Industry
AI consulting for banking and financial services
Supervisory attention has moved from whether a model is used to whether its use can be evidenced. In practice that shifts the cost of an AI programme from build to assurance, and it makes the eval harness and the audit trail part of the first release rather than a later hardening project.
- Start with
- AI Readiness Diagnostic
Workflows that carry evidence quickly
- Credit file preparation and covenant extractionAnalyst hours per file, measured across a full review cycle
- KYC and onboarding document review, with reviewer checkpointsStraight-through rate and rework rate per hundred cases
- Complaint classification and routingFirst-touch routing accuracy and time to owner
- Reconciliation exception triageExceptions cleared per analyst day, at equal accuracy
What we advise against starting with
Final credit decisioning without a reviewer
The explanation burden exceeds the saving, and the exception path becomes the whole job.
Unreviewed customer-facing advice
Suitability obligations do not tolerate a generated answer with no accountable adviser.
Fraud rules replacement in one step
Existing rules encode institutional memory that no evaluation set fully captures.
Systems
What we integrate with in this sector
- Core banking platforms
- Loan origination systems
- AML and transaction monitoring
- Data warehouse and reporting layers
- Case management
Constraints
Design inputs, not afterthoughts
- Model risk governance expectations, including documented validation and ongoing monitoring
- Data residency and outsourcing rules for regulated workloads
- Retention and audit evidence obligations that outlive the pilot team
- Customer data protection obligations under India's DPDP framework and equivalent US state law
Use cases
Anonymised workflows from this sector
FAQ
Questions about AI in banking and financial services
- Can models be used in regulated decisions?
- Usually as a preparatory or classification step with a named reviewer, not as the decision itself. The design question is where the reviewer sits, not whether one exists.
- How do you handle model validation?
- An evaluation set built from real cases including the hard tail, a documented quality bar per decision type, and monitoring for drift and override rate.
Other sectors
Where else we work
Next step
Start where the evidence arrives fastest in banking and financial services
Bring one workflow and the constraint you think blocks it. We will tell you on the first call whether it is a good first case.