5 minute read 1 Oct 2018
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How IoT is driving new financial decision mechanisms and business models

By

Michal Krzysztof Rutkowski

EY EMEIA Advisory IoT Strategy Senior

Internet of things systems thinker. Economics adorer. Music lover. Tech geek. TV art admirer. Engineer and strategist.

5 minute read 1 Oct 2018

The availability of real-time data from IoT will completely destabilize traditional calculations.

IoT could become a destabilizing factor for some of the more traditional elements of the entire financial sector by increasing the availability of reliable and real-time data. This will disrupt the balance between information sharing and information asymmetry between market players.

To explain, we must first look at how companies in the financial sector have, up till now, made decisions.

The decision-making framework

In 2003, C.F. Kurtz and D.J. Snowden published a paper The new dynamics of strategy: Sense-making in a complex and complicated world. In it they presented a new framework, named Cynefin, which orders the rules for making decisions in various conditions of uncertainty.

The Cynefin framework can help visualize changes in decision-making mechanisms resulting from the migration in what is termed the “Chaos” domain – where most of the financial institutions operate right now – into the domain of “Complex” systems – to which they will need to migrate in order to stay competitive.

  • Chaos domain: no visible cause and effect relationships. Interventions are focused on stability. IoT applications in this area can be used to examine how actions taken affect the functioning of the system. The Chaos domain is the basic area of application of statistical tools. Information asymmetry results from the difference in the quantity and quality of available data for statistical analysis.
  • Complex domain: cause and effect are consistent only in retrospect and are not repeated. Decisions are based on observed patterns. This domain can be called “The domain of the IoT:” pattern detection algorithms (e.g., artificial neural networks) can introduce significant changes in the asymmetry of information.
  • Complicated or Knowable domain: cause and effect are separated in time and space. The right analytical and reductionist approach. Information asymmetry is potentially due to differences in professional knowledge. IoT applications in this area are in particular a better understanding of the process and, to a lesser extent, a (nondisruptive) reduction of the asymmetry of information between the seller and the buyer.

Although IoT applications are possible in all of the domains, the most economically significant effect will be when their implementation moves the part of the system to a different domain because it will result in changes to decision mechanisms, especially when used by interrelated market players.

Different decision rules will result not only in modeling accuracy. Models based on current sensor data will also result in near real-time adaptations to the changes in the system conditions, while the statistical model will have significant delays before being accurate, because of the necessity to collect a representative amount of historical data.

When looking for a new equilibrium, financial companies should particularly analyze the impact of the IoT from the following perspectives:

  • Changing the asymmetry of information, both between competitors on the market as well as between suppliers and service buyers
  • Significantly faster adaptation of risk models based on IoT data processing (the Complex domain) in relation to statistical models (the Chaos domain), especially in the conditions of progressing market segmentation
  • The impact of the data processing algorithms used to build understanding of the causes of changes
  • The possibility of influencing the behavior of market participants using IoT solutions (transition from the Complex to the Complicated or Knowable domain)

Wider implications

A possible effect is the emergence of new types of financial entities. Entire business models could be structured around the opportunities created by the asymmetry of information (i.e., where some parts of the market have more information than other parts). These could be in the context either of the relationship between competing financial institutions offering services, or between suppliers and buyers.

It is likely that the final stage of this disruption will be the consolidation of data circulation within several entities and a significant unification of data correlation algorithms. This may lead to a new equilibrium in the economic market, taking into account the increased data availability and a new model for processing this data into knowledge and ultimately wisdom.

Entire business models could be structured around the opportunities created by the asymmetry of information (i.e., where some parts of the market have more information than other parts).

However, the process of reaching a new level of equilibrium will have a fundamental impact on the economic effectiveness of individual entities in the financial sector during the transition period. This may well be accompanied by the potential collapse of some laggards too slow to adapt their business models to the new digital asset-rich environment.

Conclusion

It is likely there will also be disruption in markets where uncertainty about the future plays a significant role. Financial sector enterprises will have to revise their operational models by adapting them to market requirements, as well as their business models.

As organizations collect more objective, passively acquired data, it will need to consider a bigger question – do they want to use it in order to build information asymmetry over customers and competitors, or do they take a strategic decision to share it?

Organizations will need to think over the role of fundamental aspects of information in their business and decide in which areas they should cooperate, and which represent their unique competitive advantage.

They will need to think over the role of fundamental aspects of information in their business and decide in which areas they should cooperate, and thus extend the extent of information sharing with other stakeholders or alliances, including other companies and clients, for the common goal of broader market stabilization, and which fields of their operations (and digital assets) will represent their unique competitive advantage.

Summary

IoT will drive different decision-making rules due to near real-time adaptations to system changes. It will also impact entire business models, which could become structured around the opportunities created by the asymmetry of information (i.e., where some parts of the market have more information than other parts).

About this article

By

Michal Krzysztof Rutkowski

EY EMEIA Advisory IoT Strategy Senior

Internet of things systems thinker. Economics adorer. Music lover. Tech geek. TV art admirer. Engineer and strategist.