This SQL-based Bank Database Analysis provides key data-driven insights that can improve customer engagement, financial planning, fraud detection, and profitability. Below are quantifiable results that businesses can use:
- Identified high-net-worth customers based on total account balances, increasing premium banking conversion by 20%.
- Segmented inactive savings accounts still accruing interest,to initiate reactivation through targeted campaigns.
- Optimized marketing strategy, reducing outreach to 25.68% of customers while capturing 94% of loan adopters, saving campaign costs.
- Calculated total accrued interest on deposits and loans,to align interest payouts with revenue goals.
- Assessed profitability impact through rate optimization strategies. -Identified top interest-earning customers to provide personalized financial services and improve retention.
- Flagged high-value transactions,to support fraud risk controls.
- Analyzed spending behavior (holiday vs. non-holiday, Friday trends),to strengthen fraud detection systems.
- Mapped credit card transaction patterns for dynamic credit limit adjustments and better risk management.
- Analyzed transaction volumes across ATM, POS, Net Banking, UPI, revealing a rise in online banking adoption.
- Identified the most used transaction channels, enabling the bank to streamline operations and enhance service delivery.
- Optimize interest strategies to balance interest income and payouts.
- Leverage spending trends (e.g., holiday and weekend spending) for targeted promotions.
- Enhance fraud detection by monitoring high-value transactions.
- Improve digital banking services as online transactions grow.
- SQL (MySQL Workbench) for querying and analysis|Data Modeling
- Database Tables:
bank_customer,bank_account_details,bank_account_transaction,bank_interest_rate, etc.
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Contains all SQL queries used for analysis.
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Contains Data tables information.
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Entity Relationship -ER diagram of tables.
