Banking Product Recommendation System Using RAG

Project Snapshot

• Industry: Banking & Financial Services

• Client Type: Financial Institutions / Enterprise Banking

• Duration: Multi-phase deployment

• Deployment Model: Cloud-native APIs (AWS EC2)

• Technologies: Python, Django, Django REST, BM25, LangChain, OpenAI LLM, Hybrid Search, Docker, Nginx, Gunicorn

The Challenge

Banks and financial institutions face increasing demand for personalized product recommendations to improve customer experience and boost cross-sell opportunities. Key challenges included:

• Handling large volumes of unstructured financial product data

• Building a search and recommendation engine that provides both semantic and keyword-optimized results

• Creating an LLM-powered recommendation system that adapts to customer preferences dynamically

• Ensuring scalable, API-based deployment for enterprise-grade usage

Our Solution

We designed and implemented an AI-powered Banking Product Recommendation System using Retrieval-Augmented Generation (RAG) architecture:

• Automated Data Acquisition

  • Built a web scraper with LangChain to collect product details from banking websites and repositories

• Hybrid Search Implementation

  • Integrated BM25 (lexical search) with vector embeddings for hybrid search, balancing precision and semantic understanding

• LLM-Powered Recommendations

  • Stored embeddings in a vector database and optimized recommendations with custom Chain-of-Thought prompts for context-aware outputs

• API & Deployment

  • Created scalable REST APIs with Django REST Framework

  • Configured deployment on AWS EC2, using Nginx + Gunicorn + Docker for reliability and scalability

The Impact

The solution empowered banking institutions with:

• Highly Personalized Recommendations → Improved customer satisfaction and engagement with tailored product suggestions

• Hybrid Search Accuracy → Balanced semantic and keyword-based search for optimal retrieval performance

• Seamless Integration → APIs enabled easy integration into mobile apps, web portals, and CRM systems

• Enterprise Scalability → AWS, Docker, and Nginx ensured robust, production-ready deployment

Our Role

We partnered with the client to:

• Build and integrate RAG architecture for banking product recommendations

• Engineer data scraping and hybrid search pipelines

• Optimize LLM responses with custom prompting

• Deploy a scalable, cloud-native solution with enterprise-ready APIs

Client Testimonial

“The AI-powered recommendation engine transformed how we deliver banking products to our customers. With precise, personalized insights and scalable deployment, the system added tremendous value to our digital banking strategy.”
— VP of Digital Banking Solutions