FinTech Innovation: more TECH than FIN

FinTech Innovation: more TECH than FIN

Paolo Sironi

FinTech Thought Leader at IBM Watson Financial Services and author of bestseller "FinTech Innovation" (Wiley 2016)

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FinTech Innovation: more TECH than FIN

29.12.2016 09:00 am

J. M. Keynes had already imagined central bankers as orthodontists, intervening with humble fiscal and monetary policy to optimise the dynamics of the economy at large:

“If economists could manage to get themselves thought of as a humble, competent people, on a level with dentists, that would be splendid.”

It is now common wisdom that dentists shall also pursue the goal of advising on oral hygiene, rather than simply intervening once the pain becomes unbearable. Similarly, investors shall be given the tools and the means to re-balance investments with an ex-ante view of the potential drawbacks and opportunities, which is the essence of proactive wealth management.

Empowering taxable investors to take transparent care of their own investments responds to post-GFC industry’s imperatives (eg, transparency, suitability and adequacy principles) and should be the key driver to evaluate any project of banking digitalisation.

Understanding how investment risks and returns can enfold becomes a social imperative, not just in the short (myopic trading) but also in the long term (capital protection).

 

Are the methods used to describe risk and return sufficiently transparent, intuitive and robust?

FinTechs have poured much resources into the TECH innovation, but seem to be fairly undifferentiated with respect to the FIN methods of portfolio construction. Robo-Advisors have taken the lead of the innovation race, yet they also rely upon traditional Modern Portfolio Theory which might be too restrictive to build and explain long-term model portfolios, particularly if multiple investment horizons and goals have to be accounted for. The provision of more intuitive and consistent information about potential future states of the world and the simulation of actual investment returns can contribute to improve market efficiency and reconcile tactical and strategic portfolio allocations.

Robo-Advisors might not feel compelled to discuss the gap between strategic and operational asset allocations, since they typically (so far) on-board new money directly to model portfolios, but the wealth managers' need to differentiate, and solve complex and more personalised investment decisions graphically (eg, retirement planning), will require all market participants to discuss the implications of portfolio management choices in terms of their competitive advantage.

A new interpretation of portfolio theory is therefore needed to realign more consistently investments and goals, which is the essence of Probabilistic Scenario Optimisation (see book on Amazon).

Financial advisors can verify whether a given set of an individual’s constraints complies with the simulated total return space of portfolios, by measuring on demand the probability of achieving or under-performing a defined investment goal. In essence, the probability measure becomes the key variable of the min/max objective function used in portfolio modelling, being the key information to discuss where portfolio performance lies against a stated goal, at any point in time of the investment journey.

Please check my recent interview on Risk.Net, which provides insights into Probabilistic Scenario Optimisation (12 min.) and book summary .

 

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