Internal fraud-analysis chatbot
Internal fraud-analysis chatbot combining SQL-based analytics, document embeddings (FAISS), and LLM reasoning. Supports natural-language queries over tabular fraud data and fraud reports, powered by a modular Python agent architecture.
Business Challenge
This project demonstrates a production-ready solution for an AI Engineer challenge focusing on: 1. Accuracy - Precise, factually correct responses to fraud analysis queries 2. Coverage - Adaptability to various management questions across fraud domains 3. Readability - Clean, modular code structure with meaningful naming conventions 4. Exception Handling - Robust error handling for edge cases and invalid data 5. Performance - Efficient async operations and optimized data retrieval 6. Data Processing - Advanced RAG with quality scoring and multi-source integration
Solution Delivered
Core Functionalities: ✅ Hybrid Retrieval Architecture - Combines Supabase (SQL) and Qdrant (vector search) ✅ Multi-Source Integration - Seamlessly merges insights from tabular and document data ✅ Intelligent Routing - Classifies questions and directs to optimal data sources ✅ Quality Scoring - LLM-based evaluation of answer accuracy, relevance, completeness, and clarity ✅ Session Management - Maintains chat history and context across conversations ✅ Async-First Architecture - Non-blocking I/O for all external service calls ✅ Provider Abstraction - Flexible switching between LLM and vector store providers
Outcomes Achieved
The chatbot handles various fraud analysis question categories: 1. Statistical Analysis - Fraud rates, trends, temporal patterns 2. Categorical Analysis - Merchant and category-specific fraud incidence 3. Methodology - Fraud methods, detection techniques 4. Regulatory/Technical - EEA regulations, cross-border fraud, system components 5. Comparative Analysis - Rate differences, value shares, geographic comparisons