# <div align="center">ABANCA – Banking Statement Analysis & Automation Platform</div>

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## <div align="center">Overview</div>

ABANCA is a Python-based financial automation and banking statement analysis platform developed to process, classify and visualize banking transaction data automatically.

The project focuses on automating financial workflows through:

- Excel data processing
- Banking transaction normalization
- Financial classification
- KPI generation
- Automated reporting
- Data visualization

The notebook combines data science techniques with financial process automation to simplify banking reconciliation and financial monitoring operations. :contentReference[oaicite:0]{index=0}

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# <div align="center">Main Features</div>

## Banking Statement Processing

- Import banking statement files
- Automatic data normalization
- Transaction categorization
- Financial movement processing
- Date formatting automation

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## Financial Classification

The system automatically classifies:

- Debit transactions
- Credit transactions
- Banking movements
- Financial operations
- Account balances

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## Data Cleaning & Transformation

- Null value handling
- Currency normalization
- Column standardization
- Duplicate detection
- Invalid entry filtering

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## Financial Reporting

- Summary generation
- KPI calculations
- Transaction statistics
- Balance analysis
- Aggregated reporting

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# <div align="center">Technologies Used</div>

| Technology | Purpose |
|:---:|:---:|
| Python | Main programming language |
| Pandas | Financial data processing |
| NumPy | Numerical operations |
| Matplotlib | Data visualization |
| OpenPyXL | Excel compatibility |
| Jupyter Notebook | Interactive development environment |

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# <div align="center">Project Structure</div>

```txt
.
├── ABANCA.ipynb
├── Input_Files/
├── Output_Reports/
├── Charts/
└── README.md
```

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# <div align="center">Requirements</div>

## Python Version

```txt
Python 3.10+
```

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# <div align="center">Required Libraries</div>

Install dependencies:

```bash
pip install pandas numpy matplotlib openpyxl
```

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# <div align="center">Running the Application</div>

Launch Jupyter Notebook:

```bash
jupyter notebook
```

Open:

```txt
ABANCA.ipynb
```

Execute all notebook cells sequentially.

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# <div align="center">Supported Input Formats</div>

| Format | Description |
|:---:|:---:|
| `.xlsx` | Excel banking statements |
| `.csv` | Transaction exports |
| `.xls` | Legacy Excel statements |

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# <div align="center">Main Functionalities</div>

## Transaction Analysis

The system processes:

- Transaction dates
- Descriptions
- References
- Debit values
- Credit values
- Running balances

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## Financial Aggregation

The notebook automatically generates:

- Daily summaries
- Monthly summaries
- Account movement analysis
- Transaction frequency statistics
- Financial totals

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## Data Visualization

The project supports:

- Financial charts
- Transaction trend analysis
- KPI dashboards
- Summary visualizations

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# <div align="center">Workflow</div>

```txt
Bank Statement Import
          ↓
Data Cleaning
          ↓
Normalization
          ↓
Transaction Classification
          ↓
Financial Aggregation
          ↓
KPI Calculation
          ↓
Report Generation
          ↓
Visualization
```

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# <div align="center">Generated Output</div>

The system can generate:

- Cleaned Excel files
- Financial summaries
- Transaction reports
- KPI analysis
- Graphical dashboards

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# <div align="center">Data Processing Capabilities</div>

## Cleaning Operations

- Empty row removal
- Date parsing
- Numeric conversion
- Currency formatting
- Invalid record filtering

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## Analysis Operations

- Movement counting
- Total debit calculation
- Total credit calculation
- Net balance computation
- Financial trend analysis

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# <div align="center">Intended Use Cases</div>

- Banking reconciliation
- Treasury analysis
- Financial auditing
- Banking statement normalization
- Financial KPI reporting
- Accounting support
- Financial automation workflows

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# <div align="center">Notebook Features</div>

The notebook environment allows:

- Interactive analysis
- Dynamic filtering
- Incremental execution
- Rapid financial experimentation
- Visualization customization

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# <div align="center">Future Improvements</div>

- PDF report export
- Power BI integration
- Database integration
- Real-time banking synchronization
- AI-assisted financial anomaly detection
- Automated reconciliation engine
- Web dashboard interface

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# <div align="center">Security Notes</div>

This project may process sensitive financial data.

Users should:

- Protect banking files
- Avoid sharing sensitive exports
- Secure generated reports
- Validate all processed outputs before operational usage

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# <div align="center">Disclaimer</div>

This project was developed for financial automation and analytical purposes.

Users remain responsible for validating all financial calculations and generated reports before official or accounting usage.

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# <div align="center">Author</div>

José Garcia  
Data Scientist  
Process Digitalization & Automation