AI Imaging Portal

Background

This company had developed proprietary software that presents processed images of devices to live agents that require evaluation in real time. Agents would review the images captured and were responsible for grading their condition.

However, this evaluation process was turned on its head with a new machine learning program that lives over the existing software and is able to automate device examination while also grading more quickly and accurately than live agents based off learning from extensive test data sets from thousands of device images.

The new AI was not yet ready to be fully released into the field and needed to continue collecting test data. Which meant it would need to be integrated in the same environment as live agents to collect data and complete requests.

Problem

  • Make design updates to company’s internal web portal to accommodate machine learning of “virtual agent’.
  • Ensure that live identity verification agents experienced overall ease of use while acclimating to the newly hybrid manual/AI evaluation program.
My Role
UI Design
User Research
Platforms
DESKTOP SOFTWARE

User Research

Before releasing the software, research with the product’s primary, verification agents was conducted to validate whether the proposed user interface changes are intuitive and easily understood by agents. The main objectives of the study included:

  • Validate new design concepts for PGV portal, with emphasis on testing usability of new features for device verification.

  • Evaluate how agents’ performance is affected by design changes.

  • Gain a better understanding of how agents work in current portal design to help inform new user interface.

  • Verify if proposed UI design allows agents to perform all functions needed to effectively evaluate a device’s LCD and mechanical attributes.

Solution

Before

After

The changes made to the portal’s interface design allowed users to perform their job functions more quickly and effectively. After the UI redesign was released, device evaluation accuracy has since improved by 5.5%.

Some additions to the product feature set that allowed this marked improvement:

  • The inclusion of banner-style alerts that provide instantly recognizable visual notifications for agents when there is a case of suspected fraud or suspicious activity.
  • Color classification system that allows agents to immediately distinguish when they are evaluating a different attribute of a device.
  • Information modals that provides examples of real-life device images and enhanced descriptions for what agents should be looking for when making grading determinations.
  • Modern iconography set that establishes visual language and clearly represents features, functionality, and content.