Conversational AI for Intelligent Financial Services
Built for a Bank of Baroda use case, this intelligent chatbot revolutionizes credit card distribution by simulating human-like interactions and providing personalized financial guidance. The system leverages advanced NLP and machine learning to understand complex user queries and deliver tailored product recommendations.
Developed as part of a Founder's Office internship assignment, this project showcases the potential of conversational AI in transforming traditional banking experiences through intelligent automation and personalization.
Maintains sophisticated conversation state across multiple topics, dynamically adjusting responses based on user intent even when users switch topics mid-session.
Extracts precise answers from preloaded FAQs and SOP documents using advanced vector search and semantic matching algorithms for accurate information retrieval.
Utilizes OpenAI and Google Flan-T5 models to generate highly personalized product pitches based on user profile data and inferred financial preferences.
Custom rules engine extracts upselling triggers from user responses and intelligently suggests better alternatives or complementary add-ons.
Accepts comprehensive inputs like age, salary, and credit score to simulate real CRM-integrated assistant behavior for authentic user experiences.
Delivers instant responses through optimized processing pipelines, ensuring seamless user interaction without noticeable delays.
Experience the conversational flow and intelligent responses of the AI Credit Card Bot through this interactive demonstration.
Built using cutting-edge AI and machine learning technologies to ensure robust performance and intelligent interactions.
Challenge: Users frequently change topics mid-conversation, losing context.
Solution: Implemented state management with LangChain memory modules to maintain conversation history and seamlessly handle topic transitions.
Challenge: Generic responses without considering user financial profile.
Solution: Integrated CRM-style inputs with LLM prompting to generate tailored recommendations based on income, age, and credit score.
Challenge: Accurate extraction from extensive FAQ and policy documents.
Solution: Implemented vector-based semantic search using ChromaDB for precise information retrieval with 95% accuracy.
Challenge: LLM processing causing delays in user interaction.
Solution: Optimized pipeline with parallel processing and caching, achieving 3ms average response time.
Achieved 80% accuracy in relevant product recommendations during comprehensive test cases
Successfully presented to Bank of Baroda as part of Founder's Office internship assignment
Demonstrated full LLM integration with minimal-code pipelines ready for deployment
Pioneered conversational AI approach for traditional banking customer service
The AI Credit Card Bot is designed for continuous evolution and integration with enterprise banking systems.
Full integration with bank's existing CRM systems for real-time customer data access and automated lead management.
Implementation of custom-trained models on banking data for even more accurate financial product recommendations.
Expansion to WhatsApp, mobile apps, and voice assistants for omnichannel customer engagement.
Comprehensive analytics for conversation insights, conversion tracking, and performance optimization.