Finlytic-Categorize

Finlytic-Categorize is an AI-powered machine learning model developed to automate the categorization of expenses for small and medium-sized enterprises (SMEs). This model is designed to simplify the financial accounting process by classifying business expenses into appropriate tax-related categories, ensuring efficiency, and minimizing errors.

Model Details

• Model Name: Finlytic-Categorize
• Model Type: Expense Categorization
• Framework: TensorFlow, Scikit-learn, Keras
• Dataset: The model is trained on financial transaction data, including diverse business expenses.
• Use Case: Automating the process of categorizing expenses into tax-compliant categories for SMEs in Nepal.
• Hosting: Huggingface model repository (currently used in a locally hosted setup)

Objective

The model is designed to reduce manual effort and the likelihood of human errors when handling large amounts of financial data. By using Finlytic-Categorize, SMEs can easily categorize expenses and maintain accurate records for tax filing.

Model Architecture

The model is based on a pre-trained transformer architecture, fine-tuned specifically for the task of expense categorization. The dataset used for fine-tuning includes annotated financial records with appropriate tax labels.

How to Use

To use the Finlytic-Categorize model locally, follow these steps:

1. Installation: Clone the model repository from Huggingface or use the local model by loading it with Huggingface’s transformers library.

   git clone https://huggingface.co/comethrusws/finlytic-categorize
   

2. Load the Model:

   from transformers import AutoTokenizer, AutoModel
   
   tokenizer = AutoTokenizer.from_pretrained("path_to/finlytic-categorize")
   model = AutoModel.from_pretrained("path_to/finlytic-categorize")
   

3. Input: Feed your financial data (in JSON, CSV, or any structured format). The model expects financial transaction descriptions and amounts.

4. Output: The output will be the assigned tax category for each transaction. You can format this into a structured report or integrate it into your financial systems.

Dataset

The model was trained on financial data with annotations, specifically curated for Nepalese businesses, covering a wide range of common expense types, such as:

• Delivery charges
• Software licenses
• Employee training
• Operational supplies

Evaluation

The model was evaluated using a hold-out validation set and achieved high accuracy in categorizing business expenses. Specific metrics include:

• Accuracy: 94%
• Precision: 91%
• Recall: 89%

Limitations

• The model is tailored for Nepalese SMEs and may require re-training or fine-tuning for different tax laws or regions.
• It is best suited for common expense categories and may not generalize well for very niche or rare expenses.

Future Improvements

• Expand the model's training data to include more diverse financial transactions.
• Fine-tune for region-specific tax categorization, making it more adaptable globally.

Contact

For queries or contributions, reach out to the Finlytic development team at finlyticdevs@gmail.com).