Q: Why would a bank need UX people in a data science project?
A: Banks are very risk adversed. The industry needs UX people to help them reduce costs and risks.

The problem to be solved

• Disparate tools and data
• Difficult process to get anything done
• Fear of overspending and exhausting resources

The reason data scientists are hired in the first place is to develop algorithms and build machine learning models. Yet 80 percent of a data scientist’s valuable time is spent simply finding, cleaning and reorganizing huge amounts of data.

Too much time spent on finding stuff = Wasting data scientists' time = Wasting money

Challenges

• Many APIs are read-only and an iframe form built into the portal was considered as not robust enough
• Data available at MVP stage was not granular enough for the visualisation we proposed

Process

• Stakeholder workshop to understand business needs
• Rapid prototyping with Sketch and Invision
• Data visualisation to draw attention to critical resources
• User research with different user groups
• Close collaboration with development team to create the best solution with design and technical constrains in mind
• Respond to shifting business strategy

Outcome

• MVP delivery for UAT
• High fidelity prototype, envisioned user journey and other documentations handed off to internal teams for further development

(More assets coming soon)