Robo-Advisors: Mapping the Competitive Landscape


“The Report Robo-Advisors: Mapping the Competitive Landscape provides information on pricing, market analysis, shares, forecast, and company profiles for key industry participants. – MarketResearchReports.biz”

The wealth management industry has long been resilient to the digitization process observed in the wider financial services space. This has started to change, however, with interest in robo-advice platforms increasing in 2015. The automated investment management space is hence becoming ever-more competitive as new entrants launch propositions. Supported by software developers, traditional wealth managers have also started exploring the digital advice market. Competition will thus increase further, although robo-advisors are still looking for business models that will appeal to HNW individuals.

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Key Findings

  • Regulators have not been able to keep up with the growing popularity of automated advice solutions. This creates an opportunity for industry leaders to have a direct impact on how the regulatory environment around robo-advice is designed.

  • The US is the home market of low-cost robo-advice, as self-directed investors are driven by price sensitivity above all else.

  • New entrants across the globe are introducing innovations to the automated advice space.

  • Most wealth managers focused on the HNW segment do not consider robo-advisors a threat to their business, as the wealthiest individuals show limited interest in robo-advice.

  • Robo-advisors that emerged as startup companies will partner with incumbents to attract more assets to their platforms.

Synopsis

Verdict Financials Robo-Advice: Mapping the Competitive Landscape looks at the growing robo-advice market. As the industry lacks clear definition of what robo-advice is, we clarify the differences between various automated investment platforms. Case studies of the most successful and established robo-advisors provide insight on what features appeal to clients and why. Drawing on our 2015 Global Wealth Managers Survey the report specifically analyzes HNW investors attitudes toward automated advice. Established software vendors activity in the robo-advice space is also covered.

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Specifically the report:

  • Defines robo-advice, providing an overview of its history so far and regulators approach to services provided by automated advice platforms.

  • Provides case studies of successful robo-advisors operating in a number of markets, including the US, the UK, Switzerland, and Australia.

  • Identifies the digital platform features that appeal to HNW investors.

  • Analyzes wealth managers attitudes towards robo-advice.

  • Looks at the benefits of partnerships between robo-advisors and competitors operating traditional business models.

ReasonsToBuy

  • Understand what robo-advice is and how it affects traditional wealth managers.

  • Recognize regulators approach to robo-advice and the guidelines to consider when launching digital investment platforms.

  • Learn best practice from established automated advice providers case studies.

  • Discover how automation can be beneficial to your companys operations, particularly in relation to advisor efficiency.

  • Identify the markets with the highest growth potential in the automated investment space.

Table of Contents

EXECUTIVE SUMMARY

The robo-advice market will grow as traditional players launch digital platforms

Key findings

Critical success factors

DEFINING ROBO-ADVICE

Robo-advisors are digital investment management platforms

The first client-facing robo-advisors were founded in 2008

Robo-advisors were preceded by PFM tools

Automated advice platforms are often fintech startup companies

B2C robo-advisors have been evolving, with traditional players building their platforms

Robo-advice has also entered the B2B space

Financial advisors can use existing solutions initially designed for the B2C market

Robo-advice platforms are now offered by IT software developers

Regulators are increasing the scrutiny on automated advice

Regulators’ attitudes towards robo-advice differ between markets

The industry should use the robo-advice label carefully

CLIENT-FACING ROBO-ADVISORS COMPETITIVE LANDSCAPE

The US is the home of robo-advice

US robo-advisors operate three typical business models

The vanilla US robo-advisors appeal to cost-sensitive investors looking for simplicity

Hybrid business models leverage the digital channel to offer advice provided by humans

Robo-advisors have to introduce unique features to succeed in the long term

UK robo-advisors have been lacking recognition

The uptake of digital platforms in the UK differs from the US

Few digital players have obtained advisory permissions

Major UK competitors offer non-advised digital discretionary asset management

Business models in other markets mostly follow US and UK competitors

Theme-based investing can engage clients more

More advanced portfolio management strategies could attract sophisticated investors

Few robo-advisors operate in more than one market

Universal financial services providers have the resources to launch innovative platforms

Wealth managers are not concerned by robo-advisor competition

HNW individuals in developed markets show little interest in robo-advice

Robo-advisors are not sophisticated enough to compete with traditional wealth managers

Human relationships remain vital for HNW investors

AUTOMATED INVESTMENT TOOLS FOR HUMAN ADVISORS

Relationship managers can be more efficient when backed by technology

Retail robo-advisors should be interested in partnerships with human advisors

Betterment Institutional partnered with Fidelity

Hedgeable and Scalable Capital offer investment management for institutions

RBC’s advisors will be using FutureAdvisor’s capabilities

Jemstep started as a client-facing platform but shifted to work with advisors

Automated advice platforms are becoming a standard product offered by banking software developers

Envestnet built the Advisor Now portal

Additiv offers software incorporating automation at four different levels

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