Data Analytics, Data Science, and Data Engineering employees at Liberty Mutual uses SQL to analyze claim data, including claim frequencies and severities, for fraud detection, as well as organizing customer data, such as demographics and policy information, for personalized insurance offering. For this reason, Liberty Mutual often uses SQL questions for job interviews.
Data Analytics, Data Science, and Data Engineering employees at Liberty Mutual write ad-hoc SQL queries as a big part of their job. They use SQL for analyzing claim data for fraud detection and organizing customer data for personalized insurance offering. For this reason Liberty Mutual often tests jobseekers SQL interview questions.
Thus, to help you study for the Liberty Mutual SQL interview, we've curated 11 Liberty Mutual Insurance Group SQL interview questions in this article.
Liberty Mutual wants to identify their "power users", that is, the users who have the most active insurance policies and pay their premiums regularly and on time. You are tasked to write a SQL query to find the top 10 users with the most active insurance policies, and also make sure that these users haven't missed any premium payments in the last 12 months.
Consider the following two tables: and .
policy_id | holder_id | active_since | premium_amount |
---|---|---|---|
401 | 127 | 01/01/2019 | 500 |
490 | 325 | 06/30/2020 | 700 |
510 | 127 | 05/05/2020 | 800 |
600 | 312 | 04/20/2019 | 600 |
760 | 325 | 12/25/2020 | 400 |
payment_id | policy_id | payment_date | amount_paid |
---|---|---|---|
6101 | 401 | 01/01/2020 | 500 |
6102 | 401 | 02/01/2020 | 500 |
6103 | 510 | 03/01/2020 | 800 |
6104 | 401 | 04/01/2020 | 500 |
6105 | 510 | 05/01/2020 | 800 |
You can use PostgreSQL's window function to count the number of active policies per user, then filter out those who have missed any payments in the last 12 months.
This query first counts the number of active policies per user. It then identifies any policy_id with missed or no payment made in the last 12 months. Finally, it filters out users who have any missed payments and orders the remaining users by the number of policies in descending order. The 'LIMIT 10' then provides us with our top 10 power users.
To work on a similar customer analytics SQL question where you can solve it right in the browser and have your SQL code instantly executed, try this Walmart Labs SQL Interview Question:
Given a table of Liberty Mutual employee salary information, write a SQL query to find the top 3 highest earning employees in each department.
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 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
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 |
Check your SQL query for this problem interactively on DataLemur:
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 code above is hard to understand, you can find a detailed solution with hints here: Top 3 Department Salaries.
An inner join returns only the rows that match the join condition between the two tables, whereas a full outer join returns all rows from both tables, including any unmatched rows.
Liberty Mutual being an insurance company, requires analysis around its insurance policy reviews. Imagine, the company wishes to track the average star rating received for each insurance policy on a monthly basis, based on customer reviews. To facilitate this, there is a dataset containing information for each review submitted for the various insurance policies available.
Please write a SQL query to calculate the monthly average star rating received for each insurance policy.
review_id | user_id | submit_date | policy_id | stars |
---|---|---|---|---|
6171 | 123 | 2022-06-08 | 50001 | 4 |
7802 | 265 | 2022-06-10 | 69852 | 4 |
5293 | 362 | 2022-06-18 | 50001 | 3 |
6352 | 192 | 2022-07-26 | 69852 | 3 |
4517 | 981 | 2022-07-05 | 69852 | 2 |
In PostgreSQL, this can be solved using the window function with a clause.
The above query first truncates the submit date to month using , which serves as the basis for partitioning the data. Then, the clause partitions the data by both the month and policy_id. After partitioning, the function calculates the average stars for each partition.
month | policy_id | avg_stars |
---|---|---|
2022-06-01 00:00:00 | 50001 | 3.50 |
2022-06-01 00:00:00 | 69852 | 4.00 |
2022-07-01 00:00:00 | 69852 | 2.50 |
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
A primary key is a column (or set of columns) in a table that uniquely identifies each row in the table. It cannot contain null values and must be unique across all rows in the table.
A foreign key is a column (or set of columns) in a table that references the primary key of another table. It is used to establish a relationship between the two tables. A foreign key can contain null values, and multiple rows in the referencing table can reference the same row in the referenced table.
For example, consider a database with two tables: and . The Liberty Mutual customers table might have a primary key column called , while the Liberty Mutual orders table might have a foreign key column called that references the column in the table. This establishes a relationship between the two tables, such that each row in the orders table corresponds to a specific Liberty Mutual customer.
Liberty Mutual, a global insurance company, wants to see how efficient they are in processing their insurance claims. Your task is to design a database for storing these claims, model the tables and their relationships, and then write an SQL query to calculate the average processing time for claims per claim type for the year 2022.
Let's assume we have two tables namely and :
claim_id | policy_holder_id | claim_type_id | claim_submit_date | claim_resolve_date |
---|---|---|---|---|
101 | 2345 | 3 | 2022-03-25 | 2022-04-05 |
102 | 4567 | 2 | 2022-05-01 | 2022-05-25 |
103 | 8745 | 1 | 2022-06-10 | 2022-07-11 |
104 | 2345 | 3 | 2022-07-23 | 2022-07-29 |
105 | 8745 | 1 | 2022-12-05 | 2023-01-01 |
claim_type_id | claim_type_name |
---|---|
1 | Vehicle |
2 | Home |
3 | Personal Injury |
This query calculates the average processing time (in days) by taking the difference between the claim resolve date and the claim submit date. It then averages these differences per claim type over the course of the year 2022.
If you want to return records with no duplicates, you can use the keyword in your statement.
For example, if you had a table of Liberty Mutual employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
If had the following data:
f_name | job_title |
---|---|
Akash | Data Analyst |
Brittany | Data Scientist |
Carlos | Data Engineer |
Diego | Data Engineer |
Eva | Data Analyst |
Then the output from the query would be:
job_title |
---|
Data Analyst |
Data Scientist |
Data Engineer |
Liberty Mutual being an insurance company, a relevant question might be to calculate the average premium customers are paying for each category of insurance provided by the company.
Let's consider a table which represents the policies taken by the customers:
policy_id | customer_id | category | premium |
---|---|---|---|
001 | 123 | Auto | 1000 |
002 | 265 | Home | 1500 |
003 | 362 | Auto | 1200 |
004 | 192 | Life | 5000 |
005 | 981 | Home | 2000 |
The question is: Write a SQL query to find the average premium customers are paying for each category of insurance.
This query would group the table by , and for each one, it calculates the average value of the column. This could be useful in analyzing which insurance categories have the highest average cost, which could, in turn, provide insights into patterns in sales and customer behavior.
category | avg_premium |
---|---|
Auto | 1100 |
Home | 1750 |
Life | 5000 |
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating average per category or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for identifying top items in each category.
As an analyst for Liberty Mutual, one of your roles is to understand the payment behavior of customers, especially regarding different types of insurance.
For this task, you are given access to the table that contains historical data about customers' insurance types and their respective premiums. Your task is to write an SQL query to find out the total and average premiums for each insurance type.
insurance_id | customer_id | type | premium |
---|---|---|---|
101 | 321 | Home | 1500 |
102 | 654 | Auto | 1200 |
103 | 789 | Life | 900 |
104 | 654 | Auto | 1300 |
105 | 321 | Home | 1700 |
106 | 789 | Life | 1100 |
To answer this question, you can write an SQL query using and the aggregate functions and as follows:
This query first groups the data by the insurance type. It then calculates the total premium and the average premium for each group.
The calculates the total premium of every type of insurance for all customers, while calculates the average premium for each insurance type.
Finally, the results are presented in a table with , and columns.
type | total_premium | average_premium |
---|---|---|
Auto | 2500 | 1250 |
Home | 3200 | 1600 |
Life | 2000 | 1000 |
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.
At Liberty Mutual, you are tasked to retrieve customer profiles whose addresses mention a specific city, for instance, 'Boston'. Such records are crucial for evaluations related to geographical distribution and region-specific plans.
Consider the table with the following structure:
profile_id | holder_name | address | policy_number |
---|---|---|---|
101 | John Doe | 35 N. Beacon St, Boston, MA 02215 | MA001 |
102 | Jane Smith | 120 Tremont St, Boston, MA 02108 | MA002 |
103 | Mike Davis | 1 Beach St, Unit 2, New York, NY 10011 | NY001 |
104 | Sara Elsa | 51 Chauncy St, Apt# 4, Boston, MA 02111 | MA003 |
105 | David Watson | 279 W. 113rd St, Apt 3A, New York, NY 10026 | NY002 |
Please write a PostgreSQL query to retrieve only the records of policy holders who live in 'Boston'.
This query will return all rows from the table where the field contains the string 'Boston'. The percent (%) symbol is used in conjunction with the LIKE operator to denote an unknown number or characters before and after 'Boston'.
The output will be:
profile_id | holder_name | address | policy_number |
---|---|---|---|
101 | John Doe | 35 N. Beacon St, Boston, MA 02215 | MA001 |
102 | Jane Smith | 120 Tremont St, Boston, MA 02108 | MA002 |
104 | Sara Elsa | 51 Chauncy St, Apt# 4, Boston, MA 02111 | MA003 |
The key to acing a Liberty Mutual SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Liberty Mutual SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon.
Each DataLemur SQL question has hints to guide you, step-by-step solutions and most importantly, there is an interactive coding environment so you can instantly run your query and have it executed.
To prep for the Liberty Mutual SQL interview it is also a great idea to practice interview questions from other insurance companies like:
Read about Liberty Mutual's exciting new collaboration with MIT to drive AI innovation in the insurance industry!
However, if your SQL foundations are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers SQL topics like creating summary stats with GROUP BY and LEAD window function – both of these show up frequently in Liberty Mutual SQL interviews.
Beyond writing SQL queries, the other types of problems to practice for the Liberty Mutual Data Science Interview include:
To prepare for Liberty Mutual Data Science interviews read the book Ace the Data Science Interview because it's got:
Don't ignore the behavioral interview – prep for it with this list of behavioral interview questions for Data Scientists.