Fintech Building accurate fintech chatbots with evaluation & observability
A fintech platform used Future AGI to reduce chatbot errors by 25% and boost first-contact resolution by 15% for financial queries.
Key Results
The effectiveness of fintech chatbots hinges on rigorous evaluation. Future AGI helped us transform an underperforming system into an intelligent, reliable assistant.
Use Cases
The Challenge
Fintech chatbots face uniquely high stakes. Inaccurate responses about account balances, financial products, or outdated policy information can erode trust and create compliance risks.
Key problems identified:
- Incorrect information - Chatbots providing wrong account balances, inaccurate product details, or outdated policy information
- Generic responses - Systems struggling with complex financial queries, offering cookie-cutter answers
- Failed resolution - Users experiencing failures requiring escalation to human agents
- No personalization - Lack of tailored financial guidance despite promises
The CNET cautionary tale loomed large-they published 77 finance stories using AI that were riddled with factual errors and mathematical mistakes, destroying credibility.
The Solution
Future AGI’s evaluation platform assessed the fintech chatbot’s RAG system using six core metrics:
1. Context Relevance
Ensuring the chatbot works with the right information, dramatically improving accuracy and helpfulness of responses.
2. RAG Ranking
Optimizing the retrieval process so the chatbot accesses the most relevant information first from the knowledge base.
3. Factual Accuracy
Building trust by ensuring provided information is correct and reliable, preventing the spread of financial misinformation.
4. Completeness & Groundedness
Guaranteeing thorough, evidence-based answers leading to greater user satisfaction and reducing follow-up queries.
5. Tone Analysis
Maintaining brand consistency and fostering positive customer interactions through appropriate, engaging communication.
6. Knowledge Base Quality
Highlighting issues in internal knowledge bases that caused downstream retrieval failures.
The Results
- 25% reduction in errors for responses related to account fees and transfer limits
- 15% increase in first-contact resolution rate through better query understanding
- 10% increase in positive customer feedback after tone refinements
- 20% faster chatbot update development cycles through data-driven optimization
Want similar results?
Start building reliable AI systems with Future AGI today.