Knowledge Management In the Insurance Industry

1. Knowledge Management In the Insurance Industry Making Organisational Knowledge Active

2. Knowledge Is Structure What does that mean? A “live” structure can have the flow in it changed, or its elements rearranged to produce a new structure, for a new purpose. What sort of structure does it imply? The structure contains everything needed to represent the knowledge - nothing is “outside” it. The topology of the structure is modifiable by the structure - it is self-adapting.

3. ORION Orion is a knowledge utilisation system based on using an active, undirected structure to represent knowledge. Orion covers the full knowledge cycle – creation or capture, marshalling and deployment. It is particularly suited to dynamically changing problems, where its ability to modify its own structure comes to the fore.

4. A new approach to knowledge Instead of writing programs, turn the knowledge itself into a computing machine Example: a + b = c Structure Programmatic: if input(a) and input(b) then c=a+b else if input(a) and input(c) then b=c-a else… NYK - Not Yet Known

5. Use the structure itself Example: a + b = c Values flow through the structure in any direction. The EQUALS operator allows for logical control.

6. The System as a Thinking Tool Orion The user can turn what they know into a shareable and reusable piece of knowledge by describing relations among objects - the system will use the relations any way it needs to, when it needs to. Users can refine their thinking by observing a model of it in operation.

7. Combining Knowledge Domains Knowledge models from different domains can be combined - each expert operates in their own domain, thinking in ways they are comfortable with. Economy Funding Credit Risk Integrated Model FX

8. Techniques • Knowledge about the problem area is turned into a structure, made up of variables, operators and links. The structure is undirected and extensible, and supports the following problem-solving techniques: • Logical and Numerical Analysis • Simulation • Structure Self-modification • Ranges of Values • Backtracking • Constraint Reasoning

9. Logical & Numerical Analysis Here are logic and numeric ranges interacting together in a plan.

10. Using the System

11. Logic Editor - Used to enter declarative knowledge in textual form. The text is immediately transformed into active model structure.

12. Network Display The user can: • trace the structure of the network • observe the values in particular components • set and unset values • trace the source of inconsistencies • debug the network by halting propagation and observing states

13. Stochastic Editor • The user can • visualize and manipulate distributions and N-dimensional relations. • apply constraints to variables and observe the impact on other variables • construct ad hoc distributions and relations

14. Data Miner One of the shortcomings of existing data mining technologies is that in order to use the findings, the user needs to understand them. The reason is that the technologies used for mining are different to those used in operational systems. With Orion, the same technology is used for both tasks. The Miner actually morphs newly found correlations in the data into an active component of the operational system. The system can start with mined data, then “learn on the run” from new transactions.

15. Additional tools and facilities • Structural debugger • Control Panel • Simulator • Graphing • XML Link • Time Series Analysis module

16. Insurance Applications • Knowledge Management • Dynamic Financial Analysis • Simulation of Reinsurance environment • Risk analysis • Knowledge Representation

17. “Soft”, qualitative Databases Capturing the “Big Picture” Integrates all forms of knowledge and information. This morning’s new developments Analytic, quantitative Experiential

18. DFA • Model the asset portfolio • Model the insurance risks • Combine these models easily • Simulate strategies in diverse scenarios • Refine an effective strategy using machine learning DFA is often set up with very primitive strategy - trouble is, no-one believes its results because they know it is primitive The ability to combine analysis and experience makes the simulation an accurate description of what the organisation would do under adverse circumstances

19. Network Structure – Bond Valuation The network structure created by the statements entered through the textual logic editor.

20. Keeping Close to the Meaning Minimising the transformation of knowledge maximises the understanding of what’s happening GetV(Tv, Tmat, N, C) = N * ( GetP( Tv, Tmat) + SIGMA( Tv + 1, Tmat - 1, currT, GetP( CurrT, Tmat)) * C) GetP(t, Tmat) = AtTs%[ Tmat - t ] * exp( -1 * BtTs%[ Tmat - t ] * Rates%[ t ] )

21. History Based Rates Forecasting The distribution of RateNow The relation between RateNow and NextRate The distribution of NextRate Knowing the rate now, we can use history to forecast the rate in the next time period. The history is mined from the database and stored in operators that are used to propagate distributions. Setting RateNow to a value changes the distribution of NextRate, used by a RANDOM operator to generate a singular number. Full population RateNow set to 3.14 NextRate RateNow set to 7 RateNow RANDOM DISTRIB DISTRIB RELATION

22. Catastrophe Modelling Using Available Knowledge The distributions of storm severity and of number of storms per year are used to generate values. Once generated, a histogram connecting those with geographic location is used to generate the anticipated damages. The network structure below shows the flow of information. Some of the distributions and histograms used. All can be “massaged” graphically by on-screen dragging.

23. Creation and Destruction of Assets When the simulation starts, the portfolio has 10 bond invocations maturing on 2001 to 2010. After a 20 years simulation, where maturing bonds are destroyed and new bonds are purchased if the cash position allows, the portfolio is left with 3 bonds purchased in 2018, 2019 and 2020. Different strategies for asset management can be studied under different rate scenarios.

24. Reinsurance Simulation Knowledge Model based on Modelling the Impact of Reinsurance on Financial Strength by S. Coutts & T. Thomas Source

25. Orion Commercial Property Programmatic approach Household Quota Share treaty Surplus treaty Risk Access treaty Catastrophe treaty Business Domain Model the Problem Area The model takes the form of the business structure it represents. It is “alive” all the time, every piece of it is visible and accessible.

26. With ORION As is Business Domain Software Domain No Programming Programmatic Approach Distortion Software Domain Programmers Analysts Business Domain

27. Reinsurance Simulation The structure generated for Commercial Property (each column represents a policy) Household Generated Commercial Property Claims A Surplus treaty Claims paid after the Surplus recoveries Risk Excess treaty Claims paid after the Surplus and RiskXs Combine with Household Claims after Quota Share (into Catastrophe treaty) The structure of the reinsurance program is in the form that insurance people understand it – claims flow into a reinsurance treaty, which recovers some of the payments. What remains can be combined and flow into another treaty.

28. Dramatically Shorter Implementation and Modification 18 Months / 45 Man-months C++ Implementation 3 weeks / 1.5 Man-months 6-8 Weeks C++ Modification 1-3 days Test case results, May/June 2001

29. Stability of Model C++ Model “The model took 18 months to build. ...the authors found they wanted to enhance the structure almost on a daily basis. ...regular enhancements would bring about a collapse of the project...” ORION Model The structure of the model can be quickly and easily changed, because it is a knowledge model, with the structure being part of the knowledge.

30. Risk Analysis Using Current Knowledge The area of risk being analysed in this example is Reinsurance of Earthquakes This is an area of physical science with relatively little analytical support. Part of the model is to update its knowledge from free text.

31. Earthquake Knowledge Attenuation The model is built out of the same variables, operators, links as the knowledge structures being used to analyse the text Acceleration attenuation based on magnitude, distance and local site conditions Greece Info (GIS) Intensity/ Damage Find distance between site and epicentre, local conditions, etc. Relations between acceleration, intensity and damage ratio Earthquake Event Lloyds List Frequency/ Amplification Relations between magnitude and frequency, building type, number of floors and natural frequency

32. Analytic Component - Wave Attenuation A formula for attenuation (note left hand side)

33. Propagation Experiential submodel  Damage Ratio An example of one of many value propagation paths, from event magnitude to damage ratio. Experiential submodel  intensity Magnitude – Event submodel Attenuation submodel  SAh

34. Experiential Structure Wood Reinforced Concrete The Sauter curves, linking Intensity, Building Type and Damage Ratio, are loaded into distributions and relations. These curves represent distributions that respond to other factors - earthquake codes, locales.

35. Accept Anything • Here is KM at work. Need to accept: • Different forms of knowledge - analytic, experiential • Different timescales for validity - yesterday, last month, always • Different sources - journals, consultants, suppliers, customers • Integrate it, iron out the inconsistencies, if necessary change it on the run - manage it like any other resource.

36. Orion can handle a wide range of problem areas.Its power comes from its simple form - variables, operators, links - and the fact that it is easy to combine structures which do not have a beginning or an end.

37. What Can You Put in a Model • Numbers • Strings • Logicals • Lists • Objects • For loops • RANDOM Generators • Distributions and Relations • Bayesian logic • New operators

38. How Is It Different? It is based on: Connection Influences can only take place through connections. Activity Active structures carry out processing of the information that flows through them directly, and can adapt themselves to each other. Visibility All the states are visible all the time - no structure is hidden or built on a stack.

39. It Sounds Too Complex Complexity appears quite quickly. A + B = C is static. X = SUM(List) is already dynamic. “The earthquake struck Sao Paulo last Thursday.” is just more dynamic. Knowledge in an organisation is dynamic - it is just that humans handle it without a second thought. For a system to handle it well, the system needs some similar properties - activity, connectivity, visibility.

40. Is It Really So Different We are asserting that knowledge can only be captured in active structure - structure that is capable of adapting itself to its environment. Efforts at capturing knowledge in static structure founder on two reefs - the pieces of structure will not fit together statically, and an algorithm that could manage their combination would be more complex than the combination of the pieces, and is thus both unmanageable and unreachable from text. Active structure avoids both problems - the pieces adapt to each other, and the behaviour of the combination is managed by the interaction of the pieces.