01 // Overview
Identifying Emerging Risks Through Large Data
Over the summer of 2019, I had the opportunity to work at RBC Amplify as a UX/UI designer and this case study is a general summary of the experience. RBC Amplify is an innovation program where your team is given a business challenge and by then end of the internship pitch the solution to senior executives against 21 other teams in hopes of winning one of the awards.
Due to the Non-Disclosure Agreement I signed, I am not able to discuss or show the specifics of the solution, but I have been given permission to provide a general overview of the research process I went through as well as showcasing the logo I created.
02 // Background
Tools
Role
Timeline
Team
03 // Problem
How might we identify the most important emerging risks in a large volume of data?
RBC's Market Risk department examines daily market data, position data, and risk data across many business lines in an attempt to identify emerging risks that may pose a threat to its trading portfolio. However, as the quantity of incoming data is increasing, it is becoming more difficult to identify these risks.
04 // User Research
Users had many manual processes, leaving them with little time to analyze the data
After my team participated in two weeks of knowledge sessions to gain an understanding of what Market Risk does, I structured questions for interviews with our users.
Our team conducted around 20 interviews to understand the entire problem space, as well as conducting a second round of interviews with 10 additional analysts to hone in on specific problems and questions identified in our first round. I also performed job shadows with analysts to understand what their actual work experience looked like.
The key insight I discovered was that many of the processes analysts performed involved manually sifting through large quantities of data, which occupied the majority of their day, leaving little time for actual analysis of the data.
05 // Research Synthesis
What pain points do risk analysts face?
After completing the interviews, I wrote down key phrases and ideas onto sticky notes and organized them into groups based on similarities through an affinity diagram. I discovered 4 pain points our users had.
Our team decided to focus on solving issues #2 and #3 due to the immediate benefits it would present to the users and the potential business impact it would have.
Issue // 01
Detection of outliers in data
There were no tools in place to allow for users to detect outliers in data, unless they manually identified them, which is hard when sorting through large volumes of it.
Issue // 02
Lack of visualizations for interpreting data
It was challenging to communicate insights and make informed decisions based on the data because of a lack of visualizations.
Issue // 03
Computational intensity required to perform tests
The time and computational resources required to perform these tests was a bottleneck in data analysis workflows.
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Issue // 04
Poor data infrastructure for storing data and lack of data governance
There was no unified data lake. As well, APIs were not standardized as well as column names, making it a challenge to use.
06 // Persona
Who is impacted by the pain points?
Julian
Market Risk Analyst
Profile
Julian needs to monitor and assess a large volume of data to identify any risks which may pose a threat to the trading portfolio. However, his time performing an analysis is limited due to cumbersome manual processes and the computational time it takes to run certain tests.
Needs
A quicker way to identify potential risks
The ability to assess data points through visualizations
07 // Usability Testing
Round 1 - Paper Prototype
Gaps
Not all information presented on the visualizations was useful
Analysts used several tools in their work flow and as such might forget the meaning of icons without labels
Takeaways
Change the visualizations and data to be relevant
Add labels to the icons
Focus more of our limited time on the sections that our users identified as being the highest valued features
Round 2 - Low-Fidelity Prototype
Gaps
Analysts were concerned with how accurate our insights were
Takeaways
Add a graph to compare our machine learning model's accuracy versus the traditional performance
Round 3 - High-Fidelity Prototype
Gaps
Analysts had difficulties viewing data due to visualization colours being too similar to the background
Takeaways
Change the app's colour scheme to meet accessibility needs
08 // Branding
Establishing a theme
To stay consistent with the dark theme of the website and the concept of risk, I wanted the design of the logo and branding material (such as slide decks) to incorporate both of these elements.
Incorporating our team’s problem space into the logo design
The Enterprise Market Risk team looks at identifying black swan events before they happen and since our tool would help them with that process, I decided that this idea would be appropriate in theme, while also being memorable.
I used the golden ratio to create the logo digitally and after trying out different colour schemes and gradients, I finalized the design. I decided to have the edges of the swan disappear into the background as I wanted to give the swan the idea of being illusive.
09 // Reflection
Outcome & lessons learned
After all of our team's hard work, our project was huge success!
Our team pitched our solution against 21 teams and won the "Most Disruption Solution" award at Amp Expo
Our team signed a patent, which was granted in December 2022
Our project was able to reduce the time of the traditional process from several hours to instantaneous
Lesson // 01
Trust the design process
Following the design process allowed our team to create a solution that solved for a business problem by understanding user needs, identifying a gap, and creating a solution that resolved that issue.
Lesson // 02
The importance collaboration
My team’s diverse backgrounds, skill sets, and experiences, allowed us to approach this problem space from many different angles and ultimately we came up with a better outcome as a result.
