Audit Copilot AI: An Intelligent Audit Assistance Platform Based on RAG and OCR

Audit work has always been tedious and time-consuming. Audit Copilot AI aims to revolutionize the efficiency of traditional audits using modern AI technologies. It is an integrated platform combining document OCR, fraud detection, retrieval-augmented question answering (RAG), and audit project management, built with FastAPI, React, and large language models. Positioned as a decision support tool rather than a replacement for auditors, all AI outputs require manual review and it is not yet production-ready.

Traditional audits require processing large volumes of documents, identifying abnormal transactions, and tracking evidence chains—tasks that are time-consuming and prone to omissions. Audit Copilot AI was developed by ahmadbangashdigital-svg and released on GitHub on July 12, 2026. Its tech stack includes FastAPI, React, FAISS, Tesseract, etc. The project has a clear disclaimer: it does not produce formal audit opinions, does not guarantee the accuracy of fraud detection, does not provide legal or tax advice, is a demonstration project, and should not be directly used with real customer data.

Core Features: 1. Identity Authentication (JWT double token + role-based permissions); 2. Audit Project Workspace (data isolation by organization/project); 3. Document Upload & Analysis (supports CSV/PDF/images; Tesseract OCR for text extraction; optional LayoutLMv3 for structured receipt extraction); 4. Fraud Detection (HistGradientBoosting model to score abnormal transactions); 5. RAG Q&A Assistant (general/project-limited modes; FAISS semantic retrieval + Sentence-Transformers embeddings); 6. Audit Findings & Evidence Management (record findings, link evidence, generate reports).

Tech Stack: Backend: FastAPI + SQLAlchemy; AI/ML: FAISS, Tesseract, LayoutLMv3, scikit-learn; Frontend: React + TypeScript + Tailwind CSS.

RAG Implementation: Document splitting → Sentence-Transformers embedding generation → FAISS vector indexing → Query vector retrieval → Answer generation with context. Advantages: data privacy, controllable costs, interpretability.

OCR & Document Understanding: Basic text recognition using Tesseract; optional LayoutLMv3 (requires local model) for structured extraction (e.g., receipt fields); supports multiple document formats.

Fraud Detection: Uses scikit-learn's HistGradientBoostingClassifier, trained on historical data, extracts transaction features (amount, time patterns, etc.) for real-time scoring. CatBoost/Isolation Forest considered earlier but not finally deployed.

The code structure is clearly layered: the backend app includes modules like api, audit, chatbot; frontend is in frontend/src; the test suite is complete: backend has 171+3 passed tests, frontend has 61 passed tests, TypeScript build and production build both pass—reflecting good engineering practices.

Known limitations: Voice function is disabled by default (depends on faster-whisper etc.); LayoutLMv3 requires local model (falls back to rule-based extraction if not available); PaySim dataset and training models are not included in Git; single-node SQLite prototype, not tested under production load. These aspects can be optimized in the future to improve production readiness.

Industry Value: Automating document/transaction processing improves efficiency; RAG accumulates and reuses audit knowledge; AI anomaly detection identifies human omissions; standardized processes reduce human errors.

Conclusion: Audit Copilot AI is an excellent demonstration project showing the potential of AI applications in the audit field. Its architecture and technology selection are reference-worthy. Although not production-ready, it provides a direction for AI + professional services. Final audit judgments still require human professionals.