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Mark Lynch - Importance of Big Data and Analytics for the Insurance Market

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This is a presentation delivered by Mark Lynch, at the STFC Futures / RUSI Conference Series: Data for Security and Resilience 2014

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Mark Lynch - Importance of Big Data and Analytics for the Insurance Market

  1. 1. Importance of Big Data and Analytics for the Insurance Market Mark Lynch Impact Forecasting
  2. 2. 1 Aon Benfield | Impact Forecasting Proprietary and Confidential Agenda  Political violence and the insurance industry  Use of big data and analytics in the insurance market  Challenges for the industry  Conclusions
  3. 3. Section 1: Political Violence and the Insurance Industry
  4. 4. 3 Aon Benfield | Impact Forecasting Proprietary and Confidential What constitutes political violence?  Political violence can encompass a number of things but within catastrophe modelling this is largely broken down into three key sectors  Each sector has its own intrinsic difficulties in terms of modelling and analysis  Can potentially look at each sector as a reflection of domestic support for political violence Terrorism and Sabotage Strikes, Riot & Civil Commotion Insurrection and Revolution
  5. 5. 4 Aon Benfield | Impact Forecasting Proprietary and Confidential Why does it matter to the Insurance Industry?  Political violence drives the propensity of losses in a whole variety of Lines of Business: Property Business Interruption Life Motor Workers Comp Contingency Credit Risk Health Kidnap and Random  The insurance market’s shift towards emerging markets in recent years has increased market exposure to political violence  Long term instability can have an adverse effect on the entire economy, depressing the economic viability and the insurance market Greater Penetration in Emerging Markets = Greater Exposure to PV Risk
  6. 6. 5 Aon Benfield | Impact Forecasting Proprietary and Confidential Catalyst for Political Violence Modelling Historical Changes – Terrorism Threat  Terrorism modelling is a relatively new field in catastrophe modelling  It has grown more prominent in the wake of a number of large market losses stemming from terrorism  9/11 compounded this and remains the 5th largest catastrophe loss ($22bn) and is likely to grow... “The huge payouts by insurance companies contributed to a crisis in the industry, including the near-collapse of the world's leading insurance market, Lloyd's of London.” (BBC, 1993)
  7. 7. 6 Aon Benfield | Impact Forecasting Proprietary and Confidential Catalyst for Political Violence Modelling Impact on Resilience and wider community  Due to increased uncertainty within the insurance market this has a knock on effect on resilience as a whole  Increased perception of the risk leads to an a number of factors that have a potential effect on recovery: – Removal of terrorism coverage from policies – Exclusion of high risk areas – Exclusion of CBRN coverage – Increased price of coverage  With a vacuum in the insurance market for terrorism coverage this dilutes the capacity of business and
  8. 8. Section 2: Big Data and Analytics
  9. 9. 8 Aon Benfield | Impact Forecasting Proprietary and Confidential Impact Forecasting model suite
  10. 10. 9 Aon Benfield | Impact Forecasting Proprietary and Confidential Natural Perils: Christian - Met Office data  1,378 stations available, high concentration in UK and countries of Western Europe  Period: 26th Oct 18:00 to 28th Oct 09:00 GMT 34 443740 43 40 3723 28 47 3138 2820 33 18 31 26 25 17 23 28 2032 29 21 29 26 21 30 23 2027 29 26 17 19 37 25 35 38 24 21 21 17 22 23 1922 17 24 19 21 36 35 27 20 24 30 37 25 25 14 21 34 32 40 38 4131 37 42 32 25 37 35 31 32 33 28 28 37 35 34 31 34 31 37 40 34 32 37 33 37 34 3336 27 31 37 31 34 27 29 34 30 29 31 32 31 29 2325 34 24 29 30 25 26 39 27 24 33 332731 30 2930 30 2429 23 27 27 23 31 27 27 19 23 2120 21 26 25 25 32 22 2426 22 20211821 24 22 26 22 22 22 22 21 17 21 24 18 16 17 17 16 21 22 20 23 23 22 19 30 28 33 32 29 25 32 29 30 18 39 27 21 30 2021 19 22 23 37 37 25 21 22 16 30 24 35 21 38 38 39 3230 31 34 34 34 3542 44 35 38 39 36 34 27 3730 3830 37 42 40 37 39 3439 27 35 34 39 34 31 31 31 29 3828 29 33 33 28 3441 3642 3528 32 39 31 37 32 34 32 39 36 38 43 36 32 34 34 31 37 28 40 31 32 2527 3132 33 25 38 24 28 29 25 36 22 24 25 23 31 27 22 2926 25 30 30 3431 25 27 27 23 24 20 21 27 22 21 23 23 16 19 21 25 23 17 18 21 20 12 22 18 4 537 18 12 31 27 14 27 26 25 12 17 17 31 29 22 24 26 27 2219 17 23 17 18 16 22 20 15 14 19 16 15 19 17 17 17 10 15 13 13 9 10 11 11 13 15 16 1617 18 15 11 149 13 9 10 8 9 10 10 8 11 9 9 10 12 8 13 11 13 11 9 108 6 8 11 6 7 8 9 10 7 11 4 10 11 5 68 7 10 4 7 8 9 6 6 3 4 7 6 8 10 14 24 1222 25 15 1610 6 7 7 8 7 9 7 8 17 1418 10 1216 25 22 2628 167 10 25 31 88 8 7 21 5 11 12 7 38 38 35 41 33 36 44 34 3440 37 35 3034 34 32 35 34 3131 30 25 28 2625 27 34 31 22 38 31 29 28 28 3127 24 232023 23 25 28 23 24 22 22 27 2428 21 23 21 22 21 22 24 25 23 24 26 2223 24 26 21 22 23 24 24 26 27 25 2123 21 27 2020 25 20 20 23 2223 2426 23 25 222619 23 25 24 28 22 22 21 24 24 2624 19 2721 21 2323 2221 22 27 24 23 20 24 2022 2520 23 19 16 21 20 19 2220 23 25 24 22 24 20 19 26 20 21 21 18 16 20 23 18 19 18 20 24 19 26 21 22 20 2125 22 21 20 19 15 22 20 18 30 20 17 25 20 21 2022 22 22 24 21 24 20 19 27 30 27 18 29 21 16 17 2016 13 23 27 25 17 29 19 23 17 25 1626 16 16 19 2120 25 26 25 22 34 22 2219 21 21 20 25 22 22 20 26 22 20 24 21 20 27 26 33 28 33 25 19 23 25
  11. 11. 10 Aon Benfield | Impact Forecasting Proprietary and Confidential Natural Perils: Christian - Footprint specification  0.5 * 0.5 degree footprint downloaded from http://nomad3.ncep.noaa.gov/ncep_data/  Updated daily, used: 26th,27th and 28th October  Low resolution compared to the model may underestimate losses – Increase by 5, 10 and 20% tested
  12. 12. 11 Aon Benfield | Impact Forecasting Proprietary and Confidential
  13. 13. 12 Aon Benfield | Impact Forecasting Proprietary and Confidential Unique nature of Political Violence Variations within each country  Even within territories the level of risk can vary greatly  Understanding this can have a material impact on insurance industry  We see similar issues in important emerging markets: – BRIC countries – MINT Countries Armed Conflict Location & Event Data Project (1997-2010)
  14. 14. 13 Aon Benfield | Impact Forecasting Proprietary and Confidential Multiple point simulation of the potential target Example: building: Pentagon Is point representation of the target good enough? • The image shows the maximum distance of damage • This masks a significant variation in losses that could occur vary depending on the location of the initial blast • Without this models are underestimating the potential loses –  Compared to other “natural” perils, detailed geographical location is critical for modelling terrorism risk  Our models encapsulate this and highlight the variation that can occur in the losses  01 Terrorism modelling Example loss distribution for specific attack The solution is polygon with multiple attack points • We use a polygon system and simulate blasts on over 200 sites for each target • This helps to display the target uncertainty that is inherent within each site • From this we can simulate over 4,000 attacks for each target within the model – Terrorism Modelling Hazard component
  15. 15. 14 Aon Benfield | Impact Forecasting Proprietary and Confidential Truncated illustrative target and attacks type probability matrix Synthesised IF Database of US Terrorist Attacks  In order to contextualise the losses it is important to identify the likelihood of each attack  Based on historical data, plot analysis and local expert input we are able to project the risk of terrorist attacks  01 Terrorism modelling Percentage of attacks against government buildings in US Conventional (explosives, vehicle-borne devices) Non-conventional (nuclear) Non-conventional (CBR) 97.0% 1.0% 2.0% Financial 3.0% 2.9% 0.0% 0.1% Embassies 5.0% 4.9% 0.1% 0.1% Government 17.0% 16.5% 0.2% 0.3% Military 9.0% 8.7% 0.1% 0.2% Place of worship 1.0% 1.0% 0.0% 0.0% All other targets 65.0% 63.1% 0.7% 1.3% Terrorism Modelling Probabilistic component
  16. 16. Section 3: Challenges for the Industry
  17. 17. 16 Aon Benfield | Impact Forecasting Proprietary and Confidential Challenges Data Quality  The quality of data that we receive from clients can vary wildly and is key to analysis  Blast analysis is based on extremely fine details and variance on this effects  Without this analytics proves to be highly uncertain  Data quality can be constrained by privacy concerns and market problems (competitiveness, lack of hierarchy)
  18. 18. 17 Aon Benfield | Impact Forecasting Proprietary and Confidential Challenges Market issues hindering a more accurate understanding  Short term historical memory: threat of political violence risk oscillates between mass hysteria and calm based on temporal distance from an event  Cultural issues: some brokers do not see the need for analytics in this space due to the human element, some deny the existence of risk  Arm chair expertise: political violence dominates the news thus people believe they have a comprehensive understanding of the risk including the biological impact of Cesium-137  Poor Data: Data poverty that was previously mentioned
  19. 19. 18 Aon Benfield | Impact Forecasting Proprietary and Confidential Challenges Poor central coordination  Cooperation between the insurance sector and government is still rather limited and the UK pool that deals with terrorism (Pool Re) plays a limited role  Insurance industry requires better empirical data, access to classified documentation and central coordination  Government could benefit greatly from knowledge on concentrations of a lack of insurance, the details of their coverage and potential exposure to a large scale attack  Greater cooperation and data sharing would help the industry immensely and bolster resilience capacity
  20. 20. Section 3: Challenges for the Industry
  21. 21. 20 Aon Benfield | Impact Forecasting Proprietary and Confidential Future Trends and Expansion Overall Trends  Overall the market is moving over to a more analytical framework to investigate all catastrophe events  The development in sophistication for political violence models has been exponential as it is a nascent area of analytics  Market forces are pushing the insurance sector towards a more fundamental understanding of the risk and this can only be a positive thing  Without a detailed understanding of the risk, insurers are likely to over- or under- estimate the threat having knock on effects for resilience  Greater cooperation on data sharing, standardisation and analytics would allow the insurance industry to play a more fundamental role in Analytics
  22. 22. Contact Mark Lynch Impact Forecasting + 44(0)20 7522 8255 mark.lynch@aonbenfield.com
  23. 23. 22 Aon Benfield | Impact Forecasting Proprietary and Confidential Disclaimer Legal Disclaimer ©Aon Limited trading as Aon Benfield (for itself and on behalf of each subsidiary company of Aon Corporation) (“Aon Benfield”) reserves all rights to the content of this report (“Report”). This Report is for distribution to Aon Benfield and the organisation to which it was originally delivered only. Copies may be made by that organisation for its own internal purposes but this Report may not be distributed in whole or in part to any third party without both (i) the prior written consent of Aon Benfield. and (ii) the third party having first signed a “recipient of report” letter in a form acceptable to Aon Benfield. Aon Benfield cannot accept any liability to any third party to whom this Report is disclosed, whether disclosed in compliance with the preceding sentence of otherwise. 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