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Rethinking the Wholesale Distribution Workflow (Part 2)

Wealth Management

By Thomas Etheber, PhD, CFA  |  September 8, 2021

The composition of high-quality financial investment advice has been evolving for years. Services once available only for the rich are being democratized for less wealthy audiences. This raises the bar for every advisor across all wealth and sophistication levels. High-quality advice is about acquiring the broadest possible 360-degree view on investors’ finances, and in particular, a holistic view of investors’ family bonds, their respective portfolios, risk profiles, investment constraints, and investment goals. Additionally, clients request more complex services such as financial, tax, and estate planning. In short, advisors are moving from purely product-oriented to solution-oriented sales and advisory processes. Investment advice is no longer about finding the best product—advice is about finding an integrated solution for investors’ needs. That implicitly means that individual investment products—regretfully for many wholesalers—are becoming largely replaceable.

Against this background, many wholesalers are transitioning their current service offerings to new and more advanced levels. One approach is to support their in-house specialist teams with advanced analytical platforms. In this series of articles, we concentrate on the technology part of this story and review some key characteristics of platforms. Previously, we discussed the importance of developing a modern wholesale distribution platform that is data-driven, evidence-based, scalable, convenient, as well as open and interoperable. Here we focus on having a platform that is informative and incorporates real-time monitoring.

key-characteristics-modern-wholesale-distribution-platforms

Informative

From an abstract point of view, financial advisors are intermediaries who have better access to information and for this reason, are hired to help make better investment decisions. Accordingly, the advisor’s recommendations should, of course, be in the clients’ best interests. It has been shown that this setup suffers from significant conflicts of interest and principal agent problems. To justify their commissions, advisors’ service provisioning must be perceived as valuable by their clients. If that is not the case, no client would be willing to pay for advice. The same holds true for wholesalers when they try to convince advisors of their products and services.

Wholesalers can supply the analytical platform, which helps advisors to provide the requested evidence-based advice. When thinking about such a platform and its features, there are a few things to note: first, quantitative tools are sensitive to underlying assumptions and data inputs. In their process of engagement, many investment firms start by running portfolio diagnostics, which typically means the computation of historical time-series-based analytics either on a single fund or on a full portfolio level. These computational tasks, including the subsequent generation of analytical reports, can be automated and run without any human interaction. However, when the engagement process continues and it comes to more advanced services such as portfolio construction or financial planning, the quality of the analytical output severely depends on correct and adequate user input. In that case, the output sensitivities are suddenly highly relevant. By collecting the right numbers and putting them into perspective, advisors can contribute and earn their fees.

By collecting the right numbers and putting them into perspective, advisors can contribute and earn their fees.

Accordingly, advisors must concentrate less on finding the correct products. Their value add is more about helping clients to understand complex concepts of investment risks and how different risks might be compensated in capital markets. For instance, advisors might want to discuss different types of investment risks across economic environments or time. The analysis should incorporate client’s individual circumstances to provide a sense of what realistically might happen with her investments. It should also point out potential effects on the given investment objectives such as future cash flow requirements. Finally, it should enable the advisor to calibrate recommendations to the desired risk levels. The required functionality to run those computationally-intensive tasks can easily be embedded in modern wealth management platforms and exposed at scale to the advisors’ desks. Along with the related services offered by the firm, this constitutes the degree of just-in-time financial education which helps advisors to build stronger relationships and helps investors to make better investment decisions for which they are willing to pay the extra fees.

Additionally, the question about the correct presentation formats of the achieved results arises. How can you build a solid chain of reasoning around quantitative results? What would be the best presentation format for this line of argument? The answer is: “It depends.” If a wholesaler is interacting with professional counterparts who just want to benchmark their internal perspectives, a likely choice could be to expose the internal API to them. This would allow them to directly source the underlying analytics and reports and perform the benchmarking exercise without any media breaks conveniently within their information technology systems.

The main use case, however, is that a wholesaler either will want to pitch its service to some advisory firm or enable that firm to pitch it directly to end clients. In both cases, some form of presentation or output format is needed. Having access to the full breadth of analytics frequently lures firms into creating extensive reports containing large amounts of numerical data, statistics, and key performance indicators (KPIs). This approach creates zero differentiation from competitors. Every competitor has its own complementary reports containing standard portfolio analytics such as Sharpe ratios, Treynor ratios, alphas, value at risk (VaR), etc. While these statistics might matter for financial professionals, the truth is that your clients are not interested, and from a sales perspective, that might even be the best-case scenario. If a client suddenly cares, the investment competence advisors intend to show with these analytics might quickly deteriorate.

Crunching vast amounts of analytics and other numerical data will lead to questions and contribute to overall confusion. That most likely does not help during client meetings. What is really needed is a solid and intuitive storyboard, enabling the explanation of respective investment theses, the resulting recommendations, and the complex interplay of different types of investment risks in a visually appealing and understandable form. For sales success, it is of the uttermost importance that clients can relate to what they see and are being told. Understandability builds trust and trust (due to ambiguity aversion) leads to better conversion ratios. In the end, comprehensibility trumps a few basis points of better investment performance—even more so since the signal-to-noise ratio in financial markets is low and investors’ learning capabilities are limited. Thus, repackaging portfolio analytics into easy-to-understand visual representations such as graphs, figures, and informative pictures goes a long way in this direction. It helps much more than crunching numbers or reviewing tabulated data. Furthermore, advisors want to fine tune certain parameters or instantaneously react to client questions and discuss the effects. Such a dynamic and interactive demonstration and fine tuning often contribute to a fundamentally better understanding and a more convincing sales pitch.

Analytical output should be interactive, flexible, visually appealing, and backed by evidence.

In short, the analytical output should be interactive, flexible, visually appealing, and backed by evidence. If a client asks for the underlying numbers (e.g., KPIs), the software conveniently allows them to dig deeper, but advisors should not start at the lowest possible level. Most clients might not care about any specific portfolio analytics. The key to success is about establishing trust and telling a convincing story, which if challenged, can be backed by data and sound reasoning.

(Real-Time) Monitoring

For advisors, caring about OPM (“other people’s money”) does not stop after the sale. In the first place, clients consult with advisors because they want to have an expert on their side who is looking after their investments and is providing guidance where and when it is needed. The only challenge though is as margins are coming down the loading per advisor is typically increasing. The remedy again can be found in technology—in this case, the automation of portfolio monitoring.

Modern analytical platforms allow advisors to monitor thousands of portfolios in real time in a fully configurable way. The bread-and-butter business of those monitoring services is democratizing portfolio management functions formerly only available for professionals, such as controlling a range of individual risk limits and other investment constraints. However, extended monitoring functions can be embedded in configurable workflows and can autogenerate new talking points for relationship managers. These systems could, for instance, detect cluster risks, approaching maturities of securities, portfolio drift, replacements of fund managers, changes in recommended short lists, etc. The monitoring infrastructure will push notifications to advisor dashboards where advisors can easily review their full book of business and take appropriate actions.

Important efficiency gains and increased quality standards can be realized when monitoring functions are more deeply embedded into well-designed workflows. For instance, a relationship manager could add value to the associated advisory firms by using the analytical platform to run cross-sectional reports and filter out portfolios, which require some sort of action. The latter could even be done on a regular basis by sending over these “leads” with the fitting argumentation as prepared by wholesalers’ subject matter experts (e.g., when detecting elevated risk levels or cluster risks).

Important efficiency gains and increased quality standards can be realized when monitoring functions are more deeply embedded into well-designed workflows.

Another recent example, where well-designed and integrated monitoring workflows would have led to high client recognition, is the severe COVID-19 market turmoil in March 2020. What would have happened if widespread real-time monitoring were in place for your clients at that time? The likely answer is that unless your monitoring service had been integrated into an automated workflow, your relationship managers would have seen an overflow in their advisor dashboards because virtually all alarm bells across all portfolios went off simultaneously.

How did your business deal with these extraordinary COVID-19 events? During these periods of stress most advisors likely just reached out to their preferred clients and the remaining clients (at best) received broadcasted and unspecific messages, while many may not have heard anything from their advisors. This is clearly unsatisfactory because during periods of stress, end clients obviously need reassurance. Why not leverage your internal specialist know-how and provide this kind of reassurance?

An outline of a respective workflow chain could look like this: Once volatility increases and markets turn south, monitoring notifications will begin to flood your dashboards. That could directly trigger a communication workflow where your economists, fund managers, and marketing teams are obliged to provide their perspectives on what is going on, what the near-future will most likely look like, and which preventive actions have already been or will be taken. Time-based trackers and respective escalation routines monitor the timely provisioning of this critical reassuring expert information, which once entered into the system will be distributed to the relationship managers and advisors. Depending on the asset allocation of each (model) portfolio, the newly created expert content is picked up and prefills dedicated mail templates. The result of this process step is an auto-generated draft of asset-allocation-specific client information, which would automatically load once a relationship manager opens a notification for a given portfolio. The relationship manager may briefly review the suggested communication and make edits as needed. An embedded learning system will, of course, remember the edits for the next comparable model portfolio.

Time-based workflow monitoring systems will ensure that in the next crisis your advisors and their clients get the desired reassuring high-quality and targeted information. Timely crisis-response notes of this kind provide scale and speed and offer advisors what they might be hoping for: high-quality expert information and good lines of argument.

In the next article in this series, we will continue to explore the characteristics of a modern wholesale distribution platform: it should be personalized and emotionally engaging as well as extendable.

Other articles in this series:

Rethinking Financial Advice from a Wholesaler’s and Technology Perspective

Rethinking the Wholesale Distribution Workflow (Part 1)

This blog post has been written by a third-party contributor and does not necessarily reflect the opinion of FactSet. The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.

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Thomas Etheber, PhD, CFA

Head of Investor and Marketplace Solutions, Upvest

Dr. Thomas Etheber is Head of Investor and Marketplace Solutions at Upvest. In this role, he and his teams are reengineering and innovating the machine room of today’s securities markets’ infrastructure. Prior, he was a lead consultant on market development at FactSet and has held a variety of client-facing roles in professional services and market development in the financial services industry where he primarily specialized in digitizing financial advisory processes. Dr. Etheber earned a doctorate in finance from Goethe University Frankfurt and is a CFA charterholder.

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