Roll over, don’t play dead

In my most recent report, Wings of a Butterfly: Regulation, Rollovers and a Wave of Optimization Software, I discuss the challenges the DoL conflict of interest rule poses to the $7 trillion IRA rollover business. These challenges center on the need for advisors to break down 401k plan costs and make apples-to-apples comparisons of proposed rollover solutions.   Why focus on the rollover? First, the rollover decision serves as a touchstone in the relationship between client and advisor. Trust sits at the center of recommendation to roll over, and seldom are the vulnerabilities of the client so exposed. The importance of the ┬árollover decision is further magnified by timing. It often takes place at the apex of client wealth, where the consequences of missteps for the investor can be severe. For the advisor, the rollover offers a unique opportunity to capture assets, or at least advise on their disposition, as well as present a coherent strategy for drawdown.   The implications of the decision to roll over extend beyond the client advisor relationship to firm strategy, of course. They are particularly relevant to product development and distribution. I’ll discuss these implications in a later post.

Motivations behind Outsourcing in Wealth Management

This year Celent surveyed technology providers that service wealth management firms. The goal of the survey was to learn the motivations and strategies of wealth management firms that outsource components of their business to third party vendors.  The last time we did this survey was five years ago.

From the survey, we learned that one of the main drivers of outsourcing today is so wealth managers can experiment with the latest technology before committing vast resources to a technology that may only be a fad.  Similarly, wealth management firms are eager to outsource because it allows them to scale up or down their operation, or enter new regions, quickly and efficiently.  Wealth managers prefer to work with a technology provider to test ideas, tools and regions, before building a permanent team and spending on fixed costs.

Several motivations to outsource have become more important today than they were five years ago. These motivations include: to improve efficiency, to enrich the customer experience, and to respond to regulation.  Over the last five years, across the world we have seen a push for more stringent regulation.  Therefore, it is not surprising that regulation is top of mind for most wealth managers.

As products and services are increasingly commoditized, it is important for wealth managers to distinguish themselves via the customer experience. It is likely that over the next 12 to 18 months, wealth managers will spend relatively more time on outsourcing front office operations. For example, firms may look to vendors for improvements in: the onboarding experience, components of the advice and planning process, and help desk services.

For more information on the global outsourcing landscape in wealth management, please see my report, Outsourcing in Wealth Management: The Drivers and Strategies.

Wells Fargo rides herd on DoL

It’s no coincidence that Merrill Lynch launched its new robo platform the same week it decided to exclude commission based product from IRAs. Likewise, the decision by Wells Fargo to announce a robo partnership with SigFig suggests that despite the pronouncements of pundits and industry lobbyists, DOL is hardly DOA.

It takes a brave man to guess how the Trump administration will balance populist tendencies with free market rhetoric. In this case, as I note in a previous post, the inauguration of the new president precedes DoL implementation by less than three months. The regulatory ship has left port, and in any event, it's not clear that President Trump will want to spend valuable political capital undoing DoL.

I’ll discuss Wells Fargo’s motivations in a later post. For now, I’ll note the degree to which a robo offer aligns well with the principles of transparency, low cost and accessibility at the heart of DoL. At the same time, I caution the reader to consider the challenges that any bank faces in rolling out a robo platform, a few of which I underscore in this column by Financial Planning’s Suleman Din.

DOL or DOA? The Election and the Conflict of Interest Rule

It’s one of those watershed moments. Clinton wins, and the Department of Labor (DoL) conflict of interest rule takes hold and likely gets extended beyond retirement products to all types of investments. Trump wins, and DoL gets slowed down and perhaps even rolled back.

Assuming Clinton wins (which appears likely) firms will need to gear up on three fronts:

  • Platform: DoL makes paramount the ability to deliver consistent advice across digital and face to face channels. Such consistency requires a clear view of client assets held in house, which in turn implies eliminating legacy product stacks and their underlying technology silos, as I note in a recent report.
  • Product: Offering only proprietary products only is a non-starter under DoL. But too much product choice can be as bad as too little. Firms must demonstrate why programs and portfolios offered are the best for each particular client.
  • Proposition: In a best interest world, the client proposition must extend beyond price. Client education, transparent performance reporting and fee structures, as well as an easy to use digital experience, will distinguish stand outs from the broadly compliant pack.

None of the pain points above lend themselves to easy solutions. As such, the banks and brokerages most affected by DoL are struggling to develop processes that go beyond exemption compliance. I’ll discuss more comprehensive approaches in the All Hands on Deck: Technology's Role in the Scramble to Comply with the DOL Fiduciary Rule  webinar I’m co-hosting this November 14.

I hope you will join me for the webinar, and in the meantime, you will share your thoughts and comments on this post.

New Report: Changing the Landscape of Customer Experience with Advanced Analytics

Today’s financial consumer enjoys unprecedented information and choice, both in terms of channels and access to third party or crowdsourced opinion. Higher expectations support (and in part reflect) the skepticism that to a large degree defines the Millennial generation. These expectations underscore a fundamental shift in the power balance between the client and wealth manager, one reinforced by regulation such as the US Department of Labor conflict of interest rule

The ascendance of the client should be a call to action for wealth managers. As I discuss in a new report authored with my Celent colleagues Dan Latimore and Karlyn Carnahan, wealth management firms need to operationalize insights from new data sources, and bring servicing models up to date with their more sophisticated understanding of the client.

Campaigns and next best sales approaches that have worked in the past (or at least well enough to encourage firms to invest man hours in their design and execution) must be brought into the digital age. Too often these campaigns are a blunt hammer: they are built to sell product and ignore the evolving needs of the individual client, as well as the multiplicity of digital touch points useful to reach him or her. It is hardly surprising that the client reacts negatively to the presumption inherent in these offers.

Introducing The Cognitive Advisor

Last week I published a report on a topic that has interested me for some time: the application of artificial intelligence (AI) technology to the wealth management business. To date, neither Celent nor its industry peers have written much about this topic, despite clear benefits related to advisor learning and discovery. This lack of commentary, and the industry skepticism that underlines it, reflects successive waves of disappointment around AI, and more recently, competition for research bandwidth from other areas of digital disruption, such as robo advice.

Another inhibition relates to taking on an industry shibboleth. How to reconcile AI or machine intelligence to the hands on, high touch nature of traditional wealth management? This challenge is real but overstated, even when one reaches the $1 million asset level that has defined the high net worth investor. Indeed, the extent to which wealth management is a technology laggard (in general, but also when compared to other financial services verticals) highlights the opportunity for disruption.

In particular, AI offers a means to circumvent the dead weight of restrictions presented by antiquated trust platforms and other legacy tech, a weight which reinforces advisor dependence on spreadsheets and other negative behaviors. As is set out in the report, it is precisely the combination of new behaviors and technologies that can help surmount the finite capabilities of the human advisor.

Guidance, not advice

Last week Merrill Lynch announced the launch of its long awaited Guided Investing robo advisory platform. Investors get access to a fully automated managed account for only $5,000, compared to the $20,000 required for call center driven Merrill Edge.

A new type of hybrid model

It’s interesting that Merrill Lynch would launch another managed account platform at this point, given the narrow gap between the two program minimums. But industry wide fee compression underscores the importance of cost savings, and with Merrill Edge’s best growth behind it, even a call center is expensive compared to a digital first approach.

I say “digital first” because Guided Investing clients can still get access to a human advisor. In this case, however, the advisor delivers (in the words of a Merrill spokesman) “guidance” and “education”, and not investment advice. Advisors are able to explain product choice as well as why and how a portfolio is rebalanced, for example. Such capabilities reinforce the Merrill message that its portfolio models are not just algo driven, but managed by the CIO.

Compliance friendly

The compliance friendly terms “guidance” and “education” give another clue to Merrill’s intentions. Like BlackRock and other asset managers discussed in my previous post, Merrill wants to get ahead of the DoL rule and fill the advice gap that will be left by the rollout of a uniform fiduciary standard across both the qualified (retirement) and taxable investment spaces. It’s worth pointing out that Merrill announced its decision to stop selling commission based IRA accounts the same week it launched Guided Investing.

Compliance and economics are powerful (and mutually reinforcing) motivations. Especially when the economics are not just about cost savings, but about the chance to develop a whole new client segment. Guided Investing represents not just another robo platform, in short, but an effort to lower delivery costs and fill out the range of options Merrill offers clients, particularly younger and self-directed ones.

Merrill believes (correctly, in my view) that this type of managed investment solution will be as ubiquitous as mutual funds within five years, and so it has no choice but to move forward. Vanguard finds itself at the same crossroads, which is why the firm’s plan to launch a fully automated robo platform (as a complement to its $40 billion AUM Personal Advisory Services hybrid program) is probably the industry’s worst kept secret.

 

Shining light on the thinking at BlackRock

It’s clear that there’s more than a little chutzpah behind BlackRock’s demand for tougher regulatory oversight of robo advisors. This post probes the thinking behind it.

Does BlackRock, with FutureAdvisor in hand, want to shut the door on new robo entrants? A desire to forestall such competition would suggest a level of fear that I do not think exists. (Among other things, the robo narrative has moved past the independent or 1.0 stage). BlackRock’s main concern seems to be that the sloppy hands of existing competitors might result in regulatory sanction on everyone, and so put the hegemony enjoyed by BlackRock and its asset manager competitors at risk.

Neither faster, nor better, nor cheaper

While BlackRock may have paid $150 million for FutureAdvisor, I don’t think the firm believes it owns a better mousetrap. FutureAdvisor may have an innovative glide path feature (which may explain why FutureAdvisor has an older clientele than its robo competitors), but tax loss harvesting, 401(k) advice, “try before you buy” functionality and other core capabilities have become table stakes in robo world. If anything, BlackRock may believe that its proprietary ETFs (characterized by low tracking error and a broad product base, e.g., Japanese fixed income) outshine the plain vanilla offerings of Schwab and Vanguard, although this argument is undercut somewhat by the firm’s recent decision to drop fees.

Asset managers in the catbird seat  

Like the ETF business, robo advisory services have become increasingly commoditized, even as the DoL conflict of interest rule presents a massive tailwind for both. It’s a tricky time for asset managers seeking to shift their offer from manufactured product to advice based solutions.  BlackRock appears to feel it is in the catbird seat, and is perfectly happy to secure its hand and that of its asset manager competitors, all of whom have done well by automating their investments platforms. I’m not saying there’s collusion here, just a noteworthy confluence of interests.  

I’ll talk about the motivations behind the launch of another asset manager-backed robo in my next post.

Coaching the Advisor: Predictive Analytics and NLG

Predictive analytics and natural language generation (NLG) are used throughout the financial services today, but are used less frequently in the case of wealth management. Narrative Science and Yseop are two of the few NLG companies currently selling to wealth managers.  IBM Customer Insight is bringing its cognitive Watson technology to the wealth management industry.  IBM Customer Insight is doing cool things related to behavioral segmentation, encouraging wealth managers to create more robust customer profiles by casting their data net beyond customer income information. More in my blog post here.

There are many use cases for NLG and predictive analytics in wealth management.  For example, predictive analytics can assist with risk management by spotting anomalies in real-time, and therefore, enable advisors to resolve issues faster. NLG can be used to transpose customer data into a short narrative that summarizes a client’s performance.  This narrative could be shared in an email to the client or used by an advisor preparing for an in-person client meeting. 

In my report, Coaching the Advisor: Predictive Analytics and NLG in Wealth Management, I focus on how predictive analytics and NLG solutions can enhance advisor-client relationships, the attributes of competitive analytic solutions, and propose best practices for the modern advisor. I stress the importance of advisors’ establishing genuine connections with their clients in the midst of adopting new analytic tools.

In the next 12 to 18 months, I anticipate wider adoption of these tools in the wealth management industry.

Finally, Behavioral Segmentation for Wealth Management

Yesterday’s IBM Forum for Financial Services showed Watson’s capabilities for wealth management, insurance and banking. The forum coincided with the 2.0 release for Watson’s application in financial services.  

The demo session emphasized Watson’s behavioral segmentation capabilities.  The psychographic measures delivered by IBM Customer Insight are impressive.  For example, measures include but are not limited to: openness, liberalism, cautiousness and sympathy.  Knowing all too well that a client’s personality can make them candidates for different products than what a client’s paper profile would suggest, it is easy to see how in the wealth management space, advisors could use these measures to build deeper human connections with their clients. 

I was also impressed by the fact that Watson shows financial advisors the reasoning behind the machine learning results.  Gaining insights into how Watson arrives at its recommendations empowers advisors to validate the results or add human refinement based on inputs not available to Watson.  In this way Watson can complement the financial advisor. 

It seems that there is even more that IBM is working on that is not in this latest release, so I’m looking forward to what more there is to come.