Big data and the role of modelling and science in delivering innovative insurance products

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1 Big data and the role of modelling and science in delivering innovative insurance products Dr Patrick McSharry Smith School of Enterprise and the Environment Oxford Man Institute of Quantitative Finance University of Oxford Visiting Professor Carnegie Mellon University Rwanda Contact: 29 th May 2014 CAAST-NET Plus Network, Kigali, Rwanda

2 Objective To use big data and science to construct quantitative approaches for systematic decisionmaking and policymaking To maximise rewards and minimise risks Multidisciplinary: Physics, Engineering, Mathematics, Statistics, Economics, Geography Previous EU funding Marie Curie Fellowship: Ireland to UK Marie Curie Fellowship: UK to Spain PI at Oxford University on FP7 4m consortium (20 members) on wind power generation

3 International collaboration HMG, NERC, EPSRC IBM, EDF Energy, KPMG, World Bank UNISDR, UNEP UN ECLAC World Forum of Catastrophe Programmes

4 Overview Science and policymaking Big data Quantitative modelling Forecasting Risk management Insurance

5 Science and the media Met Office and the BBC: BBQ summer and Mild winter Climategate (2009) L Aquila earthquake: seven natural disaster experts sentenced to six years in jail for manslaughter Judge: assessment of risks connected to the seismic activity in the area around L'Aquila turned out to be completely vague, generic and ineffective

6 Forecast uncertainty Honest forecasts are needed Can society handle probabilistic forecasts? New Met Office forecast system likely to mean 80% chance of confusion Met Office says it will 'empower people to make their own decisions' Tamara Cohen, Daily Mail, 10 Nov 2011.

7 Co-I on Probability, Uncertainty and Risk in the Environment ( 8m consortium in the UK) In-kind support from private and public sector organisations Web portal creating a virtual community for researchers PURE Research Associates enabling academics to work on short-term projects (3-6 months)

8 Science and policymaking Source:

9 British rainfall extremes % Frequncy Month British Rainfall (Symons, G. J.,1885) lists all observed 24 hour rainfall depths which exceeded 2.5 inches (63.5mm). H.J.E. Rodda, M.A. Little, R.G. Wood, N. MacDougall, P.E. McSharry (2009). A digital archive of extreme rainfalls in the British Isles from 1866 to1968 based on 'British Rainfall', Weather, 64(3):71-75

10 Human experts versus machines Traditionally important decisions have been made by human experts Predictive analytics is causing a revolution due to availability of data, computational resources and quantitative techniques Accounting for uncertainty is key for quantifying the confidence underlying the decision-making process

11 Intelligent decision-support Predictive analytics help organisations understand trends and patterns in human activity Facilitating the identification of relationships between explanatory variables and key performance indicators (profit, risk reduction, efficiency) Having impacts on finance, insurance, energy, healthcare, government,

12 Big data Large quantities of data Structured and unstructured data Temporal and spatial Source: Mark Graham, OII Data on individuals, households, organisations Transactional data (searches, clicks, tweets, purchases, comments ) Data about what people actually did and what they are doing in real-time

13 A&E arrivals A&E arrival rate at JR hospital depends on the day of the week, time of day, social events and the weather.

14 Challenges/Opportunities Computation Technical challenges to sift through large quantities of information Interpretation Sentiment analysis, new quantitative/qualitative collaboration Transparency Close the gap between data/models and policy Open access/open source approaches Prediction Evidence-based policy requires standards, evaluation protocols and appropriate benchmarks Scenarios Probabilistic decision support for increasing competitiveness, sustainability, reducing risk and enhancing quality of life

15 Occam s razor William of Occam studied theology at the University of Oxford from 1309 to 1321, but never completed his master's degree Occam s razor (principle of parsimony): seek the simplest model that explains the data How complicated should a model be? How can we evaluate and compare models? What are the appropriate benchmarks? Examples: Parsimonious risk indices (catastrophes, climate) Diagnostics for decision-making

16 Economic Impacts Heating and cooling degree days are used to describe the V-shaped response Effective temperature combines temperature and humidity Simpson & McSharry (2011). An economic assessment of the impacts of climate change on tourism in Barbados. UN-ECLAC

17 Probabilistic forecasts Development of a range of quantitative econometric time series models (parametric and non-parametric) Probabilistic forecasts have been produced for energy demand and production and economic growth Development of techniques for evaluating probabilistic forecasts Value: we can assess a forecast based on its value (economic) for improving decision-making Quality (statistical assessment): reliability, sharpness, resolution and skill A. Lau and P. E. McSharry (2010). Approaches for multi-step density forecasts with application to aggregated wind power. Annals of Applied Statistics 4(3): J. W. Taylor, P. E. McSharry and R Buizza (2009). Wind power density forecasting using ensemble predictions and time series models. IEEE Trans Power Conversion 24(3): S. Arora, M. Little & P.E. McSharry (2013), Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product. Studies in Nonlinear Dynamics & Econometrics (in press). P. Pinson, P. McSharry, H. Madsen (2010). Reliability diagrams for density forecasts of continuous variables: accounting for serial correlation. Quarterly Journal of the Royal Meteorological Society 136(646), pp

18 Early warning systems for EDF Energy Marine species (jellyfish and seaweed) have clogged EDF s reactor at Torness Global warming suggests that threat of jellyfish clogging could increase Using environmental and operational data, we are developing a quantitative early warning system

19 Global Trends By 2050, world population will be 9 billion with 70% living in cities Security of food, water and energy Today, 925 million people experience hunger, with food price spikes and volatility threatening the sustainability of global food security Interconnected risks: Rising food prices à Tunisian street vendor, Mohamed Bouazizi, sets himself on fire and dies à Ben Ali toppled from from power à Arab Spring

20 Model Risk Numerous major losses which were not adequately modelled Japan ($37bn); NZ quake ($12bn); Thai flood ($10bn) Vendor model changes Modelled versus real world & uncertainty Regulation associated with Solvency II and increased focus on 1 in 200 year events Compliance issues relating to model uncertainty and risk assessment

21 Systemic Risk Exposure is more interconnected than ever Sovereign risk, Arab Spring, Spatiotemporal extremes Supply chain risk (outsourcing, lean manufacturing, just in time inventory) Local and regional models are inadequate for assessing potential global losses Thailand flooding was an unexpected loss $10 bn (forecasted to reach $20 bn) Contingent business interruption Semiconductors, car manufacturing

22 Catastrophe Modelling Risk = Hazard x Exposure x Vulnerability Under certain assumptions, extreme value theory or simulation can be used to perform extrapolation in order to assess the probability of extreme losses.

23 Risk Forecasting Quantitative predictive modelling informed by qualitative analysis Fusion of scientific knowledge and empirical investigations based on data analysis Provide prospective rather than retrospective risk analysis Bridge gap between state of the art modelling and real-world decision making

24 Catastrophe risk Member of the Willis Research Network working on parsimonious models for catastrophe risk assessment Assessment of economic losses arising from disasters Demonstration of ability of atmospheric models to generate synthetic hurricanes Potential for global windstorm risk assessment Loss estimates from calibrated HIGEM model (solid) versus commercial CAT model (dashed).

25 Insufficient historical data Ongoing collaboration with EU partners in the SafeWind consortium Assessment of risk of extreme wind speed to measure resource Wind farms sites only have a few years of data Dynamical atmospheric models Reanalysis data (~ 50 years) Anastasiades & McSharry (2014). Wind Energy

26 Index-based Insurance Trigger-based insurance Independently observed index from weather stations, sensors or satellite information Advantages No adverse selection No moral hazard Challenges Data collection and modelling Basis risk

27 Dynamic risk management: Big data Insurance Telematics Insurance EU regulation against gender discrimination Black-box technology (GPS, accelerometer) to monitor driving Loss of points and higher fee for breaking speed limit and unsafe driving Healthcare Insurance Reduced fee for gym attendance, health checks and proof of exercise (pedometer)

28 Stranded Assets Risk Hazard: regulation, pricing, technology, society and physical climate change Exposure: portfolio weights Vulnerability: carbon intensity of business Financial losses: quantify using VaR and EP curves

29 Conclusions (1) PPPs to better understand risks by constructing open-access models, improving data quality and embracing forward-looking risk forecasting techniques; (2) funding for IT infrastructure, data collection and independent evaluation of model accuracy and parametric insurance products; and (3) developing education and training programmes for risk management and alternative insurance products.

30 Future Research Performance-based evaluation of risk models Comparison with simple benchmarks in order to provide quality control for end-users Risk indices based on parsimonious models Hurricane risk index using atmospheric models Stranded asset risk and company valuation Risk forecasting applications Risk assessment using atmospheric models Lighthill Risk Network (open-access CAT model)

31 References R. Thomas, P.E. McSharry (2014). Big data revolution. John Wiley & Sons, London (forthcoming). P.E. McSharry (2013). The role of scientific modelling and the insurance industry in dealing with climate change. In Preventing Disaster: Early Warning Systems for Climate Change, Singh, A., Zommers, Z. (Eds.), Springer, New York. P.E. McSharry. Stream analytics for forecasting and real time risk management (2012). Foresight: The International Journal of Applied Forecasting, 24, 7-12.