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Rethinking the Wholesale Distribution Workflow (Part 1)

Wealth Management

By Thomas Etheber, PhD, CFA  |  August 25, 2021

Globally, financial advisors are expanding their breadth of services and are deepening their relationships with clients. Furthermore, there is a trend where advisors, instead of shopping around for the best offer, are seeking to hold close relationships with only a few selected asset management partners.

To protect their important advisory distribution channel, wholesalers need to adopt a digital-first mindset, improve organizational nimbleness, rethink current advisory workflows, and leverage the capabilities of modern technologies to realize efficiency gains and offer novel value-added services to sales staff, advisors, and clients. In this series of articles, we will concentrate on the technology part of this story and review some of its key characteristics, which have turned out to be important for its implementation success.

The Wholesale Distribution Workflow

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 of investors’ finances, and in particular, a holistic view of investors’ family bonds, their respective portfolios, their 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-orientated 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.

With this transition, advisors’ questions raised to their wholesaler partners have naturally gained in complexity. They request investment know-how beyond simple product pitches. However, wholesalers’ sales forces are often not well prepared to deliver on these requests. While all of them are experts in their product domains, only a few have the competencies to deliver on more complex needs, such as sophisticated portfolio construction or financial planning services. Because these higher-value services are not only more demanding from a communicative point of view but also from a data, analytical, and computational side, clearly technology plays a significant role. There simply is no human who can perform sophisticated mathematical optimizations without any technological support. That is one reason why many wholesalers began to set up specialist teams. These teams are often not only responsible for building and maintaining their internal tooling (e.g., for portfolio construction) but also are employing their tools to deliver on advisors’ requests.

Many wholesalers are currently transitioning these exact services to a new and more advanced level by supporting their specialist teams with advanced analytical platforms. Having worked with a multitude of wholesalers on developing diverse next-level wholesale distribution platforms, we want to share a few learnings and best practices. From our experience, a modern wholesale distribution platform has at a minimum the following seven key characteristics:


Source: FactSet

Let us take a more detailed look at each of these characteristics and yet note that only the combination of multiple and at best, all of them, will allow you to achieve extraordinary results.

Data-Driven and Evidence-Based

Today’s investors no longer want to see investment decisions based on gut feelings, but rather they expect plausible evidence-based conclusions. The new breed of wholesalers and advisors should understand their role as a sparring partner for clients, providing guidance and helping with interpreting quantifiable conclusions. Consequently, the core element of the overall platform is the analytical engine, where a lot of a firm’s financial knowledge resides. Modern API-module-based platforms allow existing quantitative specialist teams to expose their specialist knowledge to the rest of the world by simply configuring the underlying software modules. These analytical software modules help to close the existing gap between a firm’s strong investment competence located in specialist teams at their home office and the available know-how at the point of sale, i.e., the wholesalers’ relationship managers and befriended advisors. The latter is a comparatively easy task when analytical thoroughness is combined with flexible and easy-to-manage workflows, which hide the underlying complexity from less sophisticated users.

Potential Components of a Module-Based Platform


Source: FactSet

The figure above contains an example of a possible configuration of such an analytical platform for wholesalers. Of course, several specialized software vendors can offer quantitative calculation engines; some are more advanced, others are less computationally demanding, but in comparison to current average market standards, nearly all of them can provide better data-driven insights.

Nevertheless, analytical engines on their own are not sufficient for achieving sustainable market success. There are a few other equally important key drivers. One certainly is data consistency. Data consistency means that all layers of the overall platform are working on the same set of data and analytics. For example, a portfolio manager in the home office would have access to the same market data and portfolio analytics as the wholesaler’s salesforce at the point of sale. Modern API technology stacks allow easy and lean integration of different systems including graphical user interfaces such as PCs, tablets, or mobile phones. This means that the same information can be exposed to external advisors and potentially to their clients.

Another important factor is to integrate these computational modules into recognizable, easy-to-use, and targeted workflows. These workflows not only contain the proprietary investment know-how as an integral ingredient but also allow the wholesaler to signal and openly advertise it to the market. That is exactly what brings the necessary market differentiation for the implementing firm and that is what no single software vendor ever will be able to accomplish on its own. Furthermore, that is also the critical task, which many software vendors with their legacy monolithic infrastructure cannot accomplish. A modular API-first approach is clearly a strategic must for modern IT systems and allows for future expandability. In sum, quantitative, evidence-based computing power seldom is sufficient, unless it is fully embedded into a broader differentiation, brand messaging, and client-focused strategy. Only the latter is a solid basis for new technology-enabled business practices of the kind we want to explore here.

Scalable and Convenient

As margins continue to decline, efficiency gains along the full value chain need to be a key consideration for management. Consequentially advisors must be able to serve standard requests as efficiently as possible, while simultaneously carving out enough time to investigate more challenging investment problems. That also includes being able to scale your business to a higher number of potential clients. Repeatedly we have seen that wholesalers’ specialist teams, in their need to deliver on more demanding advisors’ requests, have worked on setting up their own internal toolchains. Frequently, these internal tools are Excel- and VBA-based and have grown over some time. There is nothing wrong with this approach, at least at the beginning. Excel is still the most dominant workhorse in finance and a perfect way to start lean and make quick progress. However, Excel and many other homemade tools severely lack robustness, scale, and convenience.

When it comes to the robustness of results, Excel’s ease of use and its overall flexibility is a nightmare. Are you sure, that your Excel sheet does not contain any errors? If you are now, I will ask you again in six months. The number of possible error sources is substantial. Without protective measures, there is no way to be sure of the accuracy of your analysis. These kinds of errors can quickly become very costly.

Scalability comes with automation and automation comes with openness.

In terms of scale and convenience, the picture does not look much better. Effectively, many in-house teams are struggling with the mere (and growing) number of requests. With a few thousand advisors seeking guidance from wholesalers, the number of analyses (e.g., portfolio reviews) easily reaches a few hundred per week. Since producing related analytical reports involves manual work and frequently expert involvement, the current processes simply do not scale well. Scalability comes with automation and automation comes with openness.

To deliver the necessary scale and still create the highest possible client value, wholesalers must make sure that less knowledgeable advisors or relationship managers are still able to interact with the provided system and generate qualitative outputs. That necessitates that user interfaces hide the system’s underlying complexities and yet have the flexibility to drill deeper if needed. Some deployments of highly sophisticated analytical engines have failed to meet the set expectations because too many highly complex functions were exposed to end users and with that required a lot of training and ongoing consulting. As a result, many users turned out to be reluctant to use the system—sometimes less is more.

Open and Interoperable

Openness and interoperability describe the workflow-centric characteristics of a modern wholesale platform. Redesigning existing sales workflows implies defining the necessary data flows as well as user and system interactions. Let us look at a few illustrations of what openness could mean in practice. One example is where a wholesaler grants advisors (partial) access to its analytical platform and thereby effectively implements an advisor self-servicing model. This could happen in the form of easy (model) portfolio intake, where advisors can easily upload portfolios in all possible formats, including Excel, CSV, or PDF. The system would automatically do the matching to asset classes or single securities and create the portfolio for further downstream analysis. That alone frees the internal salesforce from doing repetitive work, which should better be done by machines. Depending on a wholesaler’s strategy, different and more advanced workflow configurations might exist. One wholesaler might want to stick to a very personal relationship-driven approach for elite high-net-worth clients. In this case, interoperability could mean that the system would create the new models not only in the analytical platform itself but would also seamlessly interact with external systems such as in customer relationship (CRM) systems.

Redesigning existing sales workflows implies defining the necessary data flows as well as user and system interactions.

Once a new portfolio upload has occurred, the system would alert the associated relationship manager. It could automatically measure the portfolio’s current risk, run a multi-asset-class optimization to propose an alternative portfolio, and cut the portfolio into sleeves, which are then distributed across different specialist teams (e.g., split between traditional and alternative asset classes). The subject matter experts of each team would also interact with the analytical platform to construct a bespoke investment solution jointly and iteratively. Each team would always revert to reassess total portfolio risks and selects products that best complement the portfolio under construction. Furthermore, such a system could track deadlines to guarantee quick response times. It would inform relationship managers once all analytical steps have been completed by the individual teams and the proposal is ready to be presented to the client. Of course, all these steps would happen within the platform where loosely coupled and reusable modules are linked together via APIs to create bespoke workflows, which not only exactly match a firm’s external value proposition but also its client-facing communication strategy.

Another wholesaler might want to go in a different direction and offer a degree of self-service. The focus would be on quick turnaround times and freeing as much time for internal staff as possible. In this case, the granted platform access might also include access to analytical core modules, such as for example a portfolio evaluation or optimization service. External advisors could run historical analytics, stress tests, benchmark comparisons, fund peer group analyses, or a full-scale portfolio optimization on their own. In addition, advisors have access to all product information and related marketing material. Advisors would create reports without any involvement from the internal specialist teams. In the spirit of owning the desktops, the wholesaler might decide that advisors should be able to customize the created reports with their own logos and corporate identities, thereby fully taking the position of an external analytical service provider for the advisor.

A third wholesaler could decide to run a combination of both approaches and use technology to smartly pre-classify each interaction to allocate (costly) human resources to the most promising sales opportunities. Possible classification schemas might include the source of the information request (is it stemming from a preferred or less preferred partner) or the detected complexity of the portfolio (is more specialized know-how and detailed analysis required).

Another innovative firm might want to position itself as a marketplace, establishing an external advisor and investor community. This wholesaler could for instance allow that different advisors share their model portfolios which each other. This might also imply granting frontend (or even API) access to technologically more advanced community members, which would allow them to interact with the platform directly, run the necessary analysis, or publish their investment solutions on the platform. Other external providers of investment solutions could be allowed to advertise and manage their individual portfolio models on the platform. Frequent interactions with such a digital marketplace by its related community would allow data scientists (e.g., with the help of machine learning tools) to extract valuable insights from the noise.

Data-driven business intelligence can be used to develop ideas for further agile platform development, product creation, and shaping future services.

These data-driven insights could provide answers to questions such as the following: What are the most important analyses requested by advisors, what are advisors looking for, what are the most frequent search terms, which are the most-held securities in competitors’ portfolios, what are upcoming investment trends, and which past proposals were accepted and why? Online ad hoc surveys, which are released with the help of personally-known relationship managers, are examples of modern engagement models, which are employed to collect valuable advisor feedback. This kind of “data-driven” business intelligence is then used to develop ideas for further agile platform development, product creation, and shaping future services.

In the next article in this series, we will continue to explore the characteristics of a modern wholesale distribution platform: informative and monitoring.

Other articles in this series:

Rethinking Financial Advice from a Wholesaler's and Technological Perspective

Rethinking the Wholesale Distribution Workflow (Part 2)

Rethinking the Wholesale Distribution Workflow (Part 3)

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.


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.