UNIVERSITY OF NAIROBI School of Computing and Informatics

1. UNIVERSITY OF NAIROBI School of Computing and Informatics Application of GIS Spatial Interpolation Methods in Auto Insurance Risk Segmentation territory segmentation and Rating: A Case for Nairobi County-Kenya Ruugia K. Samuel skruugia@gmail.com And Christopher. A. Moturi

2. Problem statement • Insurance uptake too low according to AKI and IRA report of 2009. penetration at 3% • Motor insurance takes over 50% of General insurance business. Poor rating and risk uptake lead to unmanageable claims • Class of Motor Insurance risk; Private Car Individual insurance Cover was considered. • According to IRA regulations limits for the rates are set 3.8% minimum to 7.5% maximum across the region. • These rates apply countrywide. New clients get the highest rate of 7.5% and are given a 10% annual discount for the subsequent 6 years to enjoy the minimum 3.8% provided they don’t file any claims.

3. Main Objectives • To establish GIS techniques, methodologies and data that is best used in insurance risk segmentation and rating. • To create a GIS database to store and analyse collected social-economic, attribute and spatial data necessary for auto insurance risk segmentation and rating. • To apply GIS spatial interpolation techniques on the created Geo-database to segment Nairobi County into varying Auto risk territories and visualize related data maps. • To develop a prescriptive model that demonstrates how GIS generated risk territories can be used in insurance auto risk premium rating.

4. Related works • Case Study: Development of Auto Location Insight (Morgando, 2011) in Minnesota USA. Development of an Auto location insight. Morgando developed auto risk territory maps • Case Study: Geo-analytics for Improved Risk Segmentation in Auto Insurance. by Sanjiv Mishra in California USA. Mishra applied GIS smoothing and clustering techniques to generate a map for spatially smoothed average commute time . • Case Study: Telematics Devices for ‘Pay as You Drive’ for Insurance Premiums. Telematics devices monitor wirelessly a vehicles geographic coordinates and driver’s performance in real time. (AnusuyaDatta, 2013).

5. IDW TOOL SELECTED • Inverse Distance Weighting Deterministic Model It uses the Tobler’s First Law of Geography by estimating unknown measurements as weighted averages over the known measurements at nearby points, giving the greatest weight to the nearest points (Longley et al., 2011). z0 = estimated value at point 0, zi= z value at a known point i, di is the distance between point i and point 0, n is the number of known points used in estimation, and k is the specified power which controls the degree of local influence (Chang, 2010).

6. Effective Data Sets

7. User Needs Analysis (Framing of the questions) Examine and Refine Results Clean Data IS Data Clean? Are results desirable? Research Methodology Framework Explore and identify Data Collection of Data Apply Results i. e as input for Computer App Data Capture and Cleaning YES NO NO YES Preform the GIS Analysis Select Analysis Methods and tools

8. Collected Control Data

9. DATA ANALYSIS AND RESULTS

10. Population Distribution

11. Splines and Kriging territory maps

12. IDW Auto Insurance Risk Territory Map for Nairobi County

13. Comparison between Splines, Kriging and IDW

14. GIS Motor Insurance risk rating table

15. GIS Optimization Insurance Rating Model Objective Function GIS Discounted Rate = GR Decision Variables These are the variables that influence the value of the objective function. GIS Highest risk Territory Upper Limit = Imax Maximum Regulated Rate = Rmax Minimum Regulated Rate = Rmin GIS Risk Territory Upper Limit Residential= GRmax GIS Risk Territory Upper Limit Work= GWmax GIS Risk Territory Lower Limit Residential = GRmin GIS Risk Territory Lower Limit Work = Gwmin Constraints GR > Rmin, GR < Rmax Xi> 0 for i = GR, GRmax, GWmax, GRmin, GRmin, Rmax, Rmin and Imax The GIS Insurance Rating Prescriptive Model GRi =(((GRmax / Imax) + (GWmax/Imax))/2) (Rmax– Rmin)) + Rmin

16. How Objectives Were Met • Identifying main GIS spatial, attribute data and appropriate GIS tools for analysis and visualization. • Data collection and creation of a Geo-database for analysis and visualization. • Segmentation and Mapping of the auto insurance risk in respect to police stations. • Application of rating territories probability ratios from the territory map to develop a prescriptive auto insurance risk rating model and developing a computer system program.

17. Future Works • Apply GIS tools in analysis of other insurance risks to get a better understanding of their distribution within a region. • Extend the scope of application to cover other regions

18. Conclusion-Recommendation • Adoption of the Optimization GIS Auto Rating model developed in this research to improve the Auto insurance product. • IRA and AKI to incorporate use of GIS in carrying out insurance risk research and eventual development of risk rating models. A policy on capturing of spatial risk data will require to be developed. • Insurance and re-insurance companies to employ use of GIS risk analysis and visualization to help in determining their uptake and distribution of risk. • Security operatives to use GIS in auto crime and accident risk analysis to assist in security resource planning and deployment in the area of motor crime and traffic offences.

19. Appendices

20. Main Form Calculating the rates

21. THANK YOU Q &A