In an interconnected and complex economy corporations form networks, or ecosystems as well as supply chains (or supply networks as they should be called). In the case of banks, telcos or insurance companies these ecosystems can be huge as they may contain millions of clients. Moreover, such ecosystems are changing constantly as companies compete, cooperate and default while new companies are being formed. Rapid change and complexity of the dynamics of these systems are their main characteristics. Like all companies, banks, telcos or insurance companies exists thanks to the ecosystems of their respective clients and, to a large degree, it may be said that the ‘state of health’ of their respective businesses depends on that of their client ecosystems. In the case of large corporate clients of, say, a bank, they themselves depend on their own ecosystems of clients. In practice, we’re talking of a system of systems of immense proportions. The complexity and degree of interdependency between its components determines many interesting characteristics:
- resilience – capacity to absorb destabilizing events and survive turbulence
- speed of propagation of contagion, shocks
- weak points, hubs
The clients of banks, insurance companies or telcos fall into two main categories: retail and corporate. An experiment has been performed an in which an ecosystem of over 1000 corporate clients of a retail bank has been analyzed. The goal was to establish its stability and resilience.
Figure 1 Complexity Map of ecosystem of 1000+ corporate clients of a retail bank
The 1000+ clients span 24 market sectors:
- Automobiles & Components
Banks
Capital Goods
Commercial & Professional Services
Consumer Durables & Apparel
Diversified Financials
Energy
Food & Staples Retailing
Food, Beverage & Tobacco
Health Care Equipment & Services
Hotels Restaurants & Leisure
Household & Personal Products
Insurance
Materials
Media
Pharmaceuticals & Biotechnology
Real Estate
Retailing
Semiconductors & Semiconductor Equipment
Software & Services
Technology Hardware & Equipment
Telecommunication Services
Transportation
Utilities
Analysis has been performed using quarterly balance sheet data but it may also be performed using, for example, monthly transactional data.
It is important to remark that the study described herein is based on a small sample of corporations and therefore it is not meant to be exhaustive. The goal is merely to illustrate what kind of results may be obtained from a similar analysis.
The Complexity Map of the ecosystem is illustrated in Figure 1. It illustrates the structure of inter-dependencies between the various industry sectors as reflected by the data. Large nodes correspond to sectors with a larger footprint on the ecosystem. In the case in question, the structure of said ecosystem is ‘dominated’ by corporations belonging to the following sectors:
- Capital goods
Commercial & professional services
Transportation
Healthcare equipment & services
Real estate
Semiconductors and semiconductor equipment
Clients from these sectors from the structural backbone of the ecosystem in terms of its stability (robustness, or resilience) and not of its performance. Evidently, the bank in question is quite aware of the performance of its clients. The goal of the study is mainly to identify the overall stability, or resilience, of the system of 1000+ clients and specifically to measure the resilience of each sector.
The results are reported in Figure 2. It may be noted that the most resilient sectors in the case in question are:
- Capital Goods
Commercial & Professional Services
Health Care Equipment & Services
Technology Hardware & Equipment
Transportation
Real Estate
Insurance
The resilience of the above sectors is above 70% (see top part of bar chart in Figure 2). The following sectors are the most fragile with resilience below 50%.
- Utilities
Semiconductors & Semiconductor Equipment
Hotels Restaurants & Leisure
Consumer Durables & Apparel
Pharmaceuticals & Biotechnology
The overall resilience of the entire ecosystem is just over 72% while average resilience is approximately 64%.
Figure 2 Resilience by sector of ecosystem of 1000+ corporate clients of a retail bank
Our objective, in the near term, is to essentially remove the above limits and to take the analysis to global scale. With world-class computational resources and in collaboration with data providers we are planning to map the entire global economy on a daily basis so to offer systemic analyses from a multitude of perspectives, such as:
- Market segment
- Geography
- Size/revenue
- Stock markets
- Rating
- etc.
Our analysis shall become increasingly comprehensive and broad-scope not just broad-scale. For example, currently there are approximately 42000 listed companies in 59 stock exchanges. Recently, an analysis of a system of 3400 companies listed on Wall Street has been performed. The analysis was based on quarterly balance sheet information and involved more than 260000 variables. During 2015, all of the 42000 listed companies shall be analyzed as a single interacting system of systems, offering a unique and deep reflection of the dynamics of the global economy. Given that the economy is turbulent and punctuated by destabilizing events, which will grow in intensity and frequency, similar analyses will have to be performed on at least a quarterly basis in order to be relevant. If, for example, before an investor invests in, say, the telco sector, he may want to know how healthy that particular sector is. One approach, the old approach, is to take the top companies, check their rating, look at market statistics, forecasts, and to ask a few experts for their (subjective) opinion. Today we can analyze the entire sector, taking into account all of the companies that belong to and interact with it and offer a complete and systemic picture. Most importantly, this can be done based on data, not on subjective opinions of experts.
We live in turbulent times, in which the only constant is change. Nothing stays in equilibrium. In turbulence an optimal solution on day one is no longer optimal on day two. It is almost impossible to make forecasts and it is certainly foolish to attempt long-term predictions. However, at the same time, we produce about 2.5 quintillion bytes of data every day. This data can and must be exploited. However, old conventional Business Intelligence and Analytics techniques cannot be applied to new classes of problems such as those posed by the global economy and its non-stationary dynamics. It is first of all necessary to look beyond bar charts, scatter plots and probability distributions or linear regressions and to focus on patterns, networks, dynamics and systems of systems. However, the most important transition that must be made is from model-based to model-free methods. The Earth is a huge computer, which produces huge amounts of real data. For free. There is no need to simulate – we have the real thing. Besides, conventional simulation technology is not equipped to cope with problems of similar magnitude and scope. While often useful, models are only models and they can only return what has been hard-wired into them. In a model, the most important parameters are those it doesn’t contain. Clearly, in order to take advantage of this immense volume of data, one needs world-class computational resources and a model-free data-centric philosophy.
In synthesis, by:
- Mapping the entire global economy on a daily basis;
- Adopting a data-centric and model-free approach, and
- Resorting to World-class supercomputer resources
we can offer unique insights into a market sector, supply network (often referred to as ‘supply chain’) geography or the entire global economy at any scale and with a high frequency. The goal is to offer strategic, systemic information and new insights to CEOs of large corporations, institutional investors and financial institutions.
Artificial Intuition 
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