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Every piece of wealth-management software that has a front-end needs tools to present data in a visual way. In today’s article, I want to share some means and tips on visualizing wealth-management data.

What is displayed visually?

As a rule, WealthTech projects use various and sundry kinds of data. Almost any data may be used for reports—this is where visualization is most often required.

Visual imagery may be used to show the following data:

  • Current status (for example, asset allocation, number of clients, assets under management):
    Statistics visualization
    Source: https://portfolios.insart.com/
  • Results of analysis (for example, risk profiling):
    Analysis visualization
    Source: https://www.riskalyze.com/
  • Statistics (for example, historical returns, investment changes for different groups of investors):
    Historical data visualization
    Source: https://portfolios.insart.com/
  • Forecasts (for example, possible portfolio returns):
    Forecasts visualization
    Source: INSART’s internal project
  • Dependencies and correlations (for example, similar historical performance, dependencies between stock returns):
    Dependencies visualization
    Source: https://correlate.pro/
  • Comparison (past returns of different assets, asset classes, types of portfolios):
    Data comparison visualization
    Source: https://portfolios.insart.com/

Depending on the purpose, you may choose to use different types of visuals, such as pie, timeline, bar, tree, or bubble charts.

How should it be designed?

The main factor that you should consider is the competency of the target audience. Charts created for financial advisors may be much more complicated compared to those created for end investors, especially if the latter are new to investing.

Most investors will understand pie and line charts if you show no more than two or three segments/lines in each chart. This may be a pie chart that shows their portfolio allocation or a line graph with expected or past returns.

On the contrary, advisors prefer to see much more information on the screen without the need to request additional data. For example, they may need all data about a particular client or household, such as:

  • Their full risk profile, including individual risk tolerance and mutual risk capacity, and their behavioral analytics;
  • Current portfolio allocation, its value, and how it has changed over time;
  • Statistics about rebalancing, including realized and unrealized gains, etc.

All this data will require various visuals—tables, charts, and graphs of different kinds depending on what they show. For example, to compare the growth of two portfolios over the years it’s better to use timelines. To show asset allocation in a portfolio, pies or bars are mostly used.

Asset allocation visualization
Source: INSART’s internal project

For visualization, we most often use such tools as D3.js, Highcharts, ChartIQ, Qlikview, FusionCharts, etc.

What about data?

Because data is the basis for any visualization, it’s crucial to prepare it thoroughly. Here, the coordination of all groups of people working with the data becomes vital:

  1. Business analysts and product owners explore what types of data are required and why they may be useful.
  2. UI/UX experts provide the design according to business goals—what data and changes should be displayed and how this should help financial advisors and investors.
  3. Database engineers and big data engineers process the data to convert it into a consistent format that is best suited for storing and rendering.
  4. Back-end and/or front-end software engineers work on visualization.

Data transfer between database (or warehouse), back-end, and front-end is mostly provided via APIs. The API documentation enables precise coordination.

I would say that the following data-preparation tools are the most popular in our teams: Alteryx Analytics, IBM Watson Analytics, TIBCO Spotfire, etc.

Some tips

Some charts may be too complicated. In this case, you may add interactivity to show additional information about assets, investors, etc. if required. When an advisor clicks certain parts of a chart, a popup appears to show quantities, prices, dates, and whatever else the advisor may need. The popup may include text, a table, or another chart.

Advisors may also need to export all or some of the charts to show or mail them to their clients. So you may provide your platform users with this feature.

If you show a number of charts on one page and data for them is prepared on the fly on the back-end, don’t use synchronous requests, or your platform users may wait a long time until the system gets the data for each chart piece by piece. Asynchronous requests won’t block the system; each chart will appear once the data for it is ready. In some cases, the last chart may appear earlier than the first one.

Use contrasting colors for data that differ greatly—for example, stocks and bonds, passive and active investors. For example, if you show several stocks and bonds in one portfolio, you may use light colors for one asset class and dark colors for the other. However, stay consistent with commonly used colors. For example, green is best used to show positive and red for negative portfolio returns.

Takeaways

In wealth management, visualization is more than just displaying data. It helps with analyzing and interpreting data. It improves the client experience and makes extensive data easier to comprehend and use. This is why visuals have become an integral part of any WealthTech platform. In the article, we discussed several important aspects that are worth considering when you seek to visualize wealth-management data.

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