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How to analyze your finances using AI locally

The ability effectively managing personal finances is more crucial than ever. With the abundance of financial data generated through various transactions, it can be overwhelming to keep track of expenses and make informed financial decisions. Fortunately, the advent of  artificial intelligence (AI) and open source  locally installed local large language models (LLMs) has transformed the way we analyze our finances, offering a secure and efficient solution for expense classification and visualization. Thu Vu data analytics has created a great video tutorial on how to install artificial intelligence locally to analyze your financial data easily and efficiently. Providing you with important insights into your spending, debts and savings.

Analyzing Your Finances With AI

A thorough review of your bank transactions is essential for effective personal finance management. By categorizing expenses into predefined categories, you can gain valuable insights into your spending habits and identify areas where you can optimize your budget. However, manually sorting through numerous transactions can be a daunting and error-prone task. This is where locally installed AI models come into play, automating the process and providing accurate expense classification.

  • Time-saving and efficient: Local AI models can quickly process large volumes of financial data, saving you valuable time and effort.
  • Accurate expense categorization: With their advanced natural language processing capabilities, local LLMs can accurately classify transactions into relevant categories.
  • Insights and patterns: By analyzing categorized expenses, you can uncover spending patterns and make data-driven financial decisions.

Ensuring Data Security with Local LLMs

AI personal finances dashboard

One of the primary concerns when it comes to personal finance analysis is data security. Uploading sensitive financial information to online services can expose your data to potential breaches and unauthorized access. Local AI models provide a secure alternative by allowing you to run the analysis on your own device, ensuring that your data remains confidential and under your control.

  • Data privacy: By running AI models locally, you eliminate the need to share your financial data with third-party services, reducing the risk of data breaches.
  • Secure processing: Local AI models perform the analysis on your device, ensuring that your sensitive information never leaves your machine.
  • Peace of mind: With local LLMs, you can have confidence in the security of your financial data, knowing that it remains under your control.

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Getting Started with Local LLMs

To begin analyzing your finances with local LLMs, you’ll need to install and run the models on your local machine. Open-source LLMs like Llama 2 have gained popularity for their performance and accessibility. Here’s a step-by-step guide to get you started:

1. Download the LLM: Obtain the Llama model of your choice from a trusted source, ensuring that you have the necessary permissions and licenses.
2. Install dependencies: Make sure you have Python and the required libraries installed on your machine. Frameworks like Llama CPP and GPT can simplify the installation process.
3. Run the model: Use the provided frameworks and instructions to execute the Llama 2 model locally on your device.

Accurate Expense Categorization

Once you have the local LLM up and running, you can leverage its capabilities to classify your bank transactions into predefined categories. Models like Mistral and Llama 2 have demonstrated high accuracy in expense categorization tasks. By comparing their performance, you can select the model that best suits your specific requirements.

  • Predefined categories: AI can classify transactions into common categories such as groceries, utilities, entertainment, and more.
  • Customization options: You can tailor the categorization process to your specific needs by creating custom categories or modifying existing ones.
  • Efficient processing: Artificial intelligence LLMs can handle large datasets efficiently by optimizing token limits, ensuring smooth performance even with extensive financial data.

Visualizing Your Financial Data

Visualizing your categorized financial data is a powerful way to gain insights into your spending patterns and make informed decisions. Python libraries like Plotly Express and Panel provide user-friendly tools for creating interactive visualizations and dashboards.

  • Income and expense breakdowns: Visualize the distribution of your income and expenses across different categories to understand where your money is going.
  • Monthly trends: Track your financial trends over time, identifying months with higher or lower spending in specific categories.
  • Interactive exploration: Use interactive dashboards to drill down into specific categories, view transaction details, and gain deeper insights.

Practical Implementation Steps

To get started with analyzing your finances using local AI models, follow these practical steps:

1. Set up your environment: Install Python and the necessary libraries on your machine, ensuring compatibility with the LLM framework you choose.
2. Download and set up the LLM: Obtain your chosen Llama AI model and follow the provided instructions to set it up on your local machine.
3. Run the analysis: Use the code examples and guidelines provided in the GitHub repository to classify your bank transactions using the local LLM.
4. Visualize your data: Use Plotly Express and Panel to create interactive visualizations and dashboards, gaining valuable insights into your financial data.

Refer to the GitHub repository for detailed code examples, step-by-step instructions, and additional resources to help you along the way.

The Future of Local LLMs in Personal Finance

The potential for local LLMs in personal finance analysis is immense. As technology continues to advance, these models will become even more accessible and efficient, empowering individuals to take control of their financial well-being. By exploring and experimenting with open-source language models, you can stay at the forefront of personal finance analysis and make data-driven decisions to optimize your financial health.

Local LLMs offer a secure, efficient, and insightful approach to managing your personal finances. By leveraging the power of these models, you can gain a deeper understanding of your spending habits, identify areas for improvement, and make informed financial decisions. Embrace the future of personal finance analysis with local LLMs and take control of your financial well-being today.

Video Credit: Thu Vu data analytics

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