Risk, fraud, and claims
Fraud & Financial-Crime Decisioning
The two-sided cost of fraud managed as an explicit economic policy, decided in line, with every decision audited.
The problem
Static rules block good customers along with bad ones, and every false decline carries a cost of its own that rules rarely see. Fraud patterns adapt quickly, labels arrive weeks later as chargebacks, and a blocked transaction never shows whether it was good.
Who owns it
Fraud and financial-crime strategy, with the chief risk officer. Fraud operations analysts work the review queue and tune the rules.
What it learns from
- The authorization stream
- Chargebacks and disputes
- Device and behavioral signals
- Case dispositions
- Watchlists, for financial crime
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Scores every transaction in real time
Streaming features such as velocity counts and device signals feed a calibrated model, and link analysis across cards, devices, merchants, and counterparties surfaces rings. Features are computed the same way in training and in production.
- 02
Decides through an economic policy
Score bands, amount, and segment set approve, step-up challenge, or block. Challenge budgets cap friction, rules such as strong customer authentication are hard constraints, and an emergency lane handles attacks in progress. Existing rules keep running inside the policy as guardrails.
- 03
Acts in the authorization path and case management
Decisions execute inside the payment authorization flow, and post-authorization reviews and confirmed fraud become cases in case management. If the model degrades, the policy keeps deciding on rules alone.
- 04
Measures with champion and challenger on live traffic
Challengers run against the champion on live traffic, and a small exploration slice measures the good customers declined and the fraud let through. Fraud loss, approval rate, challenge rate, and review rate are always reported together.
What it moves
The business levers
Fraud loss
Less fraud reaching settlement.
Approval rate
Good customers approved who would otherwise have been declined.
Review productivity
Analysts work a prioritized queue with the context already assembled.
Financial-crime triage
Alerts prioritized and assembled into cases with an auditable rationale.
Proof
How the return is proven
The return is fraud loss avoided plus approval margin recovered, minus friction and review cost, computed from matured labels in the champion and challenger ledger and stated against the return model agreed up front.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Case and decision views are scoped by role through your single sign-on.
Approval gates
Threshold structures need fraud-strategy sign-off, promoting a challenger outside pre-approved criteria needs approval, and an emergency rule pages a person.
Audit trail
Every score, rule hit, and decision is logged with its reason codes.
FCRA, ECOA, and model risk
Where a decision touches credit, FCRA and ECOA adverse-action and dispute processes apply, and decline rates are monitored for fairness. Banks get documentation for model-risk management, and for financial crime no alert is suppressed without below-the-line sampling.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.