This project simulates a real-world banking dataset and performs a series of SQL queries to extract valuable insights about customers, transactions, and geographic activity. It showcases my skills in SQL for data analysis, with a focus on financial data and customer segmentation.
To analyze simulated banking data and uncover key insights that can help drive strategic decisions such as:
- Identifying high-value customers
- Understanding transaction behaviors
- Measuring regional banking performance
- Detecting dormant and underutilized accounts
- PostgreSQL (via pgAdmin)
- SQL (CTEs, Joins, Aggregations, Window Functions)
- Data Simulation using PostgreSQL functions
| Phase | Description |
|---|---|
| 1. Data Modeling & Simulation | Created 3 core tables: customers, accounts, transactions using realistic Nigerian names, cities, account structures, and activity patterns. |
| 2. Data Querying | Wrote 12 key SQL queries to extract insights and segment customer/account behavior. |
| 3. Reporting | Documented insights with purpose-driven explanations for each query. |
| No. | Query Title | Purpose |
|---|---|---|
| 1 | Total Spend per Customer | Calculate total amount spent per customer across all accounts. |
| 2 | Salary Trend Analysis | Identify salary credit trends across months. |
| 3 | Most Active Accounts (Count) | Find accounts with the highest number of transactions. |
| 4 | Most Active Accounts (Volume) | Rank accounts by total transaction volume. |
| 5 | Monthly Transaction Breakdown | View total transaction count and volume per month. |
| 6 | Yearly Transaction Breakdown | Track annual transaction growth or decline. |
| 7 | Top 20 High-Value Customers | Rank customers based on credit inflow (e.g., deposits, salary). |
| 8 | Dormant Accounts | Detect customers who’ve had no activity in the last 12 months. |
| 9 | Single Product Customers | Identify customers holding only one account. |
| 10 | Most Used Transaction Services | Discover the most frequent transaction descriptions (e.g., airtime, utility). |
| 11 | City-wise Performance | Measure total volume and number of customers per city. |
| 12 | Engagement by Region | Analyze activity vs dormancy spread across regions. |
Highest Spender by City
Uncovers the top spending customer in each city to help with targeted relationship management and regional performance boosts.
- Built and queried a structured banking dataset from scratch.
- Strengthened core SQL concepts like
JOIN,GROUP BY,FILTER,DATE_TRUNC, andCASE WHEN. - Gained deeper understanding of financial data behaviors — transaction types, account lifecycle, regional engagement.
I'm a data analyst passionate about financial analytics and customer behavior analysis. My goal is to help businesses make informed decisions using data. This project is part of my journey toward mastering financial data analysis and preparing for real-world data roles in banking and fintech.
- LinkedIn: www.linkedin.com/in/durueke-franklin
- Portfolio: https://franklinanalytics.github.io/portfolio