mubarek4566/Intelligent-Complaint-Analysis

This repository contains the codebase and project structure for building an AI-powered complaint analysis tool for CrediTrust Financial, a digital finance company operating in East Africa. # Intelligent-Complaint-Analysis This repository contains the codebase and project structure for building an AI-powered complaint analysis tool for CrediTrust Financial, a digital finance company operating in East Africa. The system empowers internal teams—Product, Support, Compliance, and Executives—to ask natural language questions and receive evidence-backed summaries from real customer complaints, enabling: ● Faster trend identification ● Proactive issue resolution ● Enhanced decision-making across 5 key financial products: ● Credit Cards ● Personal Loans ● Buy Now, Pay Later (BNPL) ● Savings Accounts ● Money Transfers ## 🔑 Key Features ● Plain-English question answering from complaint data ● Semantic search with FAISS or ChromaDB ● LLM-generated responses backed by retrieved complaint narratives ● Real-time analysis across product categories ● Semantic search over complaint chunks ● Retrieve top relevant complaint texts based on user queries ● Generate natural-language answers using a local LLM (e.g., facebook/opt-1.3b) ● Dashboard or chatbot interface for internal users ## 📊 Project Goals & KPIs ● Reduce time to identify major complaint trends from days to minutes ● Enable non-technical teams to self-serve insights ● Shift from reactive to proactive issue management ## ⚙️ Technologies Used ● Python, LangChain, FAISS/ChromaDB ● OpenAI/GPT/LLM APIs ● Streamlit (for UI) or Chatbot integration (e.g., Rasa, Gradio) ● Pandas, spaCy, scikit-learn (for preprocessing and analytics) ● SentenceTransformers for embedding complaint texts ● FAISS for fast similarity search ● transformers (LLM) for generating context-aware answers 🧠 LLM Pipeline: Embeddings generated using "all-MiniLM-L6-v2" Local language model used for answer generation (can be swapped for API-based or smaller models)