Insurance Data Sample Clauses

Insurance Data. To the extent that, after the Closing, either Buyer or Seller requires any information regarding claim data, payroll or other information in order to make filings with insurance carriers relating to the Business, Seller shall promptly supply such information to Buyer and Buyer shall promptly supply such information to Seller.

Insurance Data. In this work, the employed damage functions are calibrated against detailed insurance loss data obtained for storm damages to residential buildings. The German Insurance Association (GDV) provided loss data relating to the ‘comprehensive insurance on buildings’ line of business resolved for 439 German administrative districts (as of 2006). The dataset comprises the magnitude of absolute losses and insured values as well as the num- ber of claims for the years 1997 to 2007 on a daily basis. With its high spatio-temporal resolution and countrywide coverage, the GDV dataset has been successfully applied for the calibration of different damage functions, e.g. Donat, ▇▇▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇ & ▇▇▇▇▇▇▇▇ (2011), ▇▇▇▇▇ et al. (2012), ▇▇▇▇▇▇▇▇▇▇▇▇ et al. (2013). In order to eliminate price effects and time-varying insurance market penetration, we consider relative figures for the amount of loss and claims throughout. The following definitions are applied: These definitions are based on the assumption that insured buildings are randomly distributed in each district and are representative of the overall residential building stock. With data coverage of up to 13.4 million insured buildings and in excess of 90% market coverage (GDV 2013) we expect the assumptions to hold. The highly skewed and heavy-tailed distribution of daily losses during the winter half-year is illustrated in Fig. 17. More than 50% of total loss is recorded for the top 6 out of 2000 loss days. The shaded area in Fig. 17 highlights the upper 10% of loss days, comprising in excess of 90% of total loss. For economic relevance, our work focusses on this loss segment, with a sub-division into 3 distinct loss classes, as shown in Table 13. Table 13: The three loss classes defined for the winter half-year. Loss class Description No. Quantiles of daily losses Loss share I Extreme 6 0.997-1.000 54.9% II Large 34 0.980-0.997 23.4% III Moderate 160 0.900-0.980 15.0% The vast number of days exhibiting negligible insured loss appears to be due to a random scattering of small losses across time and districts. Supporting the attribution to noise, ▇▇▇▇▇ et al. (2012) found a direct proportionality between the magnitude of the temporally scattered losses and the number of insured contracts in a given district. Cumulative probability