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FinTech

Welcome to the Findustrial Revolution

This is the way we will all bank in the future. The Findustrial Revolution represents an extraordinary developmental leap in the way we consider the business of finance.

This revolution is a confluence of events, driving the emergence of the new generation of FinTech companies across the world. Its influences include tougher regulation, massive pressure on costs and the adoption of new technologies. The principal driver however is talent – the thousands of technologists who are leaving the incumbent banks full of ideas, money and time to disrupt the old models. According to the consultancy McKinsey, there are now 20,000 FinTech companies in New York and London alone.

The “Findustrial Revolution” is no longer a question of if but a question of when and how.

You only have to look at how many financial institutions have opened accelerator and innovation labs, and how many banks have created ‘digital business units’, to see the traditional incumbents are taking this seriously.

Thomson Reuters is present in more than half a dozen accelerator labs, from Waterloo in Canada, to Manhattan, London, Tel Aviv, Sydney and Hong Kong.

As an open platform business, we provide a catalyst. Companies can use our open APIs, our rich content and our Eikon App Studio to experiment with our financial data and to build. Thomson Reuters is the oldest FinTech company, with 150 years of financial innovation engrained in our heritage.

So what’s happening? What are the areas of interest? Here are just a few:

Digital identities

Can we unlock the problem of trust and identification, and remove the inefficiencies of Know Your Customer (KYC) checking, client on-boarding and chasing down beneficial ownership – while meeting obligations for rules such as FATCA?

Standard symbologies and data

Can we help remove the risk and costs of non-standard data, legal entities and data reconciliation? We have now generated over 200 million permanent static data identifiers of places, people, legal entities and events, and are publishing these around the world.

Peer-to-peer lending and crowdfunding

This is now a $50 billion lending industry, where consumers and small lenders can lend and invest in small companies – disintermediating banks and traditional institutions in the process.

Federated systems and digital identities to transfer instruments, currencies, loans and payments

The block chain technologies could effectively remove 50 percent of middle and back office costs associated with debt, equities and other securities.

Machine learning and big data

How do we tune and power learning machines to automate repeatable tasks, gain insights and ideas from the exabytes of data swimming around the world, and to trust them with the decisions we would normally make for ourselves, or ask our trusted advisors to make?

Big data is not new to Thomson Reuters. On an average day we distribute 18 billion market messages.

To give you an idea of the scale and pace of innovation across the business, we now:

  • Process and collect more data in a day than we did in a month just five years ago.
  • Use our own predictive analytics to improve how we find, extract and tag data – enabling customers to use data in ways not possible before.
  • Use semantic analysis and learning machines to generate sentiment on news and social media.
  • Screen 100 million websites a day to help our customers identify hidden risks to help them protect their business.
  • Have machine learning algorithms which spot suspicious trading patterns and potential fraud, and detect problems.

And this list is, of course, by no means exhaustive. We have customers asking us to now go much further – to link our data with their data, so they can better understand where and how to invest, where to discover hidden valuation and risk information, and how to find the risks concealed in their supply chains which can impact future performance and reputation.

For instance, when news of the Volkswagen emissions scandal broke, the investment community wanted to know not just the news on Volkswagen but who the 1,000 suppliers of Volkswagen are – insights which are not easy to find without machines capable of mining huge amounts of data.

These are fairly basic, very practical uses but where could this go?

In Narrative Science’s recent survey, 50 percent of executives said their primary use of machine learning was to help decision making. Far fewer (10 percent) were using machine learning for advising or helping customers. This is clearly the next step for the industry.

Do we see robo-advisors taking over from our human relationships? Do we see asset managers turning to machines to make better investment decisions for them, or simply helping with existing decisions? In short, are we going to remove many humans and replace them with machines, or will the machines just help the humans?

The “Findustrial Revolution” is no longer a question of if but a question of when and how.

I don’t think this is a technology question; it’s a question of trust.

Will we ever get to a place where managers and customers can really trust machines, not just to eliminate the manual things but to trust them to make the important decisions in our life?

If we trust a machine to fly a plane (albeit with the pilots still in control), surely we could trust a machine to plan our pension?

From my perspective, the momentum is undisputed and the potential for growth is huge.
There are boundless opportunities, but also significant challenges. History is at a turning point. At Thomson Reuters, we are perfectly positioned to meet those challenges.


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