Finlytic-Compliance

Finlytic-Compliance is an AI-driven model built to automate the task of ensuring financial transactions meet regulatory tax requirements. It helps SMEs remain compliant with tax laws in Nepal by constantly monitoring financial records.

Model Details

• Model Name: Finlytic-Compliance
• Model Type: Compliance Check
• Framework: TensorFlow, Scikit-learn, Keras
• Dataset: The model is trained on financial transactions labeled for tax compliance.
• Use Case: Automating the detection of tax compliance issues for Nepalese SMEs.
• Hosting: Huggingface model repository (locally used)

Objective

The model reduces the need for manual checking and reliance on tax consultants by automatically flagging transactions that do not comply with Nepalese tax laws.

Model Architecture

The model is built on a transformer architecture, fine-tuned specifically for identifying compliance issues in financial transactions. It has been trained on a dataset of transactions with known compliance statuses.

How to Use

1. Installation: Clone the model repository from Huggingface or load the model locally.

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

2. Load the Model:

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

3. Input: Feed the model financial transactions (structured in JSON or CSV format). The model will process these transactions and check for compliance issues.

4. Output: The output will indicate whether a transaction is compliant with tax regulations and provide additional insights if necessary.

Dataset

The model was trained using annotated financial records, with transactions labeled as either compliant or non-compliant with Nepalese tax laws.

Evaluation

The model was evaluated using a hold-out test dataset. The performance metrics are as follows:

• Accuracy: 92%
• Precision: 90%
• Recall: 88%
• F1-Score: 89%

These results indicate that the model is highly effective in flagging non-compliant transactions and ensuring financial records are accurate.

Limitations

• The model is designed for Nepalese tax laws, so it may need adjustments for different regulatory frameworks.
• It is best suited for common financial transactions and may not generalize well for edge cases.

Future Improvements

• Expanding the dataset to cover more complex financial scenarios.
• Adapting the model to work with tax regulations from other countries.

Contact

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