Solution

AI Fraud Detection

Fewer false positives, and a reason attached to every flag.

Rule-based fraud systems fail in a specific and predictable way: fraud patterns change faster than rules are written, so recall degrades quietly while false positives accumulate. Machine learning fixes the recall problem and introduces an explainability problem, which in regulated contexts is the harder of the two.

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Why this is usually broken

Static rules degrade as patterns shift, and nobody notices until losses move
False positives consume investigator capacity and damage good customer relationships
A flag without a reason cannot be actioned or defended
Regulated decisions require an explanation the model has to be able to produce

How we build it

Behavioural and graph signals

Patterns across entities and relationships that transaction-level rules cannot express.

Explanation with every flag

Each flag carries the specific factors that drove it, which is both an investigator productivity feature and a regulatory requirement.

Tuned to investigator capacity

Thresholds set against how many cases your team can genuinely work, because alerts beyond that capacity are not reviewed.

Model outside the decision

The system surfaces and ranks. A human decides. In regulated lines this separation is what makes the deployment defensible.

What changes

Explained Driving factors on every flag

Capacity-tuned Alert volume matched to your team

Adaptive Retrained as patterns move

Defensible Human decision, documented

Figures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.

Common questions

How do you handle bias and fair lending exposure?

Disparate impact testing across protected classes is part of the build, including proxy variables. Excluding protected attributes from the feature set is necessary and nowhere near sufficient, since geography and behaviour frequently proxy for them.

The services behind it

Model Fine-Tuning & Integration

Models that speak your industry's language, trained on your data.

Local & On-Prem LLM Deployment

The most powerful AI models, running entirely on your hardware.

Find out what this looks like for your organisation

A 30-minute call. We will tell you plainly whether AI is the right tool for the problem you have, including when it is not.