8 Hanover Insurance SQL Interview Questions (Updated 2024)
At Hanover Insurance Group, SQL is crucial for analyzing policyholder data for risk assessment, including identifying high-risk drivers, as well as generating predictive models for claim probabilities, such as predicting the likelihood of accidents. That is the reason why Hanover Insurance always asks SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you prep, we've collected 8 Hanover Insurance Group SQL interview questions – can you solve them?
8 Hanover Insurance Group SQL Interview Questions
SQL Question 1: Analyze Insurance Policy Claims
The Hanover Insurance Group is an insurance company that deals with various types of insurances like home, auto, and commercial. Imagine you're given a dataset of made auto insurance claims including claim cost, the date the claim was reported, and which department handled it. Here's the task: Write a SQL query that would:
- Calculate the total claim cost handled by each department per month and year.
- Calculate the average monthly claim cost for each department over the whole period.
Let's assume the table looks like this:
Example Input:
| claim_id | report_date | claim_cost | department |
|---|---|---|---|
| 101 | 01/02/2020 | 2000.00 | Auto |
| 102 | 15/02/2020 | 3000.00 | Auto |
| 103 | 20/02/2020 | 1500.00 | Commercial |
| 104 | 01/03/2020 | 1000.00 | Auto |
| 105 | 15/03/2020 | 2500.00 | Commercial |
| 106 | 20/03/2020 | 3500.00 | Auto |
Answer:
The SQL query:
Explanation:
This query first creates a Common Table Expression (CTE) to calculate the total cost and the average cost of claims for each department every month.
The CTE groups the records by year, month, and department, then sums up the claim_cost to get the total_monthly_claim for each of these groups. It also calculates the average (avg) claim cost for these groups using a window function that partitions over department, year, and month - this provides the average monthly claim cost for each department over the whole period.
The final SELECT statement retrieves all records from the CTE, ordering them by year, month, and department for an easy-to-read format.
For more window function practice, try this Uber SQL problem within DataLemur's online SQL code editor:
SQL Question 2: Top 3 Salaries
Suppose there was a table of Hanover Insurance employee salary data. Write a SQL query to find the top 3 highest earning employees within each department.
Hanover Insurance Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Solve this question directly within the browser on DataLemur:
Answer:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is hard to understand, you can find a detailed solution here: Top 3 Department Salaries.
SQL Question 3: What are stored procedures, and why are they useful?
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For example, if you were a Data Analyst at Hanover Insurance working on a HR analytics project, you might create a stored procedure to calculate the average salary for a given department:
To call this stored procedure and find the average salary for the Data Analytics department you'd write the following query:
SQL Question 4: Policy and Claims Analysis for Hanover Insurance
Hanover is an insurance company that primarily provides home and auto insurances. You are given two tables - one indicating an insurance policy and the other indicating the claims made under the policy.
Each insurance policy is assigned to one policy holder which is denoted by 'policy_holder_id'. Each policy holder can hold multiple policies which is denoted by 'policy_id'. A policy can have multiple insurance claims.
Please structure an optimized SQL query that calculates the average claim value by each policy type, for claims filed in the year 2022.
Sample Input:
| policy_id | policy_holder_id | policy_type | policy_start_date | policy_end_date |
|---|---|---|---|---|
| 1 | 400 | home | 01/02/2021 | 01/02/2022 |
| 2 | 500 | auto | 02/15/2021 | 02/15/2022 |
| 3 | 600 | auto | 03/01/2022 | 03/01/2023 |
| 4 | 700 | home | 06/04/2022 | 06/04/2023 |
Sample Input:
| claim_id | policy_id | claim_date | claim_value |
|---|---|---|---|
| 1 | 1 | 01/10/2022 | 5000 |
| 2 | 2 | 02/26/2022 | 3000 |
| 3 | 3 | 04/05/2022 | 7000 |
| 4 | 3 | 05/03/2022 | 9000 |
| 5 | 4 | 09/23/2022 | 6000 |
| 6 | 4 | 10/12/2022 | 5000 |
Answer:
This SQL query joins the 'policies' table with the 'claims' table on the 'policy_id' field, filters the claims based on the given year (2022), and calculates the average claim value for each policy type.
SQL Question 5: Can you explain the distinction between a unique and a non-unique index?
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
To define a unique index in PostgreSQL, you can use the following syntax:
To define a non-unique index in PostgreSQL, you can use the following syntax:
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
SQL Question 6: Filter out customer records with the SQL LIKE Keyword
As an insurance company, Hanover Insurance often needs to find specific records in their customer database. We have been asked to find all customer records where the customer's first name starts with 'John'. To solve this problem, a LIKE statement in SQL has to be used.
Given a table with the following information:
Example Input:
| customer_id | first_name | last_name | policy_number | |
|---|---|---|---|---|
| 101 | John | Doe | johndoe@email.com | POL001 |
| 102 | Jane | Doe | janedoe@email.com | POL002 |
| 103 | Johnny | Doe | johnnydoe@email.com | POL003 |
| 104 | Johan | Muller | johanmuller@email.com | POL004 |
| 105 | Johnathon | Smith | johnathonsmith@email.com | POL005 |
We want to figure out the best SQL query to return all records where the first name starts with 'John'.
Answer:
An efficient PostgreSQL query would look something like this:
The '%' is a wildcard variable that means it can be replaced with any string. So this SQL command will return all customer records where the first name starts with 'John', like 'Johnny' and 'Johnathon'.
The resulting output should look like this:
Example Output:
| customer_id | first_name | last_name | policy_number | |
|---|---|---|---|---|
| 101 | John | Doe | johndoe@email.com | POL001 |
| 103 | Johnny | Doe | johnnydoe@email.com | POL003 |
| 105 | Johnathon | Smith | johnathonsmith@email.com | POL005 |
SQL Question 7: Can you explain the distinction between cross join and natural join?
A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.
For example, say you worked on the Marketing Analytics team at Hanover Insurance, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.
For an example of each one, say you had sales data exported from Hanover Insurance's Salesforce CRM stored in a datawarehouse which had two tables: and .
An (which is a type of natural join) combines the two tables on the common
This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.
One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.
SQL Question 8: Analyze the Premium Value of Customers
Company Hanover Insurance wants to analyze the premium value of its customers. They have two tables: a 'customer' table that includes the , , and , and a 'premiums' table that includes the , , , and dates.
The company would like to see which customers have the highest paid premiums for the past year. Write a SQL query to select the top 5 customers (based on the sum of their premium amounts), and join that with their names from the 'customer' table.
Example Input:
| customer_id | name | |
|---|---|---|
| 129 | John Doe | john.doe@example.com |
| 365 | Jane Smith | jane.smith@example.com |
| 789 | Bob Johnson | bob.johnson@example.com |
| 654 | Alice Green | alice.green@example.com |
| 215 | Charlie Brown | charlie.brown@example.com |
Example Input:
| customer_id | premium_id | premium_amount | valid_from |
|---|---|---|---|
| 129 | 500120 | 2000 | 2021-06-01 |
| 129 | 500121 | 3500 | 2020-07-08 |
| 365 | 500122 | 4000 | 2020-08-11 |
| 789 | 500123 | 2000 | 2021-05-10 |
| 654 | 500124 | 3000 | 2020-04-22 |
| 215 | 500125 | 1000 | 2021-07-01 |
| 215 | 500126 | 4500 | 2020-09-09 |
Answer:
With this SQL block, we first join the 'customer' and 'premiums' tables on . We then filter out the premium amount for the last one year using . After that, we group the result by and calculate the total premium amount for each customer using within . Finally, we order the customers in descending order of total premium amount and limit the result to the top 5 entries.
Since joins come up so often during SQL interviews, practice this interactive Snapchat SQL Interview question using JOINS:
Hanover Insurance SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Hanover Insurance SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Hanover Insurance SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each DataLemur SQL question has hints to guide you, full answers and best of all, there is an online SQL code editor so you can right in the browser run your query and have it executed.
To prep for the Hanover Insurance SQL interview you can also be useful to practice interview questions from other insurance companies like:
- Kemper SQL questions
- Brighthouse Financial SQL questions
- Securian Financial SQL questions
- American Financial Group SQL questions
Learn how Hanover Insurance is harnessing the power of Artificial Intelligence to create a safer workplace!
But if your SQL skills are weak, forget about jumping right into solving questions – go learn SQL with this free SQL tutorial.
This tutorial covers SQL concepts such as aggregate functions like SUM()/COUNT()/AVG() and ordering data – both of which pop up frequently in Hanover Insurance SQL assessments.
Hanover Insurance Group Data Science Interview Tips
What Do Hanover Insurance Data Science Interviews Cover?
In addition to SQL query questions, the other topics covered in the Hanover Insurance Data Science Interview are:
- Statistics and AB Testing Questions
- Python or R Coding Questions
- Product Metrics Interview Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Hanover Insurance Data Science Interviews?
To prepare for the Hanover Insurance Data Science interview make sure you have a strong understanding of the company's values and company principles – this will be important for acing the behavioral interview. For technical interviews get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Google, Microsoft & tech startups
- A Crash Course on Stats, ML, & Data Case Studies
- Great Reviews (1000+ reviews, 4.5-star rating)