AI consulting use case

AI consulting for fraud and AML detection

Detection systems that catch more real fraud, flood analysts with fewer false alarms, and stand up to the second line of defense.

Start a conversation

← All AI consulting use cases

The problem

Why fraud & aml detection is hard to get right

Rules engines fire on patterns yesterday's fraudsters used, so true fraud slips through while analysts drown in false positives — often 90-plus percent of alerts are noise. Every missed case is a loss and a regulatory exposure; every false alert is wasted investigator time. The hard part is lifting recall without making the backlog worse, and proving the model to a validation team that distrusts anything it cannot explain.

How we build it

Behavioral and graph features

Entity-resolution and transaction-graph features that surface mule networks and layering the rules engine never sees, engineered from your own history.

Risk-ranked alert triage

Models that score and rank alerts so investigators work the riskiest cases first, with the noisy long tail auto-dispositioned and logged.

Explainability for the second line

Reason codes and case narratives on every alert so analysts act faster and model risk can validate what drove each decision.

Monitoring against drift

Champion/challenger pipelines and drift detection that keep precision from decaying as typologies and customer behavior shift.

The outcome

Analysts work a smaller, sharper queue — measurably higher true-positive rate and a shrinking backlog — with a documented, monitored model your validators can sign off on rather than fight.

RelatedApplied Builds

RelatedEvaluation & Safety

Use casesAll AI consulting use casesBrowse the full set of applied-AI services we build, by the problem they solve.

ServiceAI consultingStrategy and production engineering in one continuous engagement.

Key concepts

GlossaryevalsEvals are structured tests that measure how well an AI system performs against a set of real tasks, turning model quality from a feeling into a number you can track.

GlossaryguardrailsGuardrails are the controls placed around an AI system that constrain what it can say or do — checking inputs and outputs and bounding the actions it's allowed to take.

GlossaryAI governanceAI governance is the framework of policies, controls, and accountability that decides how an organization builds, approves, monitors, and retires AI systems — so the technology is used responsibly and defensibly.

More use cases

Use caseDocument & data extractionPipelines that turn messy documents into structured data you can trust — with validation and provenance, not just a parse.

Use caseCustomer support copilotsSupport AI that resolves real tickets and briefs your agents — grounded in your own policies, with clean handoffs when it shouldn't.

Use caseDemand forecastingForecasts that beat last year's average — probabilistic, explainable, and wired into the planning decisions they're meant to drive.

Put AI to work on fraud & aml detection

Bring us the metric you need to move. We will tell you what we would build, how long it takes, and what it is worth.

Start a conversation

See our work