🏦 Data Bank – Customer & Transaction Analysis

SQL-Based Financial Data Analytics Project

The Data Bank project focuses on analyzing customer behavior, account performance, and transactional data to drive better financial insights. Using advanced SQL techniques such as window functions, joins, and time-based aggregations, this project uncovers trends in customer growth, deposits, and engagement across banking services. The analysis supports the bank’s strategic goals of improving retention and optimizing branch performance.

Data Bank SQL Project

Project Highlights

  • Customer Growth Analysis

    Tracked new and existing customer counts using SQL date-based grouping and window ranking logic.

  • Deposit & Withdrawal Trends

    Analyzed daily and monthly transaction activity to understand deposit volumes and withdrawal frequency.

  • Account Performance Insights

    Calculated average balances and high-value account contributions using SQL aggregate and ranking functions.

  • Regional Revenue Analysis

    Compared revenue and transaction counts across different regions to identify top-performing areas.

  • Customer Retention Metrics

    Measured account reactivation and retention rates through SQL-based customer journey tracking.

About the Project

Data Bank Overview

Overview

The Data Bank project simulates a modern financial institution’s database, capturing key details about customers, accounts, and transactions. Using SQL, the project helps understand customer activity, identify dormant accounts, and measure the bank’s overall financial health through structured data insights.

SQL Process & Data Modeling

  • 📥 Extract: Gathered data from customers, accounts, and transactions tables.
  • 🧹 Transform: Cleaned missing or invalid financial records using SQL CASE and NULLIF functions.
  • 🔍 Analyze: Used CTEs and window functions to calculate customer lifetime value and activity frequency.
  • 📊 Model: Built a reporting view linking customers and transactions to account growth and churn metrics.
Data Bank SQL Model
Data Bank Insights

Key Insights

  • 💰 Average customer balance grew by 18% within six months.
  • 📈 Regional performance showed a 25% higher deposit growth in metro areas.
  • 👥 Returning customers contributed 40% more revenue than new accounts.
  • ⚙️ SQL analysis detected inactive accounts for targeted reactivation campaigns.

When:
2025

Mode:
SQL Data Analysis

Dataset:
Customer, Accounts & Transactions Data

Focus:
Banking Performance & Customer Insights

Project Snapshots

Business Impact

📊 Improved financial reporting accuracy through SQL-based data validation.

💬 Helped identify customer churn trends and retention improvement areas.

🚀 Supported better decision-making for regional banking strategies.

Challenges & Learnings

⚙️ Managing large transactional datasets while ensuring SQL performance optimization.

🧩 Implementing complex window functions for time-series balance tracking.

💡 Strengthened expertise in SQL data modeling and financial analytics.

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