Real-time fraud scoring across payments, claims and identity.

AI Fraud Detection

T7 ships real-time fraud detection systems with the reason codes analysts and regulators demand — payments, claims, identity, ATO.

The problem

Rules-only fraud engines catch known patterns and miss adaptive fraud. Analyst queues drown in false positives.

Model-based fraud teams struggle with sub-100ms latency and explainability at the same time.

How T7 solves it

We combine gradient-boosted models with graph features and streaming feature stores to score events in <100ms.

Every score carries SHAP-based reason codes so analysts can act and regulators can audit.

Analyst UI, case management and feedback loops are shipped with the model — not left as an afterthought.

The workflow

Step 1

Stream & feature

Events flow through Kafka + Flink; features join in real time from feature store.

Step 2

Score & explain

Model produces score + reason codes in <100ms; low-confidence cases route to analyst queue.

Step 3

Investigate & learn

Analyst outcomes feed retraining; drift + performance monitored per segment.

Outcomes we ship for

-40%
False positives vs. rules-only
<100ms
End-to-end scoring latency
3–7%
Loss ratio improvement

The stack

KafkaFlinkXGBoostPyTorch GeometricFeastSHAPElastic

Frequently asked questions

How is this different from our rules engine?

We keep the rules engine as a floor and layer ML on top — best of both. Rules catch known, ML catches novel.

How do you handle model drift?

Live monitoring on performance + population stability; automated retraining triggers with champion/challenger rollout.

Can models run on-prem?

Yes — training and inference can be fully on-prem or edge for regulated environments.

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  • · AI feasibility & architecture review
  • · Product / MVP roadmap
  • · Integration & automation strategy