This document describes a suggested application of Tupai technology at a Reinsurer. We have made several assumptions in describing the application:
Dense and Complex Information Flows
The reinsurer has a great deal of information about hazards – earthquakes, tornadoes, marine and air disasters, human error. This information is complex, much of it is domain specific, and it needs to be brought to bear on the evaluation of a particular risk and the assumption of some part of that risk. The information also needs to be used to onsell the risk to other parties who lack the sophistication to fully understand the risk factor research, but who need sufficient information to allow them to integrate the risk they buy into their portfolio.
Complex Position
The reinsurer has a portfolio of investments designed to protect the reinsurer against both rapid short term fluctuations in liquidity caused by several major events occurring together, and long term support of the risk portfolio. The reinsurer needs to put together a complex portfolio of risk, fairly priced. The reinsurer must be confident of its ability to weather the lean years that will inevitably come in this business. Sometimes new research will show that the position in some area is optimistic, even untenable, and needs to be slowly unwound. The more knowledge that can be brought to bear on each transaction, the more confidence that the overall portfolio is wisely constructed.
Complex Client
The client of the reinsurer will either be a large insurance company or a large corporate client capable of carrying some but not all of the risk themselves. If the payout would be to enable the client to continue their business, the reinsurer needs to be convinced that the client would survive the shock and continue to pay the income stream that allowed the risk to be assumed. The client may be large, leading to overexposure, even though the incremental risk is small. The client may be naïve - not fully comprehending their operational risks, unable to see past a physical asset to the total business risk.
Overview
A large reinsurer wishes to deploy its knowledge, built up over more than 100 years, more widely. Of late, that knowledge has become increasingly complex and specialized, as its experts become more skilled in forecasting the likelihood of earthquakes and other natural hazards. The events it insures are moving from the destruction of physical assets, a plane down, to the destruction of intangible assets - an oil spill, an extortion attempt on a pharmaceuticals manufacturer. The increasing scale of the shock of losing some physical asset also require the strongest stomach for risk and the coolest head – the A-3XX for example.
The reinsurer realizes it does not just buy and sell risk, but also sells its knowledge about risk, making it a "knowledge company". It needs an environment which will allow it to acquire increasingly complex and technical knowledge, and to filter and deploy it ever more widely.
To be successful, the reinsurer must understand its client (often a chain of clients, at each remove more loosely bound) and the risk, then make a proposal that takes into account the particular needs of the client (including their behavior if the insured event should occur), the position of the reinsurer vis-à-vis the risk, and a clear eyed assessment of the risk.
Research has shown that the service that clients seek most is a trusted and stable reinsurer who can crystallize the risk issues involved, and also provide support in the areas of financial and disaster planning. The company intends to deliver these services through a choice of channels, including the Internet.
As the reinsurer delves into the problem, the magnitude of the challenge begins to emerge. It quickly becomes apparent that the planned system must deal with a dynamic environment in which client, type of and knowledge about risk, and the reinsurer itself are continuously evolving and changing. Consequently, in its interaction with its clients, the reinsurer must continuously adjust its actions based on the client’s needs and the reinsurer’s knowledge and position.
Solution Overview
In the following description we focus on the elements involved in building a system for reinsurance. We will use a scattergun of problems that relate to different areas but emphasize the difficulty of analysis. Our intent here is to highlight the fact that providing reinsurance requires the integration of multiple types of knowledge from multiple domains. As we mentioned earlier, this ability to integrate the knowledge process is a cornerstone of Tupai’s technology.
eCognitionä addresses the fluid interaction of client, risk, position by constructing a representation (or model) of the situation, and then performing analysis on it. In conducting its analysis, the system is able to deploy all kinds of analysis at every stage. The analysis is not some external process, but flows naturally from activity within the model.
The overall eCognition model for reinsurance comprises of a number of interacting components – the client, the risk, and the reinsurer’s position. Each of these components is modeled, as well as the effects of their interactions.
Model Components
I. The Client
We can draw on generic risk probabilities. Do we need to modify those risks for this particular client – are they operating to industry-standard norms, or are there special conditions that increase the risk. The client is a complex amalgam of location, operation, psychology. Some parts we can analyze precisely, some parts we can analyze using probability, some parts we can only estimate using influences.
Some of the elements of the client’s position (we are assuming the industrial client is large enough for us to deal with them directly):
Location – are there geographic factors – wing icing, poor facilities Sophistication of workforce Reliance on others – do they maintain their own planes Sophistication of market Risk minimization strategies Are they under excessive stress – will they cut corners in the future, or be taken over and operate completely differently
The above hardly exhaust the factors involved, but they do emphasize the difficulty of obtaining a simple analysis of all of the factors.
If we are dealing with an insurance company, have they understood and fairly transmitted the risk to us. Do we need to examine their risk portfolio before accepting any risk from them. Have they recently changed their business practices, so our history with them is of no use.
Understanding the reinsurance client requires a mixture of analysis and experience operating smoothly over a number of dimensions and being combining into an integrated picture of what may be a moving target.
II. The Risk
The reinsurer maintains ongoing research into risk. To a large extent it must be reactive. Some new risks without history need to be estimated based on similar phenomena. Old risks are changing their shape, with companies increasingly being asked to clean up after their disasters.
Information about risk comes in many forms. It needs to be combined into a meaningful whole, which can be applied consistently at any office throughout the world.
III. The Reinsurer
The Reinsurer component of the model acts as the decision-making element. The information coming from the research area needs to be transformed into a form that is relevant to the client’s circumstances – the returns from the various asset classes need to be seen from the viewpoint of the specific client, with their particular tax and income shaping requirements. The reinsurer is matching the client’s risk against the reinsurer’s position for that class of risk and for the financial outlook of the reinsurer’s assets. Optimization of the business proposal is possible once all the relevant influences have been identified. There may be no acceptable solution, so the business must be declined.
There can be very large sums of money changing hands, so the successful operation of the system carries considerable responsibility. An opaque "black box" or sequential programmed approach to computerized reinsurance would be hard to defend in comparison with a knowledge based approach that can demonstrate a large number of influences being taken into account in the decision making process.
Competition
Competition may come from cost of risk or quality of service. The larger the differential against lesser reinsurers, the more the Reinsurer must show that its strategy for handling the client’s risk is based on a sound knowledge of the whole position over the long term.
Interactions Among the Components
I. Client and Risk
This is the main interaction. The client affects the risk, and the risk coming to pass may destroy the client. Occasionally the reinsurer may need to mandate the use of risk minimization strategies, sometimes only realizing their necessity long after the ink is dry.
II. Reinsurer and Risk
The reinsurer, by publishing his research, may define the risk wider than was originally thought, leading to potentially larger payouts. Investigating the possible effects of a 1000 year flood can lead the client to say "You knew all this – now you must pay".
Model Machinery
The elements of model machinery needed to implement a Reinsurance system are:
Model Construction
The model for a particular risk and client needs to be constructed from components that are relevant to the wide variety of circumstances with which the system must deal. These components are not stored as static templates or profiles, but as sub-models which, when connected together, form an active model. Machinery to construct the individual model is part of the model, not an outside process.
Analysis
The system must analyze a wide range of situations. It has available to it all of the elements of numerical and logical analysis. The analytic operators in the model can operate directly on probabilistic values. Dense and complex information can flow through the connections in the model and be analyzed.
Simulation
Some of the decision-making requires simulation of risk scenarios and financial aspects over time, with different influences being active over different time periods.
Probabilistic Knowledge
The knowledge about risks is only known probabilistically, and may span many dimensions. This knowledge is easily captured in distributions and multi-dimensional relations, basic components of the model that allow information to flow across the dimensions of time, risk and money.
Experiential Knowledge
Much of the ability to estimate the risk will come from experiential knowledge, also held in relations in the model. Much of this knowledge can be automatically extracted from research databases, or quickly introduced to the model on an ad hoc basis.
Optimization
The model can create a local area where optimization is possible. It then uses forward cutting to obtain an optimum result. The process of optimization can include any other analysis, as optimization is part of the system’s normal operation. If there is no solution, it can move to a new local area by changing elements within the model.
MicroScheduling
The network is managing the interaction of many influences. The individual operators in the network control their knowledge needs, passing requests for information through the network. Complex interactions are obtained by allowing the many small elements of the network to be responsible for their own states. New influences can be easily added without concern for sequence of operation.
Additional Applications
The Reinsurance system is intended for support of an underwriter or for interaction directly over the web where the involvement of a human analyst would not be cost effective.
The good model of the client is of advantage if and when the predicted hazard eventuates, allowing estimates to be verified and new lessons learnt to be quickly integrated into the system – a strong reason for having a knowledge based system.
