Fraud and Risk

Overview

Rules catch known patterns. Models catch new ones. We combine both, score each transaction or account, and route alerts to investigators with the evidence attached.

Every alert, decision and model version is recorded, so you can show a regulator why something was stopped or let through.

Who it is for

• Payment companies and lenders that need monitoring before scale.
• Banks modernising alert handling and case management.
• Platforms facing account takeover or first-party fraud.

What you get

• Real-time and batch transaction monitoring.
• Rules your team can edit, plus anomaly and risk models.
• Case management with evidence and an investigator workflow.
• AI summaries of an account’s history for each alert.
• Model monitoring, drift alerts and audit records.

How it runs

1. Agree the risks, the data and the current alert volume.
2. Start with rules and a baseline model.
3. Build case management and the investigator view.
4. Tune on real outcomes to cut false positives.
5. Monitor models and retrain on a schedule.

In practice

• Payout fraud stopped by flagging new bank accounts with unusual first payouts.
• Investigators handling alerts faster with an AI summary of the account.
• False positives reduced by tuning rules against confirmed outcomes.

Technology

• Streaming with Kafka and a feature store.
• Gradient-boosted and graph models for risk.
• MLflow for model versions and monitoring.

How we charge

Fixed-scope build, then a monthly run with model monitoring.

Questions