Revolutionizing Finance: How AI-Powered Document Analyzers Simplify Financial Analysis

---

By Next Solution Lab on 2024-10-23 00:59:00

 

Project Descriptions:

People and businesses often find it difficult to efficiently extract, analyze, and derive useful insights from their financial documents, such as bank statements, tax returns, invoices, and receipts. Manually reviewing and processing these documents is time-consuming, error-prone, and requires a lot of effort. Additionally, concerns about privacy and security arise when sensitive financial information is shared with third-party services or stored on external servers. To address these challenges, there is a need for a private financial document analyzer that uses AI to automate the extraction, analysis, and interpretation of financial data while prioritizing user privacy and data. To do so we have developed an AI-powered Financial document analyzer which will analyze individuals financial documents without compromising the privacy. We have used the RAG pipeline specifically Langchain for connecting our LLM with our own private data.

Key Benefits:

Time-saving and Efficiency: The analyzer automates the process of extracting and analyzing financial data from documents, saving users significant time and effort compared to manual processing.

Accurate and Consistent Analysis: The system is trained on financial language and document structures that can provide accurate and consistent analysis of financial data. The analyzer can identify and extract relevant information from documents with high precision, reducing the chances of human errors or inconsistencies.

Enhanced Privacy and Security: Our system prioritizes user privacy and data security. By processing documents locally on the user's device and implementing encryption and secure storage mechanisms, the analyzer ensures that sensitive financial information remains confidential and protected from unauthorized access.

Fraud Detection and Error Prevention: The analyzer's anomaly detection capabilities can help users identify potential fraud, errors, or discrepancies in their financial documents. By flagging suspicious transactions or inconsistencies, the analyzer assists users in detecting and preventing financial irregularities early on.

Cost-effective Solution: Implementing our Private Financial Document Analyzer it can be a cost-effective solution compared to hiring financial advisors or subscribing to an expensive financial analysis service.

Project Structure:

STATEMENT_ANALYSIS/
│
├── DB
│   ├── croma.sqlite3
│   └──File_embedding (Random number)
|
├── models
│   └──AdaptLLM--finance-chat (can download from huggingface)
|
├── screenshot
|   ├── 1.png
|   ├── 2.png
|   └── 3.png
|
├── SOURCE_DOCUMENTS
│   └──Uploaded_private_document
|
├── constants.py
|
├── environment.yml
|
├── ingest.py
|
├── load_models.py
|
├── prompt_template_utils.py
|
├── README.md
|
├── run_localGPT.py
|
├── UI.py
|
└── Utils.py

Environment Setup:

To run the project Python 3.10 or later need to be install. Earlier versions of Python will not compile.

conda create env -f environment.yml
conda activate Fgpt

If you encounter an error while building a wheel during the pip install process, you may need to install a C++ compiler on your computer.

UI Interface Introduction:

UI contains two Options.

• Chat with LLM: Interact with the LLM without uploading documents. It responds based on prior knowledge.
• Chat With Document: Upload any document (PDF, Excel, TXT, etc.), and a chat interface will open to interact with your document. Run the UI with:

streamlit run UI.py

Technical Details:

By selecting the right local models and the power of LangChain you can run the entire RAG pipeline locally, without any data leaving your environment, and with reasonable performance.

ingest.py Ingest documents from a directory, including handling various file types. Parallelize document processing for efficiency. Split documents into manageable chunks for further processing. Generate embeddings for each document using a machine-learning model. Store these embeddings in a Chroma vector store, enabling persistent document retrieval.

constants.py set up an environment for document ingestion, processing, and persistence using a combination of document loaders, Chroma settings, and model configurations. In other words, The script is designed to ingest and process various document formats efficiently, store the resulting data persistently using ChromaDB, and configure the environment for further analysis or machine learning tasks, especially in finance.

UI.py The code creates a Streamlit-based web application where users can interact with financial documents and ask questions about them. It uses retrieval-based QA, which combines document embeddings and a language model to return relevant answers, and it is optimized for different devices (CPU or GPU) and model configurations (quantized or full versions).

Screenshots:

Chatting with the LLM

Uploading a Private Document

Chatting with Uploaded Document