The Climate Valuation Assessment Tool (CVAT) is an internal tool developed for Credit Suisse's climate risk platform, CRX, designed to assess climate-related risks associated with financial transactions. The tool integrates climate and emissions data from counterparties into a base run model, providing a comparative analysis of pre-deal results against the base model, helping risk analysts and relationship managers make informed decisions.
Key goals included ensuring ease of use, clear data visualization, and seamless integration within the CRX platform.
Risk analysts and relationship managers at Credit Suisse need a streamlined way to incorporate climate and emissions data from counterparties into their decision-making processes.
These users struggle to visualize and compare pre-deal analysis against base run results, making it difficult to accurately assess transaction risks.
The challenge lies in creating a user-friendly platform that simplifies the comparison of intricate datasets, allowing users to make informed, data-driven decisions with confidence.
As the designer for CVAT, my impact centered around creating an efficient, user-centered solution that addressed the complex needs of risk analysts and relationship managers.
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Both user groups require a tool that provides clear data visualizations, easy comparison features, and seamless integration into their existing workflows to make informed decisions efficiently.
These users are responsible for evaluating climate-related risks in potential transactions.
They need tools to analyze climate and emissions data, compare it against base run models, and assess the financial impact of these risks on transaction decisions.
These users interact with clients and use the insights from CVAT to inform decision-making in transactions. They need a high-level understanding of climate risks, quick access to key data points, and the ability to present the results in a clear way to clients.
During the research phase:
Understand the workflows, pain points, and data needs.
design an intuitive tool
For both risk analysts and relationship managers
Seamlessly integrates into their
processes & workflows
For assessing climate-related transaction risks.
Can you walk me through a typical day as an interventionist?
How do you currently compare pre-deal analysis data with base run results, and what improvements would make this process easier?
Right now, I manually export the data and use spreadsheets to compare. It’s a tedious process, and I wish there was a way to view both data sets side-by-side in real-time.
What types of data visualizations or tools would help you better understand climate-related risks?
I need clear, dynamic charts that show how emissions affect different risk factors, and ideally, I’d like to see scenario-based modeling—what happens if certain climate goals aren’t met.
How do you incorporate climate risk insights into your decision-making process, and what features would improve that integration?
Once I’ve identified high-risk areas, I share my findings with the team, but it would be much easier if the tool could flag critical risks automatically and provide suggestions for mitigating them.
What difficulties do you encounter when communicating climate risk assessments to clients or stakeholders?
A lot of the data is too technical, and clients don’t always understand the implications. I need simplified visuals or summaries that I can quickly show to help explain how climate risks impact their investments.
How do you currently access and interpret climate-related data, and what improvements would make this more efficient?
I have to switch between several systems to gather the data I need, which takes up too much time. A centralized tool that provides a snapshot of key metrics would be ideal.
What tools or visualizations would help you better understand and explain the impact of climate risks on transactions?
A more efficient system would free up valuable time for my team to focus on professional development and building stronger relationships with students.
How do you use the results of climate risk analysis to inform your client recommendations, and what would make that process more seamless?
I spend a lot of time breaking down complex data for clients. If the tool could auto-generate risk summaries based on the analysis, it would speed things up and make the discussions more effective.
I spoke directly with two professions within the bank; risk analysts and relationship managers.
"It’s not just about having the data; it’s about interpreting it effectively to make informed decisions."
Age
42
Education
MBA
Location
Switzerland
Occupation
Risk Analyst
"I need to simplify the complexity for clients. If I can’t explain the climate risks in a way they understand, they won’t see the urgency or the value in the analysis."
Age
55
Education
College
Location
London
Occupation
Relationship Manager
The insights from the personas and empathy map fueled the creation of a user journey map, visualizing their steps within the platform and pinpointing several pain points. This map guides us to design a user-centered experience for Practice Makes Perfect.
Ensure the platform is
easy to navigate,
For both risk analysts and relationship managers
should be visually appealing,
To make complex datasets easy to understand.
The current workflow had users manually gather climate and emissions data from various sources, making it time-consuming to consolidate information for analysis.
Comparing pre-deal data with base run results required manual effort, spreadsheets, that would lead to potential errors.
The raw emissions, financial, production data was difficult to understand, especially for users without technical expertise.
Searching for counterparties or relevant data is slow or inaccurate, leading to delays and frustration when trying to access the necessary information quickly.
I designed the information architecture based on insights from user research and interviews, ensuring it aligned with the workflows of risk analysts and relationship managers, making navigation seamless and prioritizing quick access to critical data for informed decision-making.4o
Prior to designing the wireframes, I conducted a competitive analysis, leveraging insights from similar applications. This allowed me to use previously designed and tested components, ensuring a more efficient and user-centered design process.
I created the wireframes using the existing design library, which streamlined the process and made the design more efficient, as most components were already in grayscale, aligning with the Credit Suisse brand guidelines.
After conducting usability testing, several valuable insights emerged:
During testing, counterparty search feature was not as responsive or accurate as users expected, resulting in frustration when trying to locate relevant data quickly. Suggested using search filters to make it more straightforward.
Users found initial wireframes difficult to recall the exact counterparty chosen, when viewing details. A counterparty overview was added at the top of the details screens to solve this frustration.
Users appreciated the data comparison charts, noting that it significantly streamlined their workflow by providing quick, side-by-side data comparisons, reducing manual effort.
As a result, the final outcome reflected a more user-friendly experience that aligned closely with the workflows of risk analysts and relationship managers.
By streamlining the search and data retrieval processes, the tool enabled them to perform pre-deal analyses with greater speed and accuracy. The introduction of advanced filters and data comparison features allowed users to efficiently drill down into key metrics, such as financial performance, emissions data, and production outputs, enabling more informed decision-making.
These were the main learnings made following the final handoff:
First, When working with the engineers and the development team, I maintained an ongoing dialogue to ensure the design translated into a functional product.
This included:
By using shared design tools like Figma and collaborating in real-time, we ensured that the final build adhered to the design vision while being optimized for performance and scalability.
The results of the usability improvements were measured through key metrics such as;
Post-launch feedback indicated smoother navigation, faster data retrieval, and a significant reduction in the time it took for analysts to conduct base runs and compare financial and climate risk data. These results validated the design iterations and underscored the importance of aligning user feedback with technical development.
