An end-to-end Banking Analytics Dashboard built using Power BI, focusing on customer demographics, financial health, transaction behavior, and card-level insights. This project demonstrates data modeling, DAX, slicers, KPI design, and business storytelling aligned with real-world banking and financial analytics use cases.
The dashboard provides a 360° analytical view of banking customers, enabling stakeholders to:
- Understand customer demographics and risk composition
- Analyze financial health using debt and credit indicators
- Monitor transaction performance and trends over time
- Evaluate card usage and credit limit distribution
- The dashboard is structured into four analytical pages, each answering a specific business question
Objective: Understand who the customers are
Key insights:
- Total clients, average age, average income, per capita income
- Gender distribution
- Age group and income group distribution
- Credit score categories
- Risk category composition
Objective: Assess how financially stable customers are
Key insights:
- Debt distribution across age groups
- Credit score vs debt comparison
- Identification of high-risk customers
- Snapshot-based debt analysis (current state)
Note: Financial health visuals represent a snapshot view due to the absence of a time dimension in debt data.
Objective: Analyze customer behavior
Key insights:
- Total transaction amount and count
- Transaction trends by year
- Pass vs fail transaction analysis
- Dynamic metric toggle (amount vs count)
- Conditional formatting and tooltips for deeper insights
Objective: Evaluate card-level performance
Key insights:
- Credit limit distribution
- Card usage patterns
- Card-wise transaction behavior
- Product-level analytical perspective
- Power BI Desktop
- DAX (Measures, KPIs, conditional logic)
- Data Modeling (Star schema, relationship management)
- Power Query (Data cleaning & transformation)
- Interactive Features (Slicers, bookmarks, tooltips)
Source: Publicly available banking-style dataset adapted for analytical practice
Important Note:
- The dataset is used strictly for learning and portfolio demonstration
- It does not represent real customer or bank data
- All values are anonymized / simulated
- No confidential or proprietary information is included
This ensures the project is ethical, legal, and safe to showcase publicly.
- Business-oriented dashboard design
- KPI selection and metric storytelling
- Handling inactive & ambiguous relationships
- Snapshot vs time-series analysis
- User-friendly navigation and UX design
This dashboard is suitable for:
- Data Analyst portfolios
- Banking & Financial Analytics case studies
- Risk & Credit analysis demonstrations
- Power BI interview discussions
Akhilesh Aspiring Data Analyst | Finance & Risk Analytics Enthusiast