Complexity and Resilience in the Risk Markets

In this mini-series of four segments we will begin by looking at the current state of the Risk Markets and the Insurance Industry, then…

2. The emerging Peer-to-Peer (P2P) segment of the risk markets

3. How Blockchain technology is enabling a new taxonomy in the risk markets

4. What changes may occur as a result of these new technologies and methods

While the subject at hand is the Risk Markets, the purpose of this mini-series hails from the Open Source movement in the software industry. Key to the open source philosophy is the transparent and voluntary collaboration of all interested parties. While this work has been kept fairly close to the chest for the past few years, I have taken meetings with two Fortune 500 insurance company’s strategy team and venture teams, both of which asked for a proof of concept, as well as a hand full of other large international insurance companies, and one of the big four accounting firms.

At the other end of the spectrum I have also spoken with other founders of P2P insurance startups around the world, and participated in the communities surrounding Blockchain technology. I feel that these hand full of folks have already enjoyed “early access” to these concepts and my motivation with this mini-series is to achieve a more level playing field for all parties interested in the future of the Risk Markets.

There is a LinkedIn group if you would like to join the conversation. https://www.linkedin.com/groups/8478617

To begin lets take a look at the current state of the risk markets. It is important to distinguish between drivers of economic systems and the impact they have on which business models create success in the “industrial age” vs in the “information age”.

Hardware & Technology was a key driver throughout the industrial age, which saw a growing bench of new technologies throughout it’s time, from cars & planes, to computers, smart phones, & industrial robots, etc.

Industrial age business models were almost always “extractionary” in their nature. The business model engages with some market, the model profits by keeping some portion of the market’s value.

Extracting value from the Market.

The strategies of the industrial age were

· Standardization — interchangeable parts

· Centralization — big factories, vertical integration, economies of scale

· Consolidation — this is an indication that an industry, like insurance, is about to experience a phase change, or as the media says, “get disrupted”

In the information age the nature of business models almost always embodies the creation of “network effect”. When the business model engages with a market, the individual actors in that market benefit at an increasing rate, as more actors engage with the business model. The value creation is usually tied to a network’s graph, all those who engage with the network benefit, and the value creation can grow exponentially as the network’s density grows.

Creating value for the market, not extracting value from the market.

The strategies and efficiency drivers in the information age are

· Cheap connections — enabling multi paths through the network’s graph

· Low transaction cost — in terms of time, effort, & money

· Lateral scaling — not vertical… vertical structures will be flattened out. “top down” does not work well as it increases network fragility.

· Increase in network diversity — and increase in the ways in which each node can connect.

All of these drivers lead to an increasing of network density & flow. Moving away from large brittle centralized organizational structures, and moving towards “Distributed”, or “P2P”, or “Crowd”, or “Sharing economy” type of organizational structures and business models.

Moving away from Centralized command and control organizational structures is almost impossible for those organizations that make profit from efficiency gains derived from a centralized effort. It is this attribute of those business models which necessitates new startups to test new business models, to bring improvements to the market and challenge incumbent economic & business models at the edge of the market.

The Information age is all about Networks, (not technology) and building graphs that create positive network effect.

The conceptual framework best suited to understanding networks, and the networked world we now live in is, Complexity Science. The study of Complex Adaptive Systems, and Complex Flow Networks, has grown out of it’s roots in the 1940s, and proliferated since the 1990s and the explosion of computer networks, like the internet, and now social networks. We find bodies of knowledge which have existed in the world for some time, but are not well known, if known at all, by the insurance industry and in the risk markets.

Here is a 10 minute video on the evolution of Complexity Theory… https://www.youtube.com/watch?v=7art8jsjlOI

When looking at Complex Systems we start by looking at the system’s graph. To get an idea of what a graph is, let’s look at a few examples of “graph companies”.

· Facebook built the “Social Graph” of acquaintances… It did not create acquaintances.

· Linkedin built the “Professional Graph”… It did not create coworkers & colleagues

· Google built the “Link graph”… It did not create back links for the topics searched.

Notice that in each of these cases the company built and documented the connections between the things or nodes in the network, and did not create the things or nodes themselves, or their connections, which always preexisted.

To start looking at the Risk Markets we must first understand what is being connected or transferred between the nodes aka users. It should be of little surprise that in the Risk Markets, it is Risk which is being transferred between nodes, for example a insurance user pays to transfer their risk to an insurance company.

Risk Graph wise there are currently 2 dominant graphs, and an emerging 3rd graph in the risk markets… Let’s take a look at the graphs which make up the risk markets and the insurance industry.

1. Insurance — is the Centralized “Hub & Spoke” graph

2. Reinsurance — is the Decentralized graph connecting risk Hubs

3. P2P Coverage — will be formalized into a Distributed graph. — This is the one that obviously does not exist formally, but informally you see people calling parents, friends, and using GoFundMe, or their church/office and other community organizations in their personal network to spread risk out laterally.

In today’s Risk Markets, Insurance companies act as centralized hubs where risk is transferred to and carried through time.

The Reinsurance industry graph is enabling 2nd degree connections between individual insurance companies, creating a Decentralized graph. In the current industry’s combined graph structure or stack, only these two graphs formally exist.

While the Insurance company’s ledgers remain a hub where risk is transferred to, and carried through time, Reinsurance enables those risk hubs to network together achieving a higher degree of overall system Resilience.

The P2P distributed graph in today’s market only exists via unformalized social methods.

When you stack all three graphs on top of each other, you can observe how Total Risk is addressed across all three graph types. Total Risk being defined as 100% of a Loss Event, starting at the bottom with the first dollar of loss. Each graph has its strength and weakness which lead to each graph existing in its proper place within the risk markets.

The fact that insurance as a financial service gets more expensive per $1000 of coverage, as coverage approaches the first dollar of loss, means that as a financial service, when approaching the first dollar of loss, there is a boundary where its weaknesses will outweigh its strengths.

My expectation is that much of the risk currently being carried on the Hub & Spoke Insurance graph will accrue to the P2P Distributed graph, due to improved capital efficiency of distributed methods to cover small losses. This may become a driver of a trend of increasing deductibles. This may lead to some of the risk currently carried on the Reinsurance decentralized graph being challenged by centralized insurance. We will look at this shifting balance in more detail in the forth segment of this series.

It is the proportion of Total Risk or “market share” which each graph carries that will shift in this phase change which the industry has begun.

When people say, “insurance is dropping the ball”, what they are expressing is that there is a misunderstanding or poor expectation setting, about how much of Total Risk, the first 2 graphs “should be” absorbing… and consequently, users are unhappy when they end up resulting to informal P2P methods to achieve full coverage Total Risk.

To increase the Resilience of society’s risk management systems, and fill the gaps left by the Insurance & Reinsurance graphs, we need the 3rd risk graph, a Distributed P2P method of carrying risk. Society is in need of a distributed system enabling the transfer of risk laterally from individual to individual via formalized methods. This P2P service must be able to carry un-insurable risk exposures, such as deductibles, exclusions, and niche risk exposures which Insurance is not best suited to cover. Those risks at the edge of the market.

As with other “graph companies”, much of this activity already occurs today, and in fact has been occurring since the dawn of civilization. KarmaCoverage.com is designed to formalize these currently informal methods. The financial service is designed in a way that the system’s network effect will enable individual users to mutually benefit by creating financial leverage on their “rainy day” savings funds.

When observing a system through the Complexity paradigm, another key measure to observe is a system’s balance of Resilience vs Efficiency. Resilience and Efficiency sit on opposite sides of a spectrum. A system that is 100% resilient will exhibit an excess of redundancy and wasted resources, while a system that is 100% efficient will exhibit an extreme brittleness lending itself to a system collapse.

When we look at the real world & natural ecosystems for an example, we find that systems tend to self-organize towards a balance of roughly, 67% Resilience and 33% Efficiency. Here is a video https://youtu.be/GH2b6U2CaQU?t=11m20s for more on this optimum balance between Resilience and Efficiency. Shows the optimum balance between Efficiency & Resilience, Optimum is within 2–3% of 67.5% (12:48).. (13:45 shows “window of viability” range graph)

Industrial age ideas have driven economics as a field of study to over optimize for efficiency, but economics is in recent years beginning to challenge this notion, as the field expands into Behavioral Economics, Game Theory, and Complexity Economics, shifting the focus away from solely optimizing for efficiency, towards optimizing for more sustainable and resilient systems. In the risk markets, optimizing for resilience should have obvious benefits.

Now let’s take a look at how this applies practically to the Risk Markets, by looking at the three industry graphs in more detail.

Centralized, network structures are highly efficient. This creates benefits for a user who can pay a mere $1000 per year for Home insurance, and when their home burns down they get several hundred thousand dollars to rebuild. From the user’s point of view the amount of leverage they were able to achieve via their insurance policy was highly efficient. However, like yin & yang centralized systems have an inherent weakness, by taking out a single node in the network, the insurance company, the entire system will collapse. It is this high risk of system collapse that necessitates there be so much regulation. As another example, of this high potential for system collapse underlies the cry in banking to solve, “too big to fail”.

In the Risk markets of today we can observe two ongoing efforts to reduce the risk of an insurance system collapse. We observe a high degree of regulation in the insurance product space, and the existence of Reinsurance markets. The Reinsurance markets function as a Decentralized graph within the Risk Markets. The core purpose is to connect the Centralized Insurance companies in a manner to ensure their inherent brittleness does not materialize a “too big to fail” type of event in the risk markets.

Reinsurance achieves this increase in resilience by insuring insurance companies on a global scale. If a hurricane or tsunami hits a few regional carriers of risk, those carriers can turn to their Reinsurance for coverage on the catastrophic loss. The Reinsurance companies are functionally transferring the risk of that region’s catastrophic loss event, to insurance carriers in other regions of the globe. By stacking the two system’s graphs, combining Insurance & Reinsurance, the Risk Markets ability to successfully transfer risk across society has improved overall system Resilience, while retaining a desired amount of Efficiency.

Observations of nature reveal what appears to be a natural progression of networks to grow in density of connections. It therefore makes sense that the Reinsurance industry came into existence after the Insurance industry, boosting the Risk Markets overall density of interconnections. Along the same line of thought, we would expect to see the risk markets continue to increase in the density of connections from Centralized, to Decentralized graphs of today, further towards the creating of a Distributed graph. A distributed network in the Risk Markets will materialize as some form of financial “P2P”, or “Crowd”, or “Sharing economy” coverage service. This service will not likely use the same business methods as an “insurance” service.

A networks density is defined by the number of connections between the nodes. More connections between nodes mean the network has a “higher density”. For example a Distributed network has a higher density of connections than a centralized network. However a higher density of connections requires more intense management efforts. There is a limit to how much complexity a centralized management team can successfully organize and control.

When a network’s connections grow to become more than centralized management’s capacity to control, the network will begin to self-organize, evolve and exhibit distributed managerial methods. Through this self-organization a new graph structure of the network’s connections will begin to emerge. As this process unfolds over iterations of time, an entirely new macro system structure will emerge that shows little resemblance to the system’s prior state, like a new species in evolution.

What emerges is a Macro phase change, what the media calls “disruption”, which does not necessitate any new resource inputs, only a reorganization of the resources. For example, the Macro state of water can go through a phase change and become Ice. The Micro parts that make up Water & Ice are the same. The Macro state has however undergone a phase change, and the nature of the connections between the Micro parts will have been reorganized.

In his book Why Information Grows, The Evolution of Order from Atoms to Economies, MIT’s Cesar Hidalgo explains that as time marches forward the amount of information we carry with us increases. That information ultimately requires a higher density of connections as it grows. This can be understood at the level of an individual who grows Wise with experience over time. However as the saying goes, “The more you know, the more you know you don’t know”.

In the history of human systems we have observed the need for families to create a tribe, tribes to create a society, and in societies firms organizing to achieve cross society economic work. We are now at the point of needing these firms to organize creating a Network of firms, which can handle increased complexity and coordination. It is this networking of firms which will be achieved via distributed methods, because no individual firm will ever agree to let another single firm be the centralized controller of the whole network, nor could a single firm do so.

Image source, at minute 15:06

In the next segment of this mini series we will look closer at the distributed graph which will become formalized creating a Peer-to Peer or a “P2P” system in the risk markets.

Author: Ron Ginn

www.linkedin.com/in/ronginn/

I have started a LinkedIn group for discussion on Blockchain, Complexity and P2P Insurance. Feel free to join here https://www.linkedin.com/groups/8478617

This series has been picked up by BankNXT.com I will post a link when the next segment is published.