Financial Data Exploratory Data Analysis

Introduction

This repository contains an Exploratory Data Analysis (EDA) applied to a financial dataset. The goal of this analysis is to identify patterns, relationships, and insights that can assist in decision-making within financial contexts, such as risk management, fraud detection, and financial behavior forecasting.

Business Problem and Project Objective

This project aims to analyze financial transactions to uncover key insights regarding cash flow, payment behaviors, and geographical distribution of financial operations. The analysis is divided into two main stages: an initial EDA to explore the data and a subsequent visualization phase in Power BI.

Objectives of the Analysis:

• Evaluate financial flows to identify patterns in payments and receipts and assess the company’s financial stability.
• Identify geographical patterns in customer and supplier distribution and analyze their impact on transaction volume.
• Check for delays in payments and their effects on cash flow.
• Examine which banks concentrate the most payments and receipts and assess differences in financial behavior among them.

Although this analysis does not aim to solve a predefined business problem, it seeks to process and interpret existing financial data to generate strategic insights.

Expected Benefits:

• Anticipation of financial issues: Predicting periods of lower liquidity to take corrective actions.
• Identification of seasonality: Detecting cyclical patterns to adjust inventory, promotions, and financial planning.
• Logistics optimization: Understanding the location of key customers and suppliers to reduce transportation costs and improve operational efficiency.
• Strategic expansion: Supporting decision-making for market expansion based on high transaction volumes in specific regions.

Solution Pipeline

The analysis follows a structured pipeline:

1. Data Collection and Preprocessing
   • Importing financial dataset
   • Handling missing values and outliers
2. Exploratory Data Analysis (EDA)
   • Statistical analysis and distribution visualization
   • Correlation and relationship exploration
   • Customer segmentation
3. Anomaly Detection and Risk Assessment
   • Identifying suspicious financial transactions
   • Evaluating financial risk factors
4. Results Interpretation and Business Recommendations
   • Insights derived from the analysis
   • Suggestions for improving financial decision-making

Project Structure

• BaseFinanceiro/: Folder containing the database files.
• AnaliseBaseFinancero.ipynb: Jupyter Notebook containing the exploratory data analysis.
• AnaliseBaseFinanceiro.py: Script used in Jupyter Notebook.
• LICENSE: License terms.
• README.md: Project description.
• requirements.txt: List of project dependencies.

Technologies Used

• Python 3.13.0
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

Exploratory Data Analysis Results

The exploratory data analysis (EDA) revealed several key insights:

1. General Statistics and Distribution

   • Summary statistics provided an overview of numerical data.
   • Visualizations showed trends and anomalies in financial behavior.

2. Correlation Between Variables

   • Identified strong correlations between key financial indicators.
   • Some variables showed minimal impact on financial behavior.

3. Customer Segmentation

   • Cluster analysis grouped customers based on spending behavior.
   • Helped identify high-risk customers or potential premium clients.

4. Anomaly Detection

   • Outliers indicated unusual financial transactions.
   • Potential fraud cases were detected through transaction patterns.

Next Steps and Suggestions

Based on the findings, the following steps can be taken:

• Deep dive into high-risk transactions: Apply machine learning models for fraud detection.
• Enhance customer segmentation: Implement predictive modeling to personalize financial services.
• Monitor financial trends: Use time-series forecasting for future financial predictions.

How to Reproduce the Analysis

1. Clone this repository:

   git clone https://github.com/your-username/EDA_Financial.git

2. Navigate to the project directory:

   cd EDA_Financial

3. Install the dependencies:

   pip install -r requirements.txt

4. Run Jupyter Notebook:

   jupyter notebook

5. Open the notebook and execute the cells step by step.

License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.