Banking AI Assistant - From 50% to 82% Accuracy
Case Study Summary
Client: Wijschool Foundation
Website: wijschool.nl
Industry: Education
Impact Metrics:
- Accuracy leap from 50% to 82% in handling complex banking queries
- Hallucinations reduced by over 70% in financial advice and account information
- Expansion from single pilot to multiple banking clients
- From concept to production in just 2 months
- 100% compliance with financial data security regulation
The Critical Business Problem
A promising stealth fintech startup faced a make-or-break moment:
- Banking AI assistant showing only 50% accuracy in real customer scenarios
- Initial pilot bank considering termination of the partnership
- Hallucinations in financial advice creating potential regulatory concerns
- Leadership questioning product viability in financial services market
Success Probability Reversal (From Likely Failure to 100% Success)
Instead of rushing more features or technical changes, I first implemented:
- Comprehensive evaluation framework mapping all failure points
- Systematic analysis of where the RAG pipeline broke down
- Client-specific testing suite with real banking queries
- Compliance validation protocol for every AI response
This assessment pinpointed exactly why the existing system failed where standard approaches couldn't identify the issues.
Implementation Timeline Advantage (Weeks vs. Potential 6+ Month Rebuild)
The evaluation-driven approach delivered transformative results rapidly:
- Critical retrieval pipeline improvements implemented within 2 weeks
- First accuracy improvements visible in week 3
- Banking-specific guardrails completed by week 6
- Full production readiness achieved in 8 weeks total
No Client Effort (Zero Disruption vs. Typical Pilot Restart)
The solution minimized implementation friction through:
- Zero changes required to banking systems
- No additional data requirements from client
- Maintaining existing API contracts and integrations
- Seamless transition without service interruption
Verified Business Impact (Company-Saving Results)
The approach delivered exceptional return on their initial investment:
- 82% accuracy transformed failed pilot into expansion opportunity
- From imminent failure to multiple successful deployments
- 8-week turnaround vs. months of uncertain redevelopment
- Zero disruption to existing banking client relationships
Result: Company avoided pivot, secured additional funding, and expanded to multiple banking clients.
-
Is your AI project showing early warning signs?
Don't wait until your pilot fails. Let's discuss how systematic evaluation can identify and resolve critical issues before they threaten your implementation.