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.

In robo world, B2B = buyer beware

The success of robo advisors in commoditizing the historically manual portfolio management process is proving their Achilles heel, as I noted in my last post. Incumbents have taken over the narrative. Yet the efforts of these incumbents to build, buy and partner with the robos comes with its own risks.

Foremost among these is how to implement robo advice within a multichannel ecosystem. As discussed in the report, Getting the House in Order: Consolidating Investment Platforms in the Wake of the Department of Labor Conflict of Interest Rule, the ability to deliver consistent advice across channels has become paramount in the new regulatory environment.

This consistency requires a clear view of assets held in house, which in turn implies eliminating product stacks and their underlying technology silos. Of the big four US wirehouses, Bank of America Merrill Lynch has led the way by consolidating five platforms into one. Their competitors are still trying to solve the problem.

Regional banks, with their legacy tech and limited budgets, are going to have a hard time getting this right. Asset managers are eager to help them launch robo platforms, despite the “me too” nature of the banks’ efforts. 

It’s hard to blame these asset managers for wanting to distribute their wares. B2B sales are in their DNA. But I’d point out that their headlong rush to abet bank robo contrasts with their cautious efforts to roll out on their own platforms.

Schwab spent months and millions to launch Intelligent Portfolios. UBS has moved much more slowly, and appears to be using SigFig as a placeholder until it can achieve the technological and service clarity demanded by clients and regulators alike. Fidelity danced with Betterment before rolling out Go through its retail branches. It's tepid if not touch and go.   

I don’t begrudge asset managers for taking their time. They have their own considerations, foremost distribution. That’s why they are enabling bank robo capabilities, even if it's not clear exactly how the banks will manage this. Why not give the teenager the keys to the Audi? But with their own clients, they have to get things right. They have shareholders to answer to, and the stakes are much higher.

FinovateFall 2016, NYC: Day 2

Below is a selection of companies, which demonstrated solutions that can be used in the wealth management space. The Best in Show awards went to: 1) AutoGravity, 2) Backbase (mentioned in my blog post yesterday, FinovateFall 2016, NYC: Day 1), 3) Clinc (profiled below), 4) MX, 5) Swych and 6) Trusona

Envestnet│Yodlee:

Envestnet│Yodlee presented their dynamic intelligence solution. Envestnet│Yodlee walked through an example with a fictional user, Amanda, a 29 year old with $85,000 in student debt. Amanda just received a $15,000 signing bonus.  A chatbot alerts Amanda of the new deposit and provides Amanda with three ways to use the cash: 1) pay down her credit card debt, 2) use the money for emergency savings, or 3) pay down her student loans.

Envestnet│Yodlee has data on 22 million customers in 15 countries. As such, Envestnet│Yodlee has looked at data across all of Amanda’s financial institutions and has data that shows Amanda has stopped contributing to her prior’s employer’s 401(k).  Therefore, Envestnet│Yodlee infers that the $15,000 is not a recurring deposit.

The chatbot offers Amanda detailed information on each of the three suggestions listed above. The chatbot uses Amanda’s financial information and best financial practices to offer additional information on each of the three recommendations. For example, when Amanda asks for additional insight on putting the money away in an emergency account, the chatbot provides information on how much money Amanda needs to cover one, three, or six months of spending based on her habits. 

IBM Customer Insight:

IBM Customer Insight, the second IBM product to be demonstrated at FInovate, is a dedicated cloud system.  It provides cognitive insights derived from third-party sources, customer transactional and behavioral data.

The Finovate demonstration showed what a regional manager at a bank would see when using IBM Customer Insight. The fictional regional manager, Harry can use IBM Customer Insight to predict customer attrition, mortgage churn, overdraft, and large deposits. Also, Harry can use IBM’s system to study the sum of a customer’s life events. Harry can look at one life event, such as a relocation to get information on other possible life events, like purchasing a home or retirement.

M1 Finance:

M1 Finance, a portfolio management tool, announced their public launch at FinovateFall 2016.  Their product allows users to create, organize and automate their investment portfolios.  An individual can choose to create a default M1 portfolio or create their own portfolio. M1Finance has three default portfolios: 1) Savings, 2) General, and 3) Retirement.  The portfolios are all displayed graphically as a pie chart.

In the presentation, the M1 Finance Savings portfolio was selected.  A user can then choose to edit the savings portfolio investments.  For example, a user can search the investments in the savings portfolio for FANG stocks (Facebook, Amazon, Netflix, and Google) and opt to group those stocks together.  The user can track this FANG group separately and even choose to increase the weighting of the FANG stocks.  Every slice of the pie is a visual representation. For example, if the perimeter of the FANG stocks are not be in line with the rest of the pie’s perimeter it indicates the FANG weighting is over/under target allocation.

M1 Finance is available on the web, android and iOS.  In the presentation, M1 Finance said they do not charge rebalancing commissions.

Qumram:

Qumram demonstrated how “digital business and compliance can co-exist.”  Qumram records every digital interaction, plays them in movie form, and stores the videos for as long as is required by regulation.  Currently the product is used by UBS globally.

In today’s demonstration, the Finovate audience witnessed an interaction between an advisor and a client on WhatsApp. The fictional client was Patrick, who wants to invest $30,000.  After the advisor’s conversation with Patrick concludes, the conversation is recorded and automatically categorized with the client’s name, advisor’s name and products mentioned, e.g., LinkedIn (LNKD). 

Clinc:

Clinc is another dynamic intelligence solution.  The application responds to conversational language. For example, in today’s demonstration, the demonstrator said, “I am thinking of getting something to eat after Finovate in NYC. Can I afford $150 on dinner?”  The application responded with the user’s monthly average spending on eating out.  The application also added that if the user were to spend $150 on dinner tonight that the user would still be 10% below their average monthly spend. Additionally, with the use of the Clinc app the user can move some money between accounts.

FIS:

FIS discussed their new card-less cash technology. The FIS presenter showed that an individual can retrieve cash from an ATM without inserting a card.  A user can save their preferences on their mobile device. For example, if a user usually takes out $40 at a time, then that preference can be saved. When at an ATM, the user can select card-less cash as an option. The user then scans the QR code on their mobile device. 

FIS also showed that when a user approaches an ATM that is behind a locked door, the user can open the door with their mobile device. Lastly, FIS demonstrated that with their technology allows a user to send money via Amazon’s Alexa. The demonstration closed with a video of the individual in India collecting the money from an ATM located in India. 

With the addition of FIS’ latest partnership there will be 100,000 ATMs with card-less cash optionality.  Additionally, card-less cash works at participating grocery stores, convenience stores and pharmacies.