AI Banking Assistant
Conversational AI Platform for Digital Banking
Enterprise conversational AI handling 80% of customer inquiries for a digital bank — from balance checks and transaction disputes to loan applications and compliance questions.
Queries Resolved by AI
Avg Response Time (was 12min)
Support Cost Reduction
Customer Satisfaction
5 months
7 (2 ML Engineers + 3 Backend + 1 Frontend + 1 QA)
Fintech
Web App
// About the Project
Project Overview
A digital-first bank serving 500K+ customers was scaling rapidly but customer support couldn't keep pace. Average wait times exceeded 12 minutes, CSAT scores were declining, and hiring enough agents for 24/7 multilingual support was economically unsustainable.
UppLabs built a conversational AI platform powered by fine-tuned LLMs with retrieval-augmented generation (RAG) over the bank's knowledge base, policy documents, and product catalog. The system handles account inquiries, transaction disputes, loan pre-qualification, and card management — escalating to human agents only for complex cases requiring judgment.
Security was paramount: the platform operates within the bank's VPC, uses tokenized PII handling, and maintains SOC2-compliant audit logs. Multi-language support (English, Spanish, Portuguese) was achieved through a single multilingual model rather than separate per-language deployments.
// Product
What We Built
A digital-first bank serving 500K+ customers was scaling rapidly but customer support couldn't keep pace. Average wait times exceeded 12 minutes, CSAT scores were declining, and hiring enough agents for 24/7 multilingual support was economically unsustainable.
UppLabs built a conversational AI platform powered by fine-tuned LLMs with retrieval-augmented generation (RAG) over the bank's knowledge base, policy documents, and product catalog. The system handles account inquiries, transaction disputes, loan pre-qualification, and card management — escalating to human agents only for complex cases requiring judgment.
Services Provided
Technology Stack
Banking chat interface with balance checks
AI resolution rates and sentiment analytics
// Challenges
Problems We Solved
Banking Compliance
AI responses about financial products must comply with TILA, ECOA, and state-specific regulations. Hallucinations or incorrect rate quotes could create legal liability.
Secure Data Access
Chatbot needs real-time access to account balances, transactions, and loan status without exposing PII in logs or training data. Required zero-trust data architecture.
Intent Accuracy at Scale
Banking queries span 200+ intent categories from simple balance checks to complex dispute resolution. Misrouting a fraud claim to general inquiries could cause real harm.
Seamless Human Handoff
When AI reaches its limits, the transition to human agents must include full conversation context, customer sentiment analysis, and recommended resolution paths.
// Solutions
How We Delivered
RAG-Powered Financial AI
Fine-tuned LLM with retrieval-augmented generation over 10,000+ policy documents, FAQs, and product specs. Grounded responses with source citations eliminate hallucination risk.
Tokenized Data Pipeline
Customer data accessed through tokenized API layer — chatbot sees masked account numbers and receives pre-formatted responses. No PII in model context or logs.
Hierarchical Intent Classification
Two-stage classification: broad category (accounts, cards, loans, disputes) then specific intent. 97.5% accuracy with automatic escalation for ambiguous queries.
Context-Aware Handoff
AI generates conversation summary, customer sentiment score, and recommended actions before transferring to agents. Reduces agent handle time by 40%.
// Tech Stack
Technologies Used
// Services
What We Provided
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