Audit & financial services
From reviewing everything to reviewing what matters.
An analytical workflow that ingests large volumes of audit data, scores and prioritises cases by risk, and puts expert attention where it counts.
- Project
- AI-powered audit & fraud analysis
- Client
- Audit & advisory practice
- Capabilities
- Machine learning · Risk scoring · Data engineering · Intelligent automation
- manual work
- −60%
- error rate in the evaluated workflow
- < 2%
See it work
illustrative · each cell = a batch of records
- 01
Thousands of records
- 02
Manual filtering
- 03
Manual review
- 04
Investigation
- Manual review effort
100%
- What experts look at
- Everything, in whatever order it arrives.
- Error rate (evaluated workflow)
- n/a
The problem
Audit and fraud investigation teams were working through thousands of records by hand: filtering, reviewing and only then investigating. Highly skilled people spent most of their time on cases that turned out to be routine, and the risky ones waited in the same queue as everything else.
Our approach
We didn’t try to replace expert judgement. We built a system to direct it.
- Data ingestion and normalisation brought heterogeneous audit sources into a single, consistent analytical view.
- Machine-learning risk scoring, combined with explicit analytical rules from the domain experts, ranked every case by likelihood and severity.
The result
Manual work fell by 60% and the evaluated workflow kept an error rate below 2%. The same team now spends its time where its expertise matters: investigating the cases most likely to be real.
Client details are anonymised. Demos are illustrative, built on synthetic data and fictional brands.