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--- |
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language: en |
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license: mit |
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tags: |
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- tax-compliance |
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- financial-compliance |
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- machine-learning |
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- tax-regulations |
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model-index: |
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- name: Finlytic-Compliance |
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results: |
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- task: |
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type: compliance-check |
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dataset: |
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name: finlytic-compliance-data |
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type: financial-transactions |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 86.00 |
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- name: Precision |
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type: precision |
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value: 90.00 |
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- name: Recall |
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type: recall |
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value: 82.00 |
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- name: F1-Score |
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type: f1 |
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value: 89.00 |
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source: |
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name: Internal Evaluation |
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url: https://huggingface.co/comethrusws/finlytic-compliance |
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--- |
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# Finlytic-Compliance |
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**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. |
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## Model Details |
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- **Model Name**: Finlytic-Compliance |
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- **Model Type**: Compliance Check |
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- **Framework**: TensorFlow, Scikit-learn, Keras |
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- **Dataset**: The model is trained on financial transactions labeled for tax compliance. |
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- **Use Case**: Automating the detection of tax compliance issues for Nepalese SMEs. |
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- **Hosting**: Huggingface model repository (locally used) |
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## Objective |
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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. |
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## Model Architecture |
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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. |
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## How to Use |
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1. **Installation**: Clone the model repository from Huggingface or load the model locally. |
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```bash |
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git clone https://huggingface.co/comethrusws/finlytic-compliance |
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``` |
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2. **Load the Model**: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("path_to/finlytic-compliance") |
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model = AutoModel.from_pretrained("path_to/finlytic-compliance") |
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``` |
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3. **Input**: Feed the model financial transactions (structured in JSON or CSV format). The model will process these transactions and check for compliance issues. |
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4. **Output**: The output will indicate whether a transaction is compliant with tax regulations and provide additional insights if necessary. |
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## Dataset |
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The model was trained using annotated financial records, with transactions labeled as either compliant or non-compliant with Nepalese tax laws. |
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## Evaluation |
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The model was evaluated using a hold-out test dataset. The performance metrics are as follows: |
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- **Accuracy**: 92% |
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- **Precision**: 90% |
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- **Recall**: 88% |
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- **F1-Score**: 89% |
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These results indicate that the model is highly effective in flagging non-compliant transactions and ensuring financial records are accurate. |
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## Limitations |
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- The model is designed for Nepalese tax laws, so it may need adjustments for different regulatory frameworks. |
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- It is best suited for common financial transactions and may not generalize well for edge cases. |
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## Future Improvements |
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- Expanding the dataset to cover more complex financial scenarios. |
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- Adapting the model to work with tax regulations from other countries. |
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## Contact |
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For queries or contributions, reach out to the Finlytic development team at [[email protected]](mailto:[email protected]). |
