How can you use data visualization to improve risk reporting in investment banking?

1 Use appropriate charts

Data visualization is not just about making your reports look nice, but also about choosing the right charts and graphs to convey your message. Depending on the type and purpose of your risk report, you may want to use different kinds of charts, such as bar charts, line charts, pie charts, heat maps, scatter plots, or dashboards. For example, if you want to show the distribution of credit risk across different sectors, you may use a pie chart to show the relative proportions. If you want to show the trend of market risk over time, you may use a line chart to show the changes. If you want to show the correlation between two risk factors, you may use a scatter plot to show the relationship.

2 Simplify and highlight

Data visualization is also about simplifying and highlighting the key information and insights from your data. You don't want to overwhelm your audience with too much data or too many details, but rather focus on the main points and findings. You can use techniques such as filtering, sorting, grouping, aggregating, or summarizing to reduce the complexity and noise in your data. You can also use techniques such as color, size, shape, or labels to emphasize the important or relevant data points or segments. For example, if you want to show the risk appetite and risk profile of your bank, you may use a color-coded dashboard to indicate the level of risk and performance for each category.

3 Tell a story

Data visualization is not only about presenting your data, but also about telling a story with your data. You want to engage your audience and communicate your message clearly and effectively. You can use techniques such as titles, captions, annotations, or narratives to provide context and explanation for your data. You can also use techniques such as transitions, animations, or interactions to create a dynamic and interactive experience for your audience. For example, if you want to show the impact of a stress test scenario on your bank, you may use a series of charts and graphs to show the before and after effects on your risk metrics and ratios.

4 Follow standards and guidelines

Data visualization is not without rules and principles, but rather follows some standards and guidelines to ensure quality and consistency. You want to adhere to the best practices and conventions of data visualization, as well as the specific requirements and expectations of your bank and regulators. You can use techniques such as templates, formats, or styles to create a uniform and professional look and feel for your reports. You can also use techniques such as data validation, verification, or documentation to ensure the accuracy and reliability of your data. For example, if you want to comply with the Basel III framework for risk reporting, you may use the standardized templates and formats provided by the Basel Committee on Banking Supervision.

5 Seek feedback and improvement

Data visualization is not a one-time or static process, but rather a continuous and iterative process. You want to seek feedback and improvement from your audience and stakeholders, as well as from your own evaluation and analysis. You can use techniques such as surveys, interviews, or tests to collect and measure the feedback and satisfaction of your audience. You can also use techniques such as benchmarks, metrics, or indicators to assess and monitor the performance and effectiveness of your reports. For example, if you want to improve the usability and accessibility of your reports, you may use the Web Content Accessibility Guidelines (WCAG) to check and improve the design and functionality of your reports.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?