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:
Installation: Clone the model repository from Huggingface or use the local model by loading it with Huggingface’s transformers library.
Input: Feed your financial data (in JSON, CSV, or any structured format). The model expects financial transaction descriptions and amounts.
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).
