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---
language: en
license: mit
tags:
- tax-compliance
- financial-compliance
- machine-learning
- tax-regulations
model-index:
- name: Finlytic-Compliance
results:
- task:
type: compliance-check
dataset:
name: finlytic-compliance-data
type: financial-transactions
metrics:
- name: Accuracy
type: accuracy
value: 86.00
- name: Precision
type: precision
value: 90.00
- name: Recall
type: recall
value: 82.00
- name: F1-Score
type: f1
value: 89.00
source:
name: Internal Evaluation
url: https://huggingface.co/comethrusws/finlytic-compliance
---
# 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.
```bash
git clone https://huggingface.co/comethrusws/finlytic-compliance
```
2. **Load the Model**:
```python
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 [[email protected]](mailto:[email protected]). |