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Fraud Prevention Web Application

The Challenge

We designed a fraud detection web application for a client that would enable users to quickly identify and prevent fraud. The key question we were trying to answer here is how can a company rapidly detect small frauds across millions of transactions?. The primary place that fraud occurs for the client is at the cash register, where workers can pocket small amounts of money by deleting transactions or adjusting coupon rates. These small thefts add up over time and can have a big impact on the company. Their existing method of detecting fraud took hours and involved going through over 100 million rows of data and 350+ spreadsheets. I worked on a team with 2 other designers and was involved more in the second version of the app.


  1. Interviewed the users to understand all the ways fraud could occur

  2. Documented the traces different activities can leave in the data trail

  3. Observed investigators go about their jobs of identifying fraud to understand how they analyze data 

  4. Designed custom visualizations that map the team’s real-world process to the data, and zeroes in on anomalies and events that were more likely to reflect fraudulent activities

Understanding Fraud Types

  • Subbing: Punching a ticket up with 0 value by using an illegitimate pass or coupon

  • Stub Selling: Letting people in without a real ticket, usually by providing them with a "fake" ticket

  • Short Selling: Undercharging a customer (i.e. selling a child ticket but charging for an adult ticket)

  • Refunds: Issuing a fake refund

  • Deletion: Deleting a transaction

  • Coupon: Using an illegitimate coupon

  • Refund: Issuing a fake refund

Identifying metrics and their impact on fraud

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Defining the Persona

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Version 1

Store Overview

See a high-level snapshot of metrics across stores, and identify anomalies that help direct where to dig deeper and investigate further.

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Employee Breakdown

Identify employees in a given store that may have suspicious activity, based on patterns across days and between employees.

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Employee History

See a “punchcard” of long-term employee activity, to see not only that there may be a problem, but also when the problem began.

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Version 2

After 2 years of using the app, the client came back to us for a second iteration. Some of the asks were

  • Enable analysis for seeing multiple stores week over week, quarter over quarter, and compared to last year

  • Enable analysis to compare trends of multiple metrics in a single store

  • Ability to create customized filters for better groupings of stores with common attributes

Store Overview Redesign

See a high-level snapshot of metrics across stores, and identify anomalies that help direct where to dig deeper and investigate further. The major change we made here was being able to compare the current year metrics to prior years.

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Multi-Metric Analysis

Investigate a store further by analyzing multiple metrics in one view for the selected store. Identify if there are patterns or a correlation among the metrics that indicate fraud. This was a new view that we added as a follow up to the previous view where they identify suspicious stores.

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Single Employee Breakdown

We added a view to analyze data on a single employee in-depth. This view serves as a way to confirm the fraud and provide evidence.

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We also added the ability to save current selections and lock tooltips on a screen so that they're always displayed. This would be helpful when taking screenshots as evidence.

Overall this was a very successful project. The client was able to utilize this tool to identify fraud quickly and save the company money.