Client: Large financial services organization based in Europe.

Challenge: Audits required significant manual effort and training time for auditors. The content being audited typically followed a set format, but underlying data was not always clean - creating inconsistency and rework, and making scaling difficult.

What we did:

Coucal's consultants guided the client through developing an end-to-end AI/ML audit approach:

• aligning stakeholders on what “good” looks like

• preparing and curating training data from thousands of samples

• addressing messy/variable inputs through preprocessing and rules

• developing a model that could audit against defined criteria with traceable outputs

• supporting validation, quality testing, and real-world execution to ensure the solution worked in real audit conditions.

Outcomes:

• 90% reduction in manual effort by auditors

• Higher accuracy, quality, and consistency than trained manual auditors, as per measurements

• Reduced reliance on time-intensive auditor training for routine checks

• Faster audit throughput and more predictable audit effort for standardized content types

Capability built:

• AI/ML audit framework with criteria, dataset approach, validation process

• Curated training dataset and labeling guidance

• Operational playbook for model-supported auditing with clear workflows, quality checks and escalation paths