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Archive for the ‘artificial intelligence’ Category

Many robo-advisors fail to provide an adequate client assessment

Monday, October 9th, 2017

As the robo-advisor market matures, there is an increasing number of platforms that offer little minimum investment amounts and low fees to attract novice investors across all wealth segments. To provide adequate investment services to unexperienced investors, however, robo platforms must make sure to inform properly about the risk of investing. Even more important, however, is it to provide a thorough client assessment process that covers clients’ financial literacy, their risk tolerance, and their capacity to take risk.

If at all, the average robo-advisor simply asks if a prospect already has some investment experience. Most of these platforms, however, do not take the chance of providing basic educational material for those without any investment knowledge. Only 26% of the robo-advisors do take responsibility and require prospects to first build at least some basic financial knowledge before starting to invest. Interestingly, only 18% of the hybrid platforms (those who provide personal consultation) do so while 36% of the pure robos cater for their clients’ financial literacy during the assessment process. It is likely that the availability of a certain level of human interaction in the case of hybrid platforms make the providers believe that the digital knowledge check is not necessary. However, we are convinced that each robo-advisor must be very clear about the suitability of their investment products and investors’ understanding is an integral part of this.

Most players check their prospects’ risk tolerance and the approaches differ substantially. Some questionnaires use a very scientific assessment of risk tolerance, including psychological and behavioral questions while others rely too much on prospects’ self-perception. One tool even compared the prospect’s self-evaluation of their risk type with the outcome of the risk assessment, which is a very interesting approach to show investors how their perception differs from their actual limits of tolerance. In any case, it is crucial to thoroughly explain the result of the evaluation and make sure that prospects understand the impact of their risk tolerance on their investments.

The third major element is the check for risk capacity. Displaying a highly risk-affine attitude has no meaning without the context of the capacity to take investment risk. It must be clarified whether the investor has any debts and sufficient investable assets before making suitable investment proposals. While this seems very straightforward, it is surprising to see that there are players who simply ignore that. Robo-advisors achieved the point for an adequate risk assessment only if both, the risk tolerance and the risk capacity are checked for. It is alarming that only 78% achieve that point and again, the pure robos give a substantially better performance. While only 65% of the hybrid platforms fulfill this requirement, 93% of the pure tools do so. This result supports the impression that hybrid robo platforms rely too much on the availability of a human advisor clients can turn to instead of implementing these things into their digital onboarding.

Therefore, before caring about financial planning features, portfolio analytics and reporting or the provision of valuable content, robo-advisors are strongly advised to accurately identify prospects’ risk profiles to ensure suitability. Only seven out of the 31 robo platforms included into our benchmarking fulfilled all three criteria, which is disturbing result.

Get more information about the world’s leading robo-advisors and their performances, strengths, weaknesses and best practices in our new Global Robo-Advisor Benchmarking Report 2017.

 

Why BigData projects often fail (and how to make sure yours isn’t one of them)

Thursday, June 1st, 2017

(By Onawa Lacewell, Analyst)

Big Data projects in the financial sector (and other sectors) often produce underwhelming results. It may be tempting to conclude that Big Data is just a passing fad and that wealth managers need not concern themselves with Big Data solutions. However, MyPrivateBanking’s new report on Big Data in Wealth Management thinks this is the wrong conclusion to draw from the current lackluster performance of Big Data projects. Instead, we argue that poor implementation planning that fails to take a needs-based approach is to blame for the inferior performance of Big Data projects to date. We suggest that by reversing the standard implementation process wealth managers can help ensure that Big Data projects succeed.

Why is Big Data more than just hype?

Big Data may seem like just a passing fad or a buzzword. Nevertheless, Big Data is here to stay and, from everything we’ve seen in the past five years, it seems highly likely that we are just now at the dawn of Big Data. “Big Data” simply refers to the increase volume, variety, velocity and veracity of data flooding into today’s businesses every day.

“It took from the dawn of civilization to the year 2003 for the world to generate 1.8 zettabytes (10 to the 12th gigabytes) of data. In 2011, it took two days on average to generate the same amount of data.”

(icrunchdata)

In today’s highly digital world, almost everything we do generates data: location data, social media activity, search engine metrics, and banking transactions. Additionally, as the Internet of Things brings more and more of our activity online this volume of data is only set to grow-from smart houses that provide insights into how you live to smart cars that, for example, allow insurance companies to closely monitor your driving habits. Businesses can now get nuanced, granular, and highly accurate data about their customers-both existing and potential. This is, of course, the lure of Big Data. For financial actors, the potential of Big Data is quite high. Banks can see when, where, and how their clients spend money. Wealth Managers can understand more about the behavioral profiles of their U/HNWIs. All financial actors can accurately and easily automate manual data entry processes both freeing up time for personnel to work on other tasks and reducing the possibility of data entry errors.

Why Big Data projects fail

The purpose of Big Data is clear: to help drive innovation and profits for businesses, to apply a fact-based business strategy, and to uncover insights to help increase organizational efficiency and improve client relations. Why, then, do Big Data projects have such a reputation for underperforming? MyPrivateBanking argues that much of the failure of Big Data is not because the data is somehow less useful than imagined but rather that the way many firms approach implementation sets these projects up for ultimate failure. The standard implementation process often starts with shopping around for a Big Data vendor-perhaps one that promises an all-encompassing Big Data solution that will use the firm’s internal and external data along with unstructured data (like social media commentary or search engine metrics) to modernize the entire digital ecosystem. Then, once the vendor’s solution is in place, the firm realizes that they actually don’t need all the data that they are collecting. Or, that there isn’t inhouse data science talent that can really get the most out of this new wealth of data analytically. Or, possibly, the firm realizes that the organizational siloing is standing in the way of using the new data.

A Needs-Based approach to Big Data

We argue that in order to get the most out of Big Data, and to ensure that Big Data projects are really successful, wealth managers and other financial providers should reverse the standard implementation process. Instead of focusing on the end solution, or trying to modernize the entire digital ecosystem with a general and comprehensive Big Data project, firms should instead take a needs-based approach to Big Data. The steps of this approach are rather simple, but this simple change in approach to the implementation process can make all the difference when it comes to whether your project will be successful or not.

1.     Identify the exact need (objective) of the project

This is a key step and should not be undertaken quickly-determine explicit needs, or objectives, where your firm needs a Big Data solution.

2.     Determine the type of data that best address this need

Do you need structured data? Unstructured data? A mix between the two? Determining which type of data addresses your need will help determine what type of data solution you require.

3.     Evaluate whether this data already exists within the organization

A lot of organizations think that Big Data means external data-using Facebook data, for example. However, Big Data can also mean internal data. Taking a deep look at the types of data your organization or firm already has may reveal that you don’t need external data at all-and this will determine what type of third party solution you need to shop for.

4.     Determine success metrics and expected ROI

Determining the success of a Big Data project can sometimes be difficult. Therefore, it is crucial that measures of profitability be part of the pre-planning discussion and strategy meetings.

5.     Shop around for a vendor who offers a solution that fits closely to the need

There are many different vendors offering everything from comprehensive Big Data solutions to narrowly targeted ones. Seeking a vendor that fits to your specific organizational need will help ensure that the resulting implementation plan will be a success.

6.     Determine the correct infrastructure and implementation plan to fulfill this need

Only after every other step in the needs-based chain is fulfilled should a firm or organization determine the type of infrastructure necessary for a Big Data project. The need should always drive the infrastructure-not the other way around.

By approaching Big Data from a needs-based plan, wealth managers and other financial providers stand a better chance that the resulting Big Data project will be successful. For practical information about how to take a needs-based approach to Big Data see our latest report. Here you will find practical tools that will help with determining success metrics, charting the implementation path, engaging in pre-planning and more.

 

Tech vendors’ surprising ideas about wealth management digitization

Thursday, May 4th, 2017

In My Private Banking’s latest report, on the digitalization of advisor functions, we interviewed just over 20 people from 13 different wealth management technology providers and various other industry experts.  Our interviewees gave us some fascinating insights into the direction and pace of innovation but had a number of surprises for the My Private Banking analysts.

Small surprises - but nevertheless significant findings - included a general air of confidence among technology providers about the value of their contribution and their assessment of the outlook for their wealth management clients. Initially, we thought that technology vendors might not be so sure that their digitalization message was finding willing listeners in the wealth management industry.

However there were bigger surprises in store for our researchers, firstly in our interviewees’ evaluation of the relative importance of regulatory compliance the remainder of the current decade and, secondly, perhaps most unexpectedly of all, in their estimation of the part that AI and machine learning will play in advisor digitalization in the near-term.  This provides a useful reality check to some of the technology hype that’s currently popular around the topic of AI.

Overall, our analysis of the future of advisor roles and the part likely to be played by robo advisory services was confirmed, giving us a clear picture of advisors and digital tools working in concert as a dominant model of service.

Last of all, we were a little surprised at the relatively minor attention given to the way in which advisors and relationship managers will experience the change to their roles and work styles through digital enabling.

Our report provides detailed coverage of the difference digitalization will make to compliance, advisor-client interactions (and hence client ratios and overall efficiency) and in which technologies and functions we can expect change soonest.  In addition, we have analyzed the impact of advisor digitilization on communication channels and client journeys.  Our researchers have endeavored to convey the feel of our conversations with technology providers through plentiful quotations and the report sums up My Private Banking’s findings with a number of clear recommendations for wealth management firms and private banks.

 

Robo Investing – Let’s Talk Digital

Thursday, February 2nd, 2017

(By Francis Groves, Senior Analyst)

There was a strong consensus at yesterday’s Robo Investing conference that the future of automated investing is NOT standalone D2C robo-advisors. Breaking even as a go-it-alone robo is simply too much of a challenge for many to succeed; to be successful the robo approach needs to build on the advantage of established brands, though these are by no means certain to be just existing financial brands. Andrew Power of Deloittes made the point that a robo with average portfolios of £35K and charging 75 basis points would need AuM of £3 billion to break even.

What came across most strongly was the wealth of insights into what is need for automated interfaces to play their part in engaging new clients. Speakers made the point that the public need more education about their own need to make financial provision for themselves and the importance of switching from saving to investing if they are going to make their money work for them. However, as Rob Hudson of Aberdeen Asset Management said, institutions shouldn’t make education a main focus but, instead, should use ‘the power of easy’ and concentrate on putting financial products in front of customers.

Richard Theo of Wealthify suggested that simplicity (of design) could move mountains and that design really needed to concentrate on mobile delivery, gamification and the use of ‘nudge’ techniques. Anna Lane of the Wisdom Council also voiced concerns about simplicity and strongly recommended institutions leave out jargon and give absolute costs as well as percentages and basis points. The key learning points are to recognize that financial service users prefer automation to human interaction where it delivers what’s needed and that advisors need automation to improve efficiency and raise client:advisor ratios. Engagement is more than a good user interface and requires the creation of trust by means of the kind of preference and behavior analysis and anticipation of client needs that AI/machine learning can provide.

 

Only robo-advisors constantly pushing ahead for superior client experience will survive

Thursday, November 17th, 2016

The pioneer years of robo-advisors have come to the end and the market will separate the wheat from the chaff. Too many automated investment services target the same, growing - but still not sufficient - client segment to nurture all or most of them. Too few of the automated investment services see their platform through the eyes of a first time user, while many are losing sight of the need for sustaining a customer experience that will - ideally - last for years.

In our new report on the leading robe-advisors worldwide, MyPrivateBanking makes a series of recommendations on the basis of our benchmarking evaluation, among them:

Aiming for transparency is the best policy, especially when presenting the robo-advisor’s pricing and product and process information.

Automated investment platforms need to be subjected to rigorous user experience testing. Looking good is not enough - equally, content must be in-depth.

Robo-advisors risk side-lining themselves if they don’t recognize that clients need financial plans as well as investment portfolios. At least a basic financial planning offer should be considered for inclusion as part of the robo value proposition.

We foresee the need for leading institutions to be more radical and wholehearted in their automated investment initiatives in the next few years, even if this means starting over again with a second robo-advisor to replace their first.

 

New survey: Investors surprisingly open when it comes to robo-advisors

Tuesday, May 10th, 2016

Our new report on investors’ attitudes towards robo-advisors is based on a survey covering (mass-)affluent and HNWI in the UK and the US. The 600 respondents answered questions with regard to their awareness of robo advice, benefits and risks of automated investing, awareness of existing players and many others.

One of the main findings is that investors are generally very open to the new technology as more than 70% believe that automated advisory tools can positively influence their wealth manager’s advice and decision-making process. Particularly when it comes to onboarding processes, investors see huge benefits in automated online tools – 74% think that the technology is likely to speed up registration and, hence, lead to an increased efficiency and convenience.

Similarly encouraging, investors’ awareness of the robo technology is surprisingly high: 45% of the entire sample already heard or read about the concept of robo-advisors and 20% state to know quite a lot about it or even know it in detail. At the same time, the share of people saying that they don’t think to be using robo advice in the future is 20%, which is mainly driven by the older age segment with 55 years and above. Interestingly, the share of the hesitant appears to be more than twice as high in the US (28%) than in the UK (12%).

In the US, the largest share of respondents selected Charles Schwab Intelligent Portfolios as the brand they associate most with robo advice (43%) while in the UK, it is Nutmeg that leads the field, with the same share. This is quite interesting since original robo providers such as Betterment (18%) or Wealthfront (13%) seem to be far less known – this is a strong sign that it is a lot easier for established wealth management brands to promote their automated services.

Our new survey report elaborates on these and a lot more findings that draw a very clear picture of wealthy investors’ adoption of the robo advice technology. In addition to the general results, the report describes the main differences among the two focus countries, UK and USA, different wealth segments (mass affluent, affluent, and high-net-worth) as well as different age segments in order to derive valuable recommendations and learning points dedicated to wealth managers’ target client groups.

 

Robo-Advisors and Market Falls: Leaders Like Wealthfront and Betterment Are Surprisingly Resilient

Tuesday, April 26th, 2016

Last Wednesday Bloomberg carried an interesting article: ‘For Robo-Advisors the Next Bear Market is Make or Break.‘ It left the question of make or break unresolved but it did have some interesting details about how Wealthfront and Betterment had fared in the first few months of this year, with Wealthfront reporting that it grew its client-base three times faster this January than 12 months previously. Meanwhile Betterment reported that client growth and net deposits had seen peaks in January and March.

The ability of robo-advisors to survive bear markets is still seen as their Achilles heel by some but the counter argument, that the application of behavioral finance insights and in robos’ outbound communication messages about consistent long-term diversified investing helps investors stay on course, seems to be gaining ground.
But isn’t there a more fundamental reason for the resilience of robo-advisors? Investors who start from scratch and grow their investments over a long period are likely to be less affected by aversion to loss(es) than a someone who gives a wealth manager a discretionary mandate for an already sizable fortune. The corollary for this though is that robo-advisors that require a minimum investment (e.g. Personal Capital - $25K, MedioBanca’s Yellow Advice - €20K) or ones with a charging structure that offers much better value for money for larger portfolios, may not have such an easy ride when the markets turn down. Moreover, perhaps there’s a penalty of success for very low cost robo-advisors and younger clients; growing their portfolios successfully and in 10 or 15 years’ time these clients will be a lot more worried about market downturns than they are at present.

 

New FinTech deal: Goldman Sachs buys Honest Dollar

Tuesday, March 15th, 2016

The FinTech market is becoming increasingly complex as new startups emerge and financial institutions come up with own innovations or collaborations with FinTech companies. Yesterday, I came across the latest deal – Goldman Sachs just bought retirement-saving company Honest Dollar that claims to enable employers to sign up in just 90 seconds while offering retirement benefit plans for competitive prices. The process is quick and easy - after having responded to some questions, employees are recommended one of six different portfolios consisting of Vanguard ETFs. Goldman Sachs aim to serve more people with more efficient retirement planning services by partnering with Honest Dollar and, hence, adding to their growing digital offerings. Currently, the finance giant claims, 45 million Americans are not offered retirement plans by their employers, which is mainly due to the high costs.

How important is it for people that they can sign in to such tools quickly and easily? Do they think their financial advisor can make faster and better decisions when using such tools? This and a lot more questions covering affluent people’s attitudes towards robo-advisory services are being answered by our upcoming panel study. Stay tuned! http://bit.ly/1pGGYQt

 

Will Artificial Intelligence be the key for wealth managers to stay competitive?

Thursday, February 19th, 2015

Exploring the digital world of financial advice for our upcoming report, we had to take a deeper look at cognitive computing, analysing the role of tools like IBM Watson and others. The technology certainly sounds promising: enabling advisors to make better and faster recommendations to serve a higher number of wealthy clients with higher quality recommendations through cognitive computing. This way, IBM Watson is likely to play a substantial role in easing the pressure for wealth managers, which results from shrinking margins as well as from rapid digitization in private banking.

While the use of Watson in financial advice is still in an early phase, the technology shows already promising results in healthcare. Moreover, IBM Watson plans to launch their first intelligent toy for children this year – CogniToys are supposed to develop with the children side by side, learning together with them through continuous interaction.

The need to educate Watson prior to using it effectively is the critical step for wealth managers who consider implementing the technology. Although there are some use cases already, for example at Singapore-based DBS, most banks are not yet rolling out AI platforms – such as Watson – as they are waiting for a clear proof their economic viability and ability to deliver RoI.

It is out of question that intelligent, learning software platforms with cognitive abilities will find their way into the wealth management industry. Early adopters may face some uncertainties but will certainly gain a competitive advantage being a step ahead of the pack.

 

The changing role of wealth advisors

Friday, January 16th, 2015

Remember Inspector Gadget, the detective who solves cases with the help of high-tech devices? Totally futuristic in early 80s, the concept is easily applicable to today’s financial advisors. While particularly the advisory services for the mass-affluent experience a severe threat from the booming robo-advisor industry, we forecast a pressing need for HNW clients’ advisors to redefine their role, too.

In Private Banking, advisors or relationship managers as they are called there, will not disappear as the main interface to the high-net-worth client. Digital innovations like robo-advisory tools, mobile apps, video conferencing, or social media dashboards will support and improve the advisor’s work. Just like her cartoon sibling, the new advisor is combining her human benefits (the emotional intelligence) with the technological benefits (the data, analytical and visualizing part). The new advisor will be – like Inspector Gadget - inspired by Cyborgs, bringing together the best of human capabilities and machine intelligence.

The new advisor offers to her clients support and coaching across all channels in real time in an increasing personalized way. Big data tools help her to identify relevant information to recommend the right products at the right time. Social media compliance tools deliver the compliant framework for advanced client communication, and sophisticated video conferencing tools allow for flexible advice anywhere and anytime. Thanks to dedicated mobile apps, client meetings improve in terms of quality, engagement and efficiency.

Watch out for our upcoming report on digital interfaces for financial advisors this spring!

 
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