9 Regions Bank SQL Interview Questions (Updated 2025)

Regions Bank employees use SQL daily for analyzing banking transaction data and developing financial risk assessment models. So, it shouldn't surprise you that Regions Bank almost always asks SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.

So, to help you practice for the Regions Bank SQL interview, here's 9 Regions Financial SQL interview questions can you solve them?

9 Regions Financial SQL Interview Questions

SQL Question 1: Identify High Net Worth Customers

Regions Bank, a large banking corporation, prioritizes enhancing relationships with their high net worth customers. For this purpose, the bank defines a "power user" as a customer who has high total deposits, a high frequency of transactions, and has taken out significant loans.

Write a PostgreSQL query to identify the power users. Specifically, identify customers who have aggregate deposits exceeding $200,000, more than 10 transactions per month on average, and loans exceeding$500,000.

Sample tables for 'Customers', 'Deposits', 'Transactions', and 'Loans':

Customers Sample Input:

Deposits Sample Input:

Transactions Sample Input:

Loans Sample Input:

Answer:

SELECT 
  c.customer_id, 
  c.customer_name, 
  SUM(d.amount_deposited) AS total_deposits, 
  COUNT(t.transaction_id)::decimal / COUNT(DISTINCT DATE_TRUNC('month', t.date_of_transaction)) AS avg_monthly_transactions, 
  SUM(l.loan_amount) AS total_loan_amount
FROM 
  Customers c 
LEFT JOIN 
  Deposits d ON c.customer_id = d.customer_id 
LEFT JOIN 
  Transactions t ON c.customer_id = t.customer_id 
LEFT JOIN 
  Loans l ON c.customer_id = l.customer_id 
GROUP BY 
  c.customer_id, 
  c.customer_name 
HAVING 
  SUM(d.amount_deposited) > 200000 AND 
  COUNT(t.transaction_id)::decimal / COUNT(DISTINCT DATE_TRUNC('month', t.date_of_transaction)) > 10 AND 
  SUM(l.loan_amount) > 500000;

The above script retrieves customers who fulfil the criteria of being a power user. It combines information from four tables: 'Customers', 'Deposits', 'Transactions', and 'Loans'. The query calculates the total amount deposited, the average monthly transactions, and total loan value for each customer, and filters out those who don't meet the outlined rules.

To practice a similar power-user data analysis problem question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question:

SQL Question 2: Department Salaries

You're given a table of Regions Bank employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.

Code your solution to this question directly within the browser on DataLemur:

The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.

SQL Question 3: How can you select unique records from a table?

If you want to return records with no duplicates, you can use the DISTINCT keyword in your SELECT statement.

For example, if you had a table of Regions Bank employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:

SELECT DISTINCT job_title FROM regions_bank_employees;

If regions_bank_employees had the following data:

Then the output from the query would be:

SQL Question 4: Average deposit amount by states and quarters

Regions Bank, your employer, wants to understand the average deposit made by customers in each state over a quarterly basis. This will help them identify patterns and make decisions based on seasonal trends.

You have access to a deposits table which includes the following data:

deposits Example Input:

You need to write a PostgreSQL query to find the average deposit amount made in each state for every quarter of the year 2021.

Answer:

SELECT
  state,
  EXTRACT(QUARTER FROM deposit_date) as quarter,
  AVG(amount) OVER (PARTITION BY state, EXTRACT(QUARTER FROM deposit_date)) as avg_deposit
FROM
  deposits
WHERE
  EXTRACT(YEAR FROM deposit_date) = 2021
ORDER BY
  state,
  quarter;

This query works by partitioning the data by state and the quarter of deposit_date, then calculates the average deposit amount within each of these partitions with the help of Window Function. The WHERE clause is used to limit the data to the year 2021. Finally, the results are ordered by state and quarter to keep them organized. The EXTRACT function is used to get the quarter and year from the deposit_date.

For example, in the state of FL in the 1st quarter, the average deposit would be calculated for all deposits recorded in this particular time frame in the state of FL. This pattern would continue for all states across all quarters of the year.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

SQL Question 5: How do you locate records in one table that are absent from another?

To locate records in one table that are absent from another, you can use a LEFT JOIN and then look for NULL values in the right-side table.

For example, say you exported Regions Bank's CRM (Customer Relationship Management) database, and had a table of sales leads, and a second table of companies.

Here's an example of how a LEFT JOIN query can find all sales leads that are not associated with a company:

SELECT *
FROM sales_leads
LEFT JOIN companies ON sales_leads.company_id = companies.id
WHERE companies.id IS NULL;

This query returns all rows from the sales leads table, along with any matching rows from the companies table. If there is no matching row in the companies table, NULL values will be returned for all of the right table's columns. The WHERE clause then filters out any rows where the companies.id column is NULL, leaving only sales leads that are NOT associated with a company.

See the most recent news from Regions Bank and stay up to date before the interview!

SQL Question 6: Loan Repayment Status in Regions Bank

At Regions Bank, we have loan services that are provided to different customers. Every customer may have multiple loans with different status. We have to monitor repayment status of every customer frequently.

We have two tables here:

customer Table:

loan Table:

The requirement for this task is to write a SQL query to display name and email of customers and the total amount of loan they have repaid, and also the total amount of loan ongoing for each customer.

Answer:

SELECT
    c.first_name || ' ' || c.last_name AS customer_name,
    c.email,
    SUM(CASE WHEN l.status = 'repaid' THEN l.amount ELSE 0 END) AS total_repaid,
    SUM(CASE WHEN l.status = 'ongoing' THEN l.amount ELSE 0 END) AS total_ongoing
FROM
    customer c
JOIN
    loan l
ON
    c.customer_id = l.customer_id
GROUP BY
    customer_name, c.email;

This query joins the customer and loan tables on customer_id and then uses the SUM function with a conditional CASE statement to calculate the total repaid and ongoing loan amounts for each customer. The GROUP BY clause groups the result by customer_name and email.

SQL Question 7: How are left and right joins different from each other?

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Both types of joins in SQL help you retrieve data from multiple tables and merge the results into a single table.

To demonstrate the difference between a left join versus a right join, imagine you had two database tables: an Advertising_Campaigns table that contains data on Google Ads keywords and their bid amounts, and a Sales table with information on product sales and the Google Ads keywords that drove those sales.

A LEFT JOIN retrieves all rows from the left table (in this case, the Advertising_Campaigns table) and any matching rows from the right table (the Sales table). If there is no match in the right table, NULL values will be returned for the right table's columns.

A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the Advertising_Campaigns table). If there is no match in the left table, NULL values will be returned for the left table's columns.

SQL Question 8: Click-through and Conversion Rates for Digital Banking Features

Regions Bank has been investing in its digital banking features and wants to understand the user engagement online. They keep track of the number of users who click on different features (like checking account balance, transferring money, depositing checks online, etc.) and the number of those users who actually complete the operation.

The data is stored in two tables, one for clicks and one for operations completed. Here's the schema for both tables and some sample data:

clicks Example Input:

operations Example Input:

Regions bank needs an SQL query that calculates the clickthrough rate (#operations / #clicks) for each feature on a monthly basis.

Answer:

We will be using the PostgreSQL query language.

SELECT 
    DATE_PART('month', c.click_date) AS month, 
    c.feature_id, 
    COUNT(o.operation_id)::decimal / COUNT(c.click_id) AS clickthrough_rate
FROM clicks c
    LEFT JOIN operations o ON c.feature_id = o.feature_id AND c.user_id = o.user_id
GROUP BY month, c.feature_id
ORDER BY month, c.feature_id;

This SQL query will join the clicks and operations tables based on company_id and user_id. It then calculates the number of operations completed and the number of clicks for each feature every month.

To solve a related SQL interview question on DataLemur's free online SQL coding environment, solve this SQL interview question asked by Facebook:

SQL Question 9: Filter Customers Based on Account Type

As part of the Data Team at Regions Bank, you are tasked with analyzing customer data. Given the customer table, can you write a SQL query to find all customers who have a checking account that contains the word 'Gold' in it?

customer Example Input:

|**customer_id**|**first_name**|**last_name**|**account_type**|
|:----|:----|:----|:----|
|1932|John|Doe|Gold Checking Account|
|2501|Jane|Smith|Silver Checking Account|
|3682|Sam|Brown|Gold Checking Account|
|4015|Lisa|Green|Silver Checking Account|
|5186|Nancy|Wilson|Gold Saving Account|

Example Output:

|**customer_id**|**first_name**|**last_name**|**account_type**|
|:----|:----|:----|:----|
|1932|John|Doe|Gold Checking Account|
|3682|Sam|Brown|Gold Checking Account|

Answer:

SELECT * 
FROM customer 
WHERE account_type LIKE '%Gold Checking Account%';

This PostgreSQL query returns all records in the 'customer' table where the 'account_type' contains the string 'Gold Checking Account'. The '%' wildcard character is used on both sides of the string to account for any additional characters that could appear before or after 'Gold Checking Account' in the 'account_type' column. In this case, the query returns customer records who have a Gold Checking Account.

How To Prepare for the Regions Bank SQL Interview

The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the earlier Regions Bank SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.

Each DataLemur SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and best of all, there's an interactive coding environment so you can instantly run your SQL query and have it executed.

To prep for the Regions Bank SQL interview it is also helpful to practice interview questions from other banking & finanacial services companies like:

• Fifth Third Bank SQL questions

• Bank of New York Mellon SQL questions

• Citizens Bank SQL questions

• Huntington Bank SQL questions

However, if your SQL coding skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers SQL topics like CASE/WHEN statements and handling date/timestamp data – both of which pop up often in Regions Bank interviews.

Regions Financial Data Science Interview Tips

What Do Regions Bank Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems covered in the Regions Bank Data Science Interview include:

• Statistics & Probability Questions
• Coding Questions in Python or R
• Product-Sense and Business Case Questions
• Machine Learning Questions
• Behavioral Interview Questions

How To Prepare for Regions Bank Data Science Interviews?

The best way to prepare for Regions Bank Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from companies like Google, Tesla, & Goldman Sachs
• A Crash Course covering Stats, SQL & ML
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