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How to survive the findustrial revolution

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REUTERS/Tyrone Siu

The use of big data is poised to revolutionize the wealth and asset management industry, but how close are we to allowing machines to make the important decisions in our lives?

The scale of big data and the opportunities it presents is shown at Thomson Reuters, where we distribute 10 billion bytes of real-time pricing data every single day. At its peak, this can run to eight million bytes per second.

We now process and collect more data in a day than we did in a month five years ago, with tweets, emails, PDF documents and many other formats converted into digital structures, databases and knowledge graphs. This has led customers to ask how we can link their data so they can better understand:

  1. Where and how to invest
  2. Where to discover hidden valuations
  3. How to measure risk

Under pressure

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Photographer: Nacho Doce

With firms under pressure from tougher regulation, cost pressures and newer technologies, the use of big data has never been more relevant.

David Craig, President of Finance & Risk at Thomson Reuters, said “The Findustrial Revolution is real. It’s not a question of if but when and how.”

Peer-to-peer lending and crowd funding is already a $50 billion industry, where consumers and small lenders can lend and invest in small companies. Digital identities are also being used to unlock the problem of trust and identification.

Robo advisors?

But the biggest development involves machine learning and big data, where machines can automate repeatable tasks, gain insights and ideas from data and make decisions on our behalf.

In a recent survey, 50% of executives reported that machine learning was already being used to help decision-making and forecasting, although far fewer were using it to advise or help customers.

This raises some crucial questions going forward. How quickly will technology move? How can the truth be identified within this tsunami of data? And will we trust machines with the decisions we would normally make ourselves or ask our advisors to make on our behalf?

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Trusted information and the impact on markets

James Land, Market Development Manager, Pacific, Thomson Reuters, chaired a discussion between a panel of experts seeking to find answers to the above questions. Find out what they had to say below. The panel for ‘Intelligence Tools and Innovation for Growth’ consisted of:

  • James Land – Market Development Manager, Pacific, Thomson Reuters (Moderator)
  • Simon David Allen – CEO, APT Capital Management
  • Jeroen Bulwalda – Partner, Wealth and Asset Management Advisory, E&Y
  • Jame DiBiasio – Editorial Director, Haymarket Financial Media – publisher of AsianInvestor
  • Alex Johnston – Director Client Technology, TAM Asia, Thomson Reuters

Jame: “Data is being likened to land, labor and capital: it’s a factor of productivity. And the volume of data points available is mind-bogglingly vast. Many institutional investors — asset owners and professional money managers — are struggling to adapt big data capabilities to legacy systems. Uncertain regulation (especially around customer privacy) compounds the challenge of integrating data from multiple sources.”

Jeroen: “We have seen many firms face increasing challenges with regards to their data management.  Firms need their data to comply with global and local regulatory and compliance requirements — a huge challenge. Furthermore, clients are demanding greater transparency regarding portfolio information and fees.”

“Lastly, firms want to know what products investors are buying so they can manufacture products that best meet their needs. Those firms that rationalize their data requirements and implement strong and effective governance create a stronger competitive advantage in the current climate.”

Finding ways to acquire insight and knowledge

People stream into the Apple store on 5th Avenue on Black Friday in New York November 28, 2014. REUTERS/Carlo Allegri (UNITED STATES - Tags: BUSINESS) - RTR4FZLC
Photographer: Carlo Allegri

James: “Do you think the market is currently able to extract meaning from all this information?”

Simon: “There are tools available, but at this stage they are still quite crude. Whilst real-time actionable analysis is available the tools currently in use lack the ability to take account of context in a sophisticated way and are prone to over-reaction and error and are vulnerable to being gamed.”

Jame: “Unless companies are already quite sophisticated with regard to data analytics, they are going to struggle if they ignore the basics of governance, security and privacy. Issues around compliance and treatment of data must be addressed first.”

Alex: “The digitization story certainly needs to play out before widespread tool adoption. Fintech has a head start, they are digital from the beginning. The hard part still remains asking the right questions to gain real, actionable insight and identifying the correct set of data to interweave.”

James: “What is cognitive computing and how does it work?  How can it help revolutionize decision making, create efficiencies and deliver better services to customers?”

Alex: “Cognitive computing is essentially automation of knowledge work. Pattern recognition and analytical tasks once seen as solely human capabilities are now being codified in applications like Watson. This cognition is characterized by conversational interaction with a machine — often in the form of questions — from which answers are presented with differing levels of confidence.

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James: “How can it help revolutionize decision making, create efficiencies and deliver better services to customers?”

Alex: “Cognitive computing differs to Google search through the ability to infer new knowledge, seek out facts to support its supposition and learn over time which answers are the most correct. Confidence is the new measure. Knowledge is extracted from analyst reports, news content, fundamental data, social media, public records or even email.”

James:  “Simon, do you think the industry is ready for cognitive computing?  Where do you think it could add value?”

Simon: “I think that we are going to see a stratified market. For the emerging middle class, who previously wouldn’t be able to afford financial advice, machine intelligence will provide ‘mass customization’: mass produced advice customized to each client. High net worth individuals will still pay for a bespoke service from a human being.”

Alex: “There is at least one large bank in Australia developing a cognitive capability to deliver tailored ‘Statement of Financial Advice’ to customers.”

James:  “How can it add value to the asset management and wealth industry, and what is the feedback you are hearing?  Is there a first mover advantage?”

Jame: “AsianInvestor recently polled our readership and found most buy sides and brokers in Asia Pacific see their biggest challenges as gleaning insights from data in order to make better investment decisions or to reduce risk.”

Jeroen: “Consistent and appropriate advice is where ‘digital advice’ can play a big role. To make sure the boundaries are defined, the advisor still has discretion with the client but  there will be a compliant and suitable outcome for the client.”

Alex: “Cognition certainly augments knowledge work. I might look at a new investment opportunity, a company and the data gathered on it, and ask “which of my customers or funds is this most appropriate for?” It’s for me to make the ultimate decision but a broad fact base can been considered quickly and through a standardized, evidence-based process.”

The Future for wealth and asset management

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Photographer: David Moir

James: “With such rapid development of new technology, I’d be interested to hear your prediction of what the future of the financial services industry will look like.  Will it be materially different to what it is today and why?”

Jeroen: “Fitbit is a good example of a tool widely used today to simplify the life of consumers. Algorithms will be commonly used to solve financial planning and investment scenarios for many, particularly the 80% of unadvised. With a heavy slant on passive indexing, robo-advisors could become a new benchmark.”

Jame: “There is no end-game to big-data analytics. It solves problems that investors could not address before, but there’s no longer a point at which an organization can say, “Okay, we’re done.” The only way to master this constant change is for technology decisions to be integrated into top-down management. Gone are the days that a CEO or even a COO could hand these things off to the tech team.”

Alex: “Looking forward five years I see multiple deployments of cognitive computers within a large organization, each asking a specific question. Much transactional and knowledge work will be automated, leaving relationship management as a key differentiator. Humans will have more room to express creatively: innovative financial products & services, smart contracts created to suit new market opportunities, or mass customized portfolios suiting specific customer needs and outlooks.”

Simon: “The future direction will depend upon regulation, not just technology. On the one hand, a machine adviser will not deliberately break the rules. On the other hand, when the software goes wrong who is liable? The software makers or the bank?”

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