems miss adaptive fraud

    Static rules work until fraudsters adapt. New attack patterns bypass existing controls until someone notices and updates the rules.

  • False positives that damage the customer relationship

    Overly aggressive detection blocks legitimate customers. Every false positive is a friction event — and a potential churn event.

  • Investigation backlog that never clears

    Analysts review flagged cases manually. The queue grows faster than the team can work through it.

  • Models that drift unnoticed

    Fraud patterns evolve faster than rule and model updates. Detection quality decays until losses or false positives spike.

How it works

Risk scoring that explains itself—and improves with every case

Step 1

Enrich the event

Payments, logins, and applications are joined with device, graph, and historical behaviour signals.

Step 2

Score and explain

Models rank risk with reason codes analysts can challenge—reducing black-box rejections.

Step 3

Close the loop

Investigation outcomes feed labels and thresholds so the next similar pattern is caught earlier.

Rules and models can run in parallel during migration—no big-bang cutover.

Stop fraud before it clears. Not after.

What's included

What you get when you run this with Thinkia

A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.

Real-time transaction scoring

Assesses every transaction for fraud probability at the moment it occurs.

Behavioural anomaly detection

Learns normal patterns per user, account or entity and flags deviations.

Adaptive model updating

Retrains on new fraud patterns continuously without requiring manual rule updates.

False positive reduction

Contextual scoring that distinguishes suspicious from legitimate unusual behaviour.

Investigation prioritisation

Ranks flagged cases by risk score and evidence strength so analysts focus on what matters.

Audit and regulatory reporting

Full decision log for every flagged transaction, formatted for regulatory submission.

Powered by Thinkia Sentinel

Results

What changes when this runs in production

Results vary by transaction volume, fraud typology and existing detection infrastructure.

+40%

Improvement vs. rule-based baseline on adaptive fraud patterns

–35%

Reduction in legitimate transactions incorrectly flagged

–50%

With AI-prioritised queue and pre-assembled evidence

How we work

From rules fatigue to adaptive signals analysts can explain

Signal inventory

Week 1–2

Fraud typologies, data feeds, and investigation workflows are baselined with your SOC/FIU.

Model & thresholds

Week 3–5

Scores, tiers, and override paths are tuned for precision/recall and regulatory expectations.

Champion/challenger

Week 6–9

Shadow scoring on live traffic; investigators validate alerts and narrative quality.

Operate & refresh

Week 10+

Feedback loops, drift monitoring, and periodic model reviews enter BAU governance.

Latency and explainability requirements vary by product line; scope follows highest-loss flows first.

Get started

Ready to scope this for your context?

We start with a focused session—no commitment—to map constraints and a sensible path.

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