The Loan Types interview question typically presents you with a database schema containing information about users and their respective loan accounts. The core objective is to write a SQL query that identifies specific users based on the types of loans they hold. Usually, the prompt asks you to find users who have at least two distinct, specified types of loans simultaneously—for instance, users who currently hold both a "Mortgage" and an "Auto Loan". This is a classic relational database problem that tests your ability to filter and aggregate data based on multiple conditions across rows belonging to the same entity.
Companies frequently ask the Loan Types coding problem to evaluate a candidate's practical proficiency with SQL. In real-world enterprise applications, especially in finance and banking tech, analyzing user portfolios is an everyday task. Interviewers want to see if you can translate business logic (e.g., "Find cross-sell opportunities for customers with multiple loan products") into efficient database queries. It tests your understanding of GROUP BY, HAVING clauses, and aggregate functions, which are essential for any backend, data, or full-stack engineering role.
Since this is a Database problem, it doesn't use a traditional algorithmic pattern like two-pointers or dynamic programming. Instead, it relies on the Aggregation and Filtering pattern in SQL. The most common approach is to group the records by the user identifier (GROUP BY user_id) and then apply a conditional filter on the aggregated groups using the HAVING clause. Alternatively, it can be solved using Self-Joins or Subqueries (e.g., intersecting a set of users with loan A and a set of users with loan B), but aggregation is generally the most concise and idiomatic method.
Imagine you have a Loans table with two columns: user_id and loan_type.
| user_id | loan_type |
|---|---|
| 101 | Mortgage |
| 101 | Auto |
| 102 | Auto |
| 103 | Mortgage |
| 103 | Personal |
If the goal is to find users who have both a "Mortgage" and an "Auto" loan, we group the table by user_id. For each group, we check if they have a record for both loan types.
User 101 has both, so they are included in the result. User 102 only has an Auto loan, so they are excluded. User 103 has a Mortgage but no Auto loan, so they are also excluded. The final output would simply be user 101.
A very common mistake when solving the Loan Types interview pattern is trying to use a simple WHERE clause like WHERE loan_type = 'Mortgage' AND loan_type = 'Auto'. This will always return zero rows because a single row cannot simultaneously have two different loan types! Another mistake is forgetting that a user might have multiple loans of the same type. If you use COUNT(loan_type) = 2, it might accidentally include a user with two Auto loans. You must ensure you are counting distinct types or explicitly checking for the presence of each required type using conditional aggregation (like SUM(CASE WHEN...)).
To master this type of database problem, practice writing queries using conditional aggregation. Get very comfortable with expressions like SUM(CASE WHEN condition THEN 1 ELSE 0 END) > 0 inside a HAVING clause. This technique is incredibly versatile and will serve you well not only for the Loan Types question but across many complex SQL reporting scenarios you will face in tech interviews.
