Credit Card Transaction Power BI Dashboard
The Credit Card Financial Dashboard is a Power BI report designed to analyze credit card transactions, financial trends, and key performance indicators (KPIs). This dashboard helps financial analysts and business stakeholders gain insights into customer spending behaviors, revenue generation, and financial health through interactive visualizations and detailed data analysis.
The Power BI file consists of multiple report pages, each dedicated to a specific aspect of financial analysis:
- Provides a high-level summary of key financial metrics.
- Displays total transactions, revenue, and customer engagement metrics.
- Features KPIs such as average transaction value, total active customers, and monthly revenue trends.
- Examines trends in credit card transactions over different time periods (daily, weekly, monthly, yearly).
- Categorizes transactions into different types such as online purchases, in-store transactions, bill payments, and cash withdrawals.
- Provides visual comparisons between transaction categories and their respective contributions to revenue.
- Segments customers based on their spending habits and credit utilization.
- Identifies high-value customers and their contribution to overall revenue.
- Analyzes customer retention and churn rates.
- Includes filters for customer demographics, location, and transaction frequency.
- Breaks down revenue sources by transaction type, customer segment, and time period.
- Provides profitability insights through margin analysis.
- Highlights the impact of different revenue streams on overall financial performance.
- Includes visualizations such as revenue distribution charts and heatmaps.
- Identifies anomalies and suspicious transactions that could indicate fraud.
- Utilizes pattern recognition techniques to detect potential fraudulent activities.
- Provides risk assessment models for monitoring credit usage and potential defaults.
- Suggests preventive measures to mitigate financial risks.
- The dataset consists of multiple sources, including:
- Customer transactions from financial institutions.
- Demographic details for customer profiling.
- Financial statements and historical transaction records.
- Market data for external benchmarking.
- Identification of top-performing credit card categories and transaction types.
- Customer segmentation based on spending behavior and credit usage.
- Seasonal trends in credit card transactions, helping in demand forecasting.
- Detection of fraudulent transactions to minimize financial losses.
- Revenue trends highlighting growth opportunities and areas of improvement.
- Power BI: Used for data visualization, interactive dashboards, and reporting.
- DAX (Data Analysis Expressions): Employed for custom calculations, KPIs, and aggregations.
- SQL: Used for data extraction, transformation, and loading (ETL) processes.
- Excel/CSV: Data sources for financial transactions and supplementary datasets.
- Real-Time Data Integration: Connecting to live transaction data for up-to-date analysis.
- Machine Learning for Fraud Detection: Implementing AI-based models for identifying fraudulent transactions.
- Predictive Analytics: Forecasting future customer spending trends and revenue growth.
- Enhanced Customer Segmentation: Using clustering techniques to improve marketing strategies.
- Drill-Down Capabilities: Enhancing interactive features for deeper data exploration.
- Mobile-Friendly Reports: Optimizing dashboards for accessibility on mobile devices.
