One of the coding interview questions involved identifying potentially fraudulent transactions from a list. Each transaction had a name, amount, timestamp, and location. The goal was to flag transactions as fraudulent if:

• The amount was over $1000, or
• There was another transaction by the same person in a different location within 60 minutes.

The input was given as a string with each transaction on a separate line, and we had to return the suspicious ones based on these rules. It tested data parsing, map-based grouping, and condition-based filtering—great for evaluating edge cases and clean logic.