Why Finance SQL Is Different
The financial sector deals with data that has unique characteristics setting it apart from other industries. Transactions occur in real-time and must be processed with absolute precision. A single misplaced decimal point can result in significant financial losses. Time is a critical dimension, as the exact timestamp of a transaction can determine whether it falls within a reporting period or triggers a fraud alert.
When you interview at a bank, payment processor, or fintech startup, the technical questions will reflect these realities. Interviewers want to see that you understand not just SQL syntax, but the specific challenges of working with financial data.
Fraud Detection Queries
Detecting fraudulent transactions is one of the most common interview topics in fintech. These questions test your ability to identify patterns that suggest suspicious activity.
A typical fraud detection question might ask you to find accounts where transactions occurred in different countries within an impossibly short time window. This requires combining window functions with geographic logic:
The interviewer expects you to think about what makes a transaction suspicious. Transactions that occur within an unusually short time window across different geographic locations suggest a stolen card being used while the legitimate cardholder is elsewhere. Accounts with sudden spikes in transaction volume compared to their historical patterns warrant investigation. Round-number transactions that exceed normal spending patterns might indicate testing of stolen credentials.
These queries typically require window functions like LAG and LEAD to compare consecutive transactions, self-joins to analyze patterns within accounts, and complex date arithmetic to calculate time differences across time zones.
Account Balance Calculations
Financial applications frequently need to calculate running balances, which presents interesting SQL challenges. Unlike simple aggregations, balance calculations must respect the temporal order of transactions and handle various transaction types differently.
Interview questions in this area might ask you to calculate the average daily balance for a set of accounts over a month, determine the minimum balance reached by each account during a period, or identify accounts that fell below required minimum balances and when those violations occurred.
These problems test your understanding of window functions with proper ordering, your ability to handle deposits and withdrawals correctly, and your skill in working with date ranges and business calendars.
Regulatory Reporting
Financial institutions must generate reports that comply with regulations from bodies like the SEC, FINRA, or international equivalents. These reporting requirements define exactly how data must be aggregated and presented.
Interview questions might involve calculating risk metrics according to specific formulas, generating anti-money laundering reports that flag accounts meeting certain criteria, or producing audit trails that track every change to sensitive data.
The key challenge in regulatory reporting is precision. The queries must produce exactly the results specified by regulations, handling edge cases correctly. Interviewers want to see that you can translate regulatory requirements into accurate SQL.
Interest and Financial Calculations
Calculating compound interest, loan amortization schedules, and portfolio returns demands precision with decimal arithmetic. Financial calculations often involve specific rounding rules that must be followed exactly.
You might be asked to calculate the total interest accrued on a portfolio of loans over a period, determine the remaining principal on loans after a series of payments, or compute investment returns adjusted for the timing of deposits and withdrawals.
These questions test your ability to work with decimal precision, implement financial formulas in SQL, and handle the complexity of time-weighted calculations.
Practice with Realistic Scenarios
Understanding these patterns is the first step. To truly prepare for fintech interviews, you need hands-on practice with realistic financial data.
SQLSandboxes offers case studies that simulate real financial scenarios, including transaction fraud detection and customer analytics for financial services. The SQL Editor provides practice databases with realistic financial schemas.
For more on preparing for domain-specific interviews, see our guide on Why Domain-Specific SQL Practice Matters.
Key Takeaways
When preparing for finance and fintech SQL interviews, focus on these areas:
- Window functions for sequential analysis of transactions
- Precise handling of timestamps across time zones
- Decimal arithmetic with appropriate rounding
- Self-joins for pattern detection within accounts
- Understanding of what constitutes suspicious activity
The technical skills matter, but demonstrating that you understand the business context of financial data will set you apart from other candidates.