Supporting Quantitative Analysis for. The Use of Credit Information for Insurance Purposes. Prepared by Baron Insurance Services.

Transcription

1 Supporting Quantitative Analysis for The Use of Credit Information for Insurance Purposes Prepared by Baron Insurance Services For Canadian Association of Direct Response Insurers May 211 Report Authors: Barb Addie Brad Neilson

2 Page 2 of 1 This Page Was Intentionally Left Blank

3 Page 3 of 1 Table of Contents Executive Summary... 5 Context... 5 Scope... 5 Data... 5 Analysis... 6 Total Automobile (Appendix A)... 7 Total Property (Appendix A)... 7 Accident Year Automobile (Appendix B)... 7 Accident Year Property (Appendix B)... 7 Credit Score Used In Rating (Appendix C)... 8 Prince Edward Island (Appendix D)... 8 Definitions... 8 List of Appendices... 9

5 Page 5 of 1 Executive Summary Credit scoring is used in rating by the majority of the top personal lines companies in the United States and has become increasingly important in recent years to remain competitive. Multiple papers have come to the conclusion that credit score is correlated with and predictive of insurance losses (e.g. Monaghan, The Impact of Personal Credit History on Loss Performance in Personal Lines, 2; Wu and Guszcza, Does Credit Score Really Explain Insurance Losses? A Multivariate Analysis from a Data Mining Point of View, 23). Those papers have relied on US data, providing a good deal of credibility and a proxy for the Canadian industry due to the size of the US industry and similarities between the two. This analysis uses solely Canadian data and reaches the same conclusions, demonstrating a strong correlation between credit score and insurance losses. Context The correlation between credit score and insurance losses has been demonstrated in multiple studies; however, these studies have used data primarily from the United States. In defending the use of credit score in insurance rating, CADRI has been requested to provide Canadian data on a nationwide and provincial basis supporting its position. We were selected as an independent third party to protect the confidentiality of the member companies data. Scope This report is prepared to illustrate the results of the analysis performed on credit score data sent to us members (the Companies) of the Canadian Association of Direct Response Insurers (CADRI). As an independent third party, we are responsible for aggregating and verifying the reasonableness of the data. In addition, we are performing an analysis of the relationship between credit score and various insurance loss metrics. This analysis is intended to show a broad picture of this relationship and, assuming the significance of the findings, justify further studies in more detail. This report does not address many of the political issues surrounding the use of credit score and is put forth simply to show the statistical significance of credit score in insurance. In preparing this report, we have relied on the Companies actuaries, who reviewed the data before sending it to us. Although we can evaluate the data s reasonableness to a degree, we have relied on the Companies for its accuracy. Despite the limitations of this analysis, the volume of data is credible enough to demonstrate a relationship of credit scores to insurance losses. Data Members of CADRI were requested to fill out a uniform template (shown in Appendix P) containing automobile and property insurance experience for the last three years. The following five members provided data for our analysis: - Belair Insurance Inc.

6 Page 6 of 1 - Desjardins General Insurance Group - RBC Insurance - TD Insurance Meloche Monnex - The Co-operators Group Ltd. The provided template broke down the Companies insurance history by province, accident year, line of business, credit score and whether or not credit score was used in rating. The data provided was exposures, premiums, number of and incurred (excluding IBNR provisions). Complications arising from different ways of measuring credit score were addressed by grouping insureds with credit ratings into percentiles. The 1% of insureds with the lowest credit scores were grouped ( P-P1 ), the next 1% were also grouped ( P1-P2 ), etc. Bankruptcy and No Hit, No Score, No Consent were also provided as categories for those policyholders who did not give or have credit scores. Providing data in a uniform manner and eliminating numbers that can vary between companies (method of credit scoring or setting of IBNR) allowed for the summation of relatively homogeneous data. One s property data was aggregated by average, as opposed to individual, credit percentile. This s data was excluded from the property analyses due to clustering and a non-uniform distribution of credit scores. All remaining data was re-coded when necessary to reach a uniform state and then aggregated. We looked at the trends in the data and determined that it would not materially enhance the usability of the results of the analysis to add in adjustments for trends over the three year period. Also, it would not greater serve the purpose of providing a broad look at the correlation of credit scores to insurance losses by adding trends to a study not intending to specifically quantify such a correlation. Tables showing the various aggregations of the data are shown in the Appendices along with comparisons of the exposures with credit scores available to those without. The Bankruptcy category was not used in the analysis, because of limited exposures in this category, and has been excluded from the Appendices. Analysis All analysis contained in this report was done on the aggregation of the Companies data to protect the confidential nature of the data. Linear regressions were run in Excel showing the relationship between credit score and claim frequency, average claim cost, pure premium and loss ratio. Regressions were performed on the following for each of property and automobile: - Total of all Canadian data - By accident year (27-29) - Policies where credit rating was used/not used - By province (territories combined) The formulae and R-squared values for the regressions are displayed on the graphs in the appendices. Since the data was regressed against credit score percentile groups of insureds, the significance of the R- squared values is more useful than the formulae themselves. This study is more concerned with displaying a statistically significant correlation than determining the magnitude of that correlation.

7 Page 7 of 1 Total Automobile (Appendix A) On a total basis for Canadian auto, claim frequency shows high correlation with credit score (R 2 =.9788). Pure premium and claim cost also show high correlation with R 2 values around.9. The range of pure premium from those in the lowest bracket of credit scores to the highest is 323 to 985; for comparison, the average pure premium of those who did not provide credit information and the total average was 572 and 58 respectively. Loss ratios ranged from 43% to 92% around a total average of 62%. Credit score data was available for 59% of the exposures submitted for this analysis. An additional exhibit showing Canadian auto excluding Québec is given. This is effectively the same exhibit as Appendix C Auto Credit Score Not Used; however, we believed it would be pertinent to show beside the full Canadian industry exhibit as well. Compared to Canada including Québec, it shows steeper relationships between all four variables and credit score percentiles with similar R 2 values; the loss ratio exhibit, especially, would suggest that Québec s use of credit scores in automobile rating is lowering the difference in loss ratios between good and poor credit scores. Overall, the data supports better credit scores being correlated with lower claim frequency, lower claim cost, lower pure premium and lower loss ratios. Total Property (Appendix A) On a total basis for Canadian property, pure premium shows a high correlation with credit score (R 2 =.9128). Claim frequency has a very high correlation with an R 2 of.9687 and claim cost less so at.798. The range of pure premium from those in the lowest bracket of credit scores to the highest is 244 to 64; for comparison, the average pure premium of those who did not provide credit information and the total average was 391 and 398 respectively. Loss ratios ranged from 47% to 95% around a total average of 65%. Credit score data was available for 6% of the exposures used in this analysis. The data supports better credit scores being correlated with lower claim frequency, lower claim cost, lower pure premium and lower loss ratios. Accident Year Automobile (Appendix B) The relationship between credit score and claim frequency is virtually the same in the three accident years looked at and highly significant in all (R 2 >.97). Pure premium is has a high R 2 (~.91) for all accident years though the slope of the relationship has a larger absolute value due to higher than normal losses in the worst credit percentile. The significance of the relationship between claim cost and credit score varied between accident years (.7885 < R 2 <.887). Loss ratio had an R 2 varying through the mid.8 s and had the same difference as pure premium in accident year 29. The data supports better credit scores being correlated with lower claim frequency and lower pure premium across different accident years. Lower claim costs and lower loss ratios also appear correlated with better credit scores, but with lower R 2 values. Accident Year Property (Appendix B) Pure premium showed higher levels of correlation to credit score in accident years 28 (R 2 =.9344) and 29 (R 2 =.9121) than in 27 (R 2 =.8218) though still high in all years. Claim frequency showed a very high correlation in all years: 27 (R 2 =.9548), 28 (R 2 =.956), 29 (R 2 =.9778). The significance of the relationship with claim cost varied from an R 2 of.6636 to The R 2 values for

8 Page 8 of 1 loss ratios were similar but slightly lower than pure premium. The data supports better credit scores being correlated with lower claim frequency, lower loss ratios and lower pure premium across different accident years. Lower claim costs also appear correlated with better credit scores, but with lower R 2 values. Credit Score Used In Rating (Appendix C) Quebec is the only province allowed to use credit score in rating automobile insurance. The data shows that although credit scores are used, there is still a significant correlation (R 2 =.9641) between credit score and loss ratio, though the magnitude of the relationship is much less compared to other provinces where credit score is not used in rating. Since credit score information is often not gathered for insureds where credit scoring is not used in rating, the level of significance for the regressions run on policies not using credit rating will be lower (more policies will fall under the No Hit, No Score, No Consent category). All insurers in the analysis use credit score for rating property policies; hence, there is not a breakdown of insurers that do and do not used credit score in rating property. Prince Edward Island (Appendix D) Appendices for each province have been compiled with descriptions of the findings at the beginning of each appendix. Additionally, Appendix E shows the Atlantic provinces combined Definitions Accident Year grouping of portions of policies that have premium earned and any that occurred during a particular calendar year IBNR Incurred but Not Reported estimates on reserves for that have occurred but have not been included in the case reserves set by adjusters Pure the actual cost of settling any associated with a particular policy or group of polices before any expenses not associated with individual ; equal to average claim cost x frequency R-squared generally, the portion of variance of the data set that can be explained by a particular model (ie. Linear regression) as opposed to outside factors or randomness loss incurred divided by premiums earned for a given accident year loss incurred divided by the number of Frequency number of divided by the number of number of cars / properties insured

9 Page 9 of 1 List of Appendices Total All s, All Accident Years Appendix A Total All s, By Accident Year Appendix B Total Credit Score Used/Not Used in Rating Appendix C Prince Edward Island Appendix D Atlantic s Appendix E Template for Data Submission Appendix F

11 Appendix A Total All s, All Accident Years

12 Total - All Accident Years, All s Appendix A Page 1 Automobile Accident Year Credit Score used in Rating of No Hit, No Score, No Consent 3,58,99 3,289,717, ,49 2,7,819, % 4, % P-P1 518, ,68,181 85,34 511,132,155 1, % 5, % P1-P2 515, ,78,275 79,294 47,183,375 1, % 5, % P2-P3 519,52 519,72,75 75, ,693,627 1,1 14.6% 4, % P3-P4 518,759 51,479,983 72,78 324,521, % 4, % P4-P5 536,45 52,661,46 72,24 291,28, % 4, % P5-P6 57, ,527,897 66, ,93, % 4, % P6-P7 549,59 47,89,284 69,8 259,346, % 3, % P7-P8 51, ,25,469 58,665 27,685, % 3, % P8-P9 521, ,146,952 59, ,337, % 3, % P9-P1 46, ,456,52 45, ,757, % 3, % Grand Total 8,654,932 8,27,529,591 1,17,781 5,11,661, % 4, % 18.% vs. Credit Percentile 7, 16.% 14.% 6, 12.% 1.% 5, 4, 8.% 6.% 4.% y = -.64x R² = , 2, y = x R² = % 1,.% Pure vs. Credit Percentile 1% vs. Credit Percentile 9% 1,2 1, Pure 8% 7% Linear () Linear (Pure ) y = x R² =.923 6% 5% 4% 3% y = -.451x R² = % 1% % with Credit Score % 4, % without Credit Score % 4, %

13 Total - All Accident Years, All s Appendix A Page 2 Automobile All - excl. Quebec Accident Year Credit Score used in Rating of No Hit, No Score, No Consent 2,136,36 2,546,595, ,77 1,577,442,894 1, % 7, % P-P1 244, ,121,913 37, ,128,449 1, % 1,5 1,65 18% P1-P2 244,19 355,976,271 32, ,63,15 1, % 9,186 1,225 84% P2-P3 245,34 344,935,313 3, ,476,358 1, % 8,739 1,74 76% P3-P4 242, ,534,157 28, ,473,466 1, % 8, % P4-P5 252, ,252,577 27, ,5,9 1, % 7, % P5-P6 241, ,875,799 25, ,397,58 1, % 7, % P6-P7 247,125 37,592,582 24, ,34,853 1, % 6, % P7-P8 25, ,476,175 23, ,876,824 1, % 5, % P8-P9 242, ,8,24 21,34 147,739,64 1, % 6, % P9-P1 228,46 238,926,388 17,413 94,241,785 1,48 7.6% 5, % Grand Total 4,576,714 5,715,295, ,362 3,691,23,299 1, % 7, % 18.% vs. Credit Percentile 12, 16.% 14.% 1, 12.% 1.% 8.% 6.% 4.% y = -.73x R² = , 6, 4, y = x R² = % 2,.% Pure vs. Credit Percentile 12% vs. Credit Percentile 1,8 1% 1,6 1,4 Pure 8% Linear () 1,2 1, Linear (Pure ) 6% y = x R² = % 2% y = -.617x R² =.865 % with Credit Score 1, % 7, % without Credit Score 1, % 7, %

14 Total - All Accident Years, All s Appendix A Page 3 (Multiple Items) Property Accident Year Credit Score used in Rating of No Hit, No Score, No Consent 1,476, ,42,167 75, ,832, % 7, % P-P1 225, ,884,46 16, ,544, % 8, % P1-P2 216,83 141,365,49 14,38 117,859, % 8, % P2-P3 223,934 14,861,967 13,895 11,42, % 7, % P3-P4 227,81 14,23,859 13,96 93,917, % 7, % P4-P5 221,29 132,814,883 12,444 86,499, % 6, % P5-P6 222,255 13,185,247 11,615 82,271, % 7, % P6-P7 234, ,139,97 12,181 78,14, % 6, % P7-P8 216,22 121,57,78 1,91 71,152, % 7, % P8-P9 223,44 121,212,413 1,368 65,324, % 6, % P9-P1 198,158 13,84,573 7,763 48,419, % 6, % Grand Total 3,685,41 2,27,22, ,718 1,465,879, % 7, % 8.% vs. Credit Percentile 1, 7.% 6.% 5.% 9, 8, 7, 6, 4.% 3.% 2.% 1.% y = -.33x +.73 R² = , 4, 3, 2, 1, y = x R² =.798.% Pure vs. Credit Percentile 1% vs. Credit Percentile 9% Pure Linear (Pure ) 8% 7% 6% 5% Linear () 3 2 y = x R² = % 3% y = -.444x R² = % 1% % with Credit Score % 7, % without Credit Score % 7, %

15 Appendix B Total All s, By Accident Year

16 Total - All s, By Accident Year Appendix B Page 1 Automobile Accident Year 27 Credit Score used in Rating of No Hit, No Score, No Consent 1,421,938 1,314,881, ,43 827,33, % 4, % P-P1 135, ,95,666 22,183 13,327,143 1, % 5, % P1-P2 134,76 14,981,911 2,528 14,61,816 1, % 5, % P2-P3 136, ,267, 19,335 89,99,699 1,7 14.2% 4, % P3-P4 138, ,747,68 19,35 91,693, % 4, % P4-P5 143, ,933,81 19,24 79,791, % 4, % P5-P6 131, ,863,78 16,679 73,162, % 4, % P6-P7 147,15 127,823,86 18,384 76,928, % 4, % P7-P8 123,276 16,829,283 14,117 55,57, % 3, % P8-P9 144, ,24,825 16,337 63,53, % 3, % P9-P1 124,159 92,97,367 12,185 42,87, % 3, % Grand Total 2,78,868 2,572,634, ,85 1,634,653, % 4, % 18.% vs. Credit Percentile 7, 16.% 14.% 6, 12.% 1.% 8.% 6.% 4.% y = -.63x R² =.977 5, 4, 3, 2, y = x R² = % 1,.% Pure vs. Credit Percentile 1% vs. Credit Percentile 1,2 1, 8 Pure Linear (Pure ) 9% 8% 7% 6% Linear (Loss Ratio) y = x R² = % 4% 3% 2% y = -.383x R² = % % with Credit Score % 4, % without Credit Score % 4, %

17 Total - All s, By Accident Year Appendix B Page 2 Automobile Accident Year 28 Credit Score used in Rating of No Hit, No Score, No Consent 1,14,317 1,66,285, ,9 628,722, % 4, % P-P1 175, ,152,463 29, ,32,15 1,57 17.% 5, % P1-P2 175,13 181,493,1 27,516 13,332,529 1, % 4, % P2-P3 176,64 175,585,883 26, ,6, % 4, % P3-P4 177, ,98,666 25,819 19,257, % 4, % P4-P5 173, ,929,99 23,49 87,668, % 3, % P5-P6 188, ,655,68 25,635 94,351, % 3, % P6-P7 173, ,134,514 22,51 8,758, % 3, % P7-P8 184, ,75,932 22,442 72,149, % 3, % P8-P9 158, ,283,27 18,316 73,98, % 4, % P9-P1 163,79 119,1,97 16,867 48,622, % 2, % Grand Total 2,885,797 2,659,218,14 376,73 1,69,745, % 4, % 18.% vs. Credit Percentile 6, 16.% 14.% 5, 12.% 1.% 4, 8.% 6.% y = -.65x R² = , 2, y = x R² = % 2.% 1,.% Pure vs. Credit Percentile 9% vs. Credit Percentile 1, 8% Pure Linear (Pure ) y = x R² =.971 7% 6% 5% 4% 3% 2% Linear (Loss Ratio) y = -.396x R² = % % with Credit Score % 4, % without Credit Score % 4, %

18 Total - All s, By Accident Year Appendix B Page 3 Automobile Accident Year 29 Credit Score used in Rating of No Hit, No Score, No Consent 945,844 98,55,815 17, ,765, % 5, % P-P1 28, ,432,52 33, ,52,96 1, % 6,685 1,74 99% P1-P2 25,25 215,63,353 31,25 172,789,31 1,5 15.2% 5, % P2-P3 26,627 26,867,868 3,13 152,12,192 1,1 14.5% 5, % P3-P4 22, ,751,78 27, ,57, % 4, % P4-P5 219,694 21,797,66 29,51 123,82, % 4, % P5-P6 187,7 176,8,581 23,84 98,39, % 4, % P6-P7 228, ,851,685 28,96 11,66, % 3, % P7-P8 193,4 168,715,254 22,16 8,29, % 3, % P8-P9 217, ,622,857 24,572 82,33, % 3, % P9-P1 172, ,475,183 16,729 57,328, % 3, % Grand Total 2,988,267 2,795,677,16 376,21 1,767,262, % 4, % 18.% vs. Credit Percentile 8, 16.% 14.% 12.% 1.% 7, 6, 5, 8.% 6.% y = -.63x R² = , 3, y = x R² = % 2, 2.% 1,.% Pure vs. Credit Percentile 12% vs. Credit Percentile 1,2 1% 1, 8 Pure Linear (Pure ) 8% Linear (Loss Ratio) 6 4 y = x R² =.949 6% 4% y = -.541x R² = % % with Credit Score % 4, % without Credit Score % 5, %

19 Total - All s, By Accident Year Appendix B Page 4 (Multiple Items) Property Accident Year 27 Credit Score used in Rating of No Hit, No Score, No Consent 64,94 365,51,933 29,78 28,436, % 7, % P-P1 56,617 35,379,159 3,314 31,975, % 9, % P1-P2 54,146 32,964,429 2,941 26,98, % 8, % P2-P3 56,48 33,347,588 2,969 2,959, % 7, % P3-P4 55,187 31,859,193 2,534 16,828, % 6, % P4-P5 54,692 3,824,57 2,511 16,774, % 6, % P5-P6 58,41 32,328,351 2,637 17,747, % 6, % P6-P7 57,777 31,127,848 2,517 15,816, % 6, % P7-P8 52,615 28,5,416 2,11 14,191, % 6, % P8-P9 53,314 27,742,956 1,982 12,469, % 6, % P9-P1 53,324 26,879,935 1,946 11,52, % 5, % Grand Total 1,157, ,556,377 54,53 392,799, % 7, % 7.% vs. Credit Percentile 12, 6.% 1, 5.% 4.% 8, 3.% 2.% y = -.24x R² = , 4, y = x R² = % 2,.% Pure vs. Credit Percentile 1% vs. Credit Percentile Pure Linear (Pure ) 9% 8% 7% 6% Linear (Loss Ratio) 3 5% 2 1 y = x R² = % 3% 2% y = -.445x R² = % % with Credit Score % 7, % without Credit Score % 7, %

20 Total - All s, By Accident Year Appendix B Page 5 (Multiple Items) Property Accident Year 28 Credit Score used in Rating of No Hit, No Score, No Consent 462,337 37,347,834 27, ,771, % 7, % P-P1 75,62 5,794,657 6,726 52,723, % 7, % P1-P2 73,54 48,4,65 5,982 43,52, % 7, % P2-P3 75,118 47,39,418 5,726 38,984, % 6, % P3-P4 74,131 45,36,35 5,327 34,545, % 6, % P4-P5 76,296 45,538,338 5,325 33,477, % 6, % P5-P6 73,418 42,833,959 4,712 3,47, % 6, % P6-P7 77,444 44,219,143 5,32 3,958, % 6, % P7-P8 68,714 38,359,467 3,927 23,579, % 6, % P8-P9 83,459 44,965,79 4,913 27,885, % 5, % P9-P1 61,775 32,24,481 2,871 16,549, % 5, % Grand Total 1,21, ,487,773 78, ,446, % 6, % 1.% vs. Credit Percentile 9, 9.% 8.% 7.% 6.% 5.% 4.% 3.% y = -.4x +.93 R² =.956 8, 7, 6, 5, 4, 3, y = -21.2x R² = % 2, 1.% 1,.% Pure vs. Credit Percentile 12% vs. Credit Percentile 8 1% 7 Pure 6 5 Linear (Pure ) 8% Linear (Loss Ratio) 4 6% y = x R² = % 2% y = -.483x R² =.934 % with Credit Score % 6, % without Credit Score % 7, %

21 Total - All s, By Accident Year Appendix B Page 6 (Multiple Items) Property Accident Year 29 Credit Score used in Rating of No Hit, No Score, No Consent 49, ,2,4 18, ,624, % 8, % P-P1 94,144 66,71,644 6,338 59,845, % 9, % P1-P2 88,397 6,396,33 5,385 48,258, % 8, % P2-P3 92,336 6,474,961 5,2 41,98, % 7, % P3-P4 97,763 62,84,631 5,235 42,544, % 8, % P4-P5 9,41 56,451,975 4,68 36,247, % 7, % P5-P6 9,426 55,22,937 4,266 34,53, % 7, % P6-P7 99,278 58,792,978 4,632 31,24, % 6, % P7-P8 94,873 55,97,826 4,63 33,382, % 8, % P8-P9 86,631 48,53,667 3,473 24,969, % 7, % P9-P1 83,6 44,9,157 2,946 2,368, % 6, % Grand Total 1,326, ,176,55 64, ,633, % 8, % 8.% vs. Credit Percentile 1, 7.% 6.% 5.% 4.% 9, 8, 7, 6, 5, y = x R² = % 2.% 1.% y = -.32x R² = , 3, 2, 1,.% Pure vs. Credit Percentile 1% vs. Credit Percentile 7 9% Pure Linear (Pure ) 8% 7% 6% Linear (Loss Ratio) 3 5% 2 1 y = x R² = % 3% 2% 1% y = -.413x R² =.8889 % with Credit Score % 8, % without Credit Score % 8, %

22 Appendix C Credit Scores Used/Not Used in Rating

23 Credit Scores Used In Rating Appendix C Page 1 Automobile Accident Year Credit Score used in Rating Y of No Hit, No Score, No Consent 1,371, ,122,71 211,279 43,376, % 2, % P-P1 273,96 188,558,268 47, ,3, % 2, % P1-P2 271,4 182,12,4 46,739 18,12, % 2, % P2-P3 273, ,785,437 45,52 15,217, % 2, % P3-P4 275, ,945,825 44,633 96,48, % 2, % P4-P5 283,51 165,48,884 44,53 93,23, % 2, % P5-P6 265,564 15,652,98 4,873 83,55, % 2, % P6-P7 31, ,216,72 45,373 9,6, % 1, % P7-P8 25,62 131,774,294 35,312 69,88, % 1, % P8-P9 278, ,138,747 37,921 71,598, % 1, % P9-P1 232,183 17,53,132 28,368 54,516, % 1, % Grand Total 4,78,218 2,312,234, ,419 1,32,431, % 2, % 2.% vs. Credit Percentile 3, 18.% 16.% 2,5 14.% 12.% 1.% 8.% 6.% y = -.54x R² = , 1,5 1, y = x R² = % 2.% 5.% Pure vs. Credit Percentile 7% vs. Credit Percentile 5 6% Pure Linear (Pure ) 5% 4% Linear () y = x R² = % 2% y = -.121x R² = % % with Credit Score % 2, % without Credit Score % 2, %

24 Credit Scores Used In Rating Appendix C Page 2 Automobile Accident Year Credit Score used in Rating N of No Hit, No Score, No Consent 2,136,36 2,546,595, ,77 1,577,442,894 1, % 7, % P-P1 244, ,121,913 37, ,128,449 1, % 1,5 1,65 18% P1-P2 244,19 355,976,271 32, ,63,15 1, % 9,186 1,225 84% P2-P3 245,34 344,935,313 3, ,476,358 1, % 8,739 1,74 76% P3-P4 242, ,534,157 28, ,473,466 1, % 8, % P4-P5 252, ,252,577 27, ,5,9 1, % 7, % P5-P6 241, ,875,799 25, ,397,58 1, % 7, % P6-P7 247,125 37,592,582 24, ,34,853 1, % 6, % P7-P8 25, ,476,175 23, ,876,824 1, % 5, % P8-P9 242, ,8,24 21,34 147,739,64 1, % 6, % P9-P1 228,46 238,926,388 17,413 94,241,785 1,48 7.6% 5, % Grand Total 4,576,714 5,715,295, ,362 3,691,23,299 1, % 7, % 18.% vs. Credit Percentile 12, 16.% 14.% 1, 12.% 1.% 8, 8.% 6, 6.% 4.% y = -.73x R² = , y = x R² = % 2,.% Pure vs. Credit Percentile 12% vs. Credit Percentile 1,8 1% 1,6 1,4 Pure 8% Linear () 1,2 1, Linear (Pure ) 6% y = x R² = % 2% y = -.617x R² =.865 % with Credit Score 1, % 7, % without Credit Score 1, % 7, %

25 Appendix D Prince Edward Island

26 Appendix D Prince Edward Island Automobile Swings due to limited data resulted in low R-squared values for the regressions. Credit score data was available for almost 53% of the exposures submitted for analysis. Additional data is available for the Atlantic s in Appendix O. Data was not parsed further into accident years because of limited exposures. Property Limited data prevented meaningful regressions from being performed on the data. Large individual losses skew the results and the analyses experienced low R-squared values. The data was not separated further into accident years because of this. Credit score data was available for 6% of the exposures submitted for analysis.

27 - Prince Edward Island Appendix D Page 2 Automobile PE Accident Year Credit Score used in Rating of No Hit, No Score, No Consent 13,682 9,638,58 1,151 3,11, % 2, % P-P1 1,538 1,231, , % 3, % P1-P2 1,568 1,27, , % 2, % P2-P3 1,495 1,196, , % 3, % P3-P4 1,546 1,217, , % 1, % P4-P5 1,589 1,248, , % 2, % P5-P6 1,493 1,172, , % 1, % P6-P7 1,626 1,262, , % 2, % P7-P8 1,495 1,1, , % 1, % P8-P9 1,626 1,189, , % 1, % P9-P1 1, , , % 1, % Grand Total 28,949 21,466,735 2,649 6,42, % 2, % 14.% vs. Credit Percentile 3,5 12.% 3, 1.% 8.% 6.% y = -.25x R² =.22 2,5 2, 1,5 4.% 1, y = x R² = % 5.% Pure vs. Credit Percentile 5% vs. Credit Percentile Pure 45% 4% 35% Linear () 25 Linear (Pure ) 3% 2 25% 15 1 y = x R² =.451 2% 15% y = -.211x R² = % 5% % with Credit Score % 2, % without Credit Score % 2, %

28 - Prince Edward Island Appendix D Page 3 (Multiple Items) Property PE Accident Year Credit Score used in Rating of No Hit, No Score, No Consent 1,947 1,16, , % 8, % P-P , , % 7, % P1-P , , % 11, % P2-P , , % 9, % P3-P , , % 3, % P4-P , , % 5, % P5-P , , % 7, % P6-P , , % 8, % P7-P , , % 3, % P8-P , , % 36,362 1, % P9-P , , % 5, % Grand Total 4,838 2,66, ,52, % 8, % 9.% vs. Credit Percentile 4, 8.% 7.% 6.% 5.% 35, 3, 25, 4.% 3.% y = -.27x R² = , 15, 2.% 1.% 1, 5, y = x R² =.653.% Pure vs. Credit Percentile 4% vs. Credit Percentile 2, 35% 1,8 1,6 1,4 1,2 1, Pure Linear (Pure ) y = x R² =.147 3% 25% 2% 15% 1% Linear (Loss Ratio) y =.592x R² =.388 5% % with Credit Score % 9, % without Credit Score % 8, %

29 Appendix E Atlantic s (NB, NS, NF, PE) Automobile Combined, the Atlantic s showed a high correlation between claim frequency, pure premium and loss ratio and credit score (R 2 =.9549,.8622,.843). Pure premium ranged from 228 to 53 over the best to worst credit percentiles around an average of 343. Loss ratios ranged from 3% to 58% around an average of 41%. Claim cost showed a less significant correlation (R 2 =.4585), but was affected by swings in the data. Credit score data was available for 49% of the exposures submitted for analysis. Across accident years 27-29, claim frequency consistently showed correlation with credit score. Pure premium and loss ratio regressions varied in their R-squared values, but also showed support for the fact that better credit scores are associated with lower pure premiums and lower loss ratios. Claim cost regressions had R-squared values all under.3. Property Regressions showed support for better credit scores being correlated with lower claim frequency, lower pure premium and lower loss ratios. On a total of accident years the R-squared values were.8716,.6552 and.579 for the regressions of claim frequency, pure premium and loss ratio respectively. Credit score data was available for 52% of exposures submitted for analysis. Accident years 27 and 29 showed large swings in the data. 28 was the smoothest of the individual years with R-squared values of.8472 and.8175 for the regressions of pure premium and loss ratio. In this year, pure premiums ranged from 345 (lowest was 316 in the 7-8 th percentile) to 128 for those with the best to those with the worst credit scores (average of 547). Loss ratios ranged from 6% to 128% (5% in the 7-8 th percentile) for those same credit scores (average of 81%). The data warrants further analysis, but does support better credit scores being correlated with lower claim frequency, lower pure premium and lower loss ratios.

30 Atlantic s (NB, NS, PE, NF) Appendix E Page 2 Automobile Atlantic Accident Year Credit Score used in Rating of No Hit, No Score, No Consent 174, ,977,596 16,51 57,29, % % P-P1 17,397 15,88,399 2,448 9,212, % % P1-P2 17,196 15,415,316 2,112 7,397, % % P2-P3 17,134 15,16,839 1,991 6,71, % % P3-P4 17,164 15,29,931 1,95 7,514, % % P4-P5 17,865 15,333,943 1,87 5,68, % % P5-P6 17,72 14,443,775 1,763 5,359, % % P6-P7 17,731 14,619,724 1,716 4,873, % % P7-P8 17,288 13,948,61 1,542 4,985, % % P8-P9 17,225 13,62,989 1,519 4,827, % % P9-P1 15,683 11,988,237 1,22 3,582, % % Grand Total 345, ,347,361 34, ,28, % % 16.% vs. Credit Percentile % 12.% % 8.% 6.% 4.% y = -.6x R² = y = x R² = % 5.% Pure vs. Credit Percentile 7% vs. Credit Percentile 6 6% 5 4 Pure Linear (Pure ) 5% 4% Linear (Loss Ratio) y = x R² = % 2% y = -.264x R² =.843 1% % with Credit Score % 3, % without Credit Score % 3, %

31 Atlantic s (NB, NS, PE, NF) Appendix E Page 3 Automobile Atlantic Accident Year 27 Credit Score used in Rating of No Hit, No Score, No Consent 67,663 57,835,158 6,378 23,671, % % P-P1 4,455 4,92, ,43, % % P1-P2 4,4 3,983, ,665, % % P2-P3 4,452 3,958, ,77, % % P3-P4 4,436 3,899, ,629, % % P4-P5 4,482 3,898, ,65, % % P5-P6 4,595 3,895, ,446, % % P6-P7 4,41 3,677, ,218, % % P7-P8 4,351 3,527, ,659, % % P8-P9 4,36 3,483, ,79, % % P9-P1 4,238 3,281, , % % Grand Total 111,84 95,533,584 1,77 39,95, % % 14.% vs. Credit Percentile % 1.% 8.% 6.% 4.% y = -.56x R² = y = x R² = % 1 5.% Pure vs. Credit Percentile 7% vs. Credit Percentile 6 6% 5 Pure 5% Linear (Loss Ratio) 4 Linear (Pure ) 4% y = -.229x R² = y = x R² = % 2% 1% % with Credit Score % 3, % without Credit Score % 3, %

32 Atlantic s (NB, NS, PE, NF) Appendix E Page 4 Automobile Atlantic Accident Year 28 Credit Score used in Rating of No Hit, No Score, No Consent 55,925 46,546,544 5,268 18,92, % % P-P1 6,12 5,461, ,257, % % P1-P2 5,943 5,291, ,645, % % P2-P3 5,813 5,93, ,917, % % P3-P4 5,962 5,22, ,36, % % P4-P5 5,98 5,67, ,86, % % P5-P6 6,24 5,59, ,873, % % P6-P7 6,37 5,182, ,695, % % P7-P8 5,842 4,692, ,5, % % P8-P9 5,779 4,544, ,514, % % P9-P1 5,462 4,153, ,293, % % Grand Total 115,47 96,295,839 11,44 38,957, % % 16.% vs. Credit Percentile 6 14.% 12.% 5 1.% 4 8.% 3 6.% 4.% y = -.55x R² = y = x R² = % 1.% Pure vs. Credit Percentile 7% vs. Credit Percentile 6 6% 5 4 Pure Linear (Pure ) 5% 4% Linear (Loss Ratio) y = x R² = % 2% y = -.299x R² = % % with Credit Score % 3, % without Credit Score % 3, %

33 Atlantic s (NB, NS, PE, NF) Appendix E Page 5 Automobile Atlantic Accident Year 29 Credit Score used in Rating of No Hit, No Score, No Consent 5,593 41,595,895 4,855 15,445, % % P-P1 6,93 6,254,287 1,29 3,551, % % P1-P2 6,854 6,139, ,86, % % P2-P3 6,869 6,19, ,22, % % P3-P4 6,766 5,927, ,578, % % P4-P5 7,43 6,367, ,97, % % P5-P6 6,453 5,488, ,39, % % P6-P7 7,15 5,759, ,959, % % P7-P8 7,95 5,728, ,824, % % P8-P9 7,87 5,592, ,232, % % P9-P1 5,984 4,553, ,388, % % Grand Total 119,5 99,517,938 12,458 39,227, % % 16.% vs. Credit Percentile 4 14.% % 1.% % 6.% 4.% y = -.68x R² = y = x R² = % 5.% Pure vs. Credit Percentile 6% vs. Credit Percentile 6 5 Pure 5% 4 Linear (Pure ) 4% Linear (Loss Ratio) y = x R² =.824 3% 2% y = -.255x R² = % % with Credit Score % 3, % without Credit Score % 3, %

34 Atlantic s (NB, NS, PE, NF) Appendix E Page 6 (Multiple Items) Property Atlantic Accident Year Credit Score used in Rating of No Hit, No Score, No Consent 58,718 4,273,723 2,384 27,82, % % P-P1 6,323 4,63, ,242, % % P1-P2 6,268 4,458, ,779, % % P2-P3 6,315 4,383, ,924, % % P3-P4 6,354 4,235, ,222, % % P4-P5 6,497 4,278, ,44, % % P5-P6 6,131 3,988, ,888, % % P6-P7 6,71 4,299, ,513, % % P7-P8 6,194 3,917, ,967, % % P8-P9 6,221 3,753, ,263, % % P9-P1 5,649 3,31, ,496, % % Grand Total 121,38 81,493,591 5,244 59,56, % % 8.% vs. Credit Percentile 16 7.% 14 6.% 5.% % 8 3.% 2.% y = -.31x R² = y = x R² = % 2.% Pure vs. Credit Percentile 16% vs. Credit Percentile 12 14% 1 Pure 12% 8 Linear (Pure ) 1% Linear (Loss Ratio) 6 8% 4 2 y = x R² = % 4% y = -.667x R² =.579 2% % with Credit Score % 11, % without Credit Score % 11, %

35 Atlantic s (NB, NS, PE, NF) Appendix E Page 7 (Multiple Items) Property Atlantic Accident Year 27 Credit Score used in Rating of No Hit, No Score, No Consent 23,5 14,92, ,76, % % P-P1 1, , ,219, % % P1-P2 1, , , % % P2-P3 1, , , % % P3-P4 1, , , % % P4-P5 1, , , % % P5-P6 1,36 799, , % % P6-P7 1,534 93, , % % P7-P8 1,425 86, , % % P8-P9 1, , , % % P9-P1 1, , , % % Grand Total 37,778 23,784,221 1,218 12,87, % % 6.% vs. Credit Percentile 2 5.% % % 1 2.% 1.% y = -.25x R² = y = x R² = % Pure vs. Credit Percentile 14% vs. Credit Percentile 9 12% Pure Linear (Pure ) 1% 8% Linear (Loss Ratio) 4 3 6% 2 1 y = x R² = % 2% y = -.632x R² =.3443 % with Credit Score % 9, % without Credit Score % 1, %

36 Atlantic s (NB, NS, PE, NF) Appendix E Page 8 (Multiple Items) Property Atlantic Accident Year 28 Credit Score used in Rating of No Hit, No Score, No Consent 18,71 13,26, ,183, % % P-P1 2,147 1,555, ,996, % % P1-P2 2,134 1,513, ,879, % % P2-P3 2,13 1,455, ,467, % % P3-P4 2,189 1,442, ,31, % % P4-P5 2,93 1,357, ,227, % % P5-P6 2,255 1,45, ,97, % % P6-P7 2,275 1,43, , % % P7-P8 1,985 1,243, , % % P8-P9 2,3 1,36, , % % P9-P1 1,789 1,36, , % % Grand Total 4,7 27,5,88 1,935 21,898, % % 8.% vs. Credit Percentile 18 7.% 6.% % 4.% 3.% 2.% y = -.27x R² = y = -72.1x R² = % 2.% Pure vs. Credit Percentile 14% vs. Credit Percentile 1 12% Pure Linear (Pure ) 1% 8% Linear (Loss Ratio) y = x R² = % 4% 2% y = -.815x R² =.8175 % with Credit Score 65 5.% 1, % without Credit Score % 11, %

37 Atlantic s (NB, NS, PE, NF) Appendix E Page 9 (Multiple Items) Property Atlantic Accident Year 29 Credit Score used in Rating of No Hit, No Score, No Consent 16,58 12,146, ,542, % % P-P1 2,741 2,91, ,26, % % P1-P2 2,681 1,983, ,41, % % P2-P3 2,75 1,986, ,222, % % P3-P4 2,749 1,917, ,816, % % P4-P5 2,837 1,955, ,33, % % P5-P6 2,57 1,738, ,4, % % P6-P7 2,91 1,938, ,191, % % P7-P8 2,784 1,813, , % % P8-P9 2,576 1,597, ,247, % % P9-P1 2,499 1,489, , % % Grand Total 43,595 3,658,49 2,91 24,799, % % 8.% vs. Credit Percentile 2 7.% 6.% 5.% % 1 3.% 2.% 1.% y = -.37x R² = y = x R² =.26.% Pure vs. Credit Percentile 16% vs. Credit Percentile 12 14% 1 Pure 12% 8 Linear (Pure ) 1% Linear (Loss Ratio) 6 8% 4 6% y = -.56x R² = y = x R² = % 2% % with Credit Score % 11, % without Credit Score % 12, %

38 Appendix F Data Template

39 Data Template Appendix F (Title) CADRI - Call for Data - Research project Step 1 : Univariate Analysis Aggregated Data by, CBIS, & Using Accident Year 27, 28 & 29 Input Required Calculated Field (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) (11) (12) (13) (14) (15) (16) Line of Accident Credit Score Earned Earned Number of Credit Score Business Year used in Rating Incurred 27 No Hit, No Score, No Consent 27 Bankruptcy 27 P-P1 27 P1-P2 27 P2-P3 27 P3-P4 27 P4-P5 27 P5-P6 27 P6-P7 27 P7-P8 27 P8-P9 27 P9-P1 27 Total Distribution Claim frequency cost per claim Pure Loss Ratios Line of Accident Credit Score Earned Earned Number of Credit Score Business Year used in Rating Incurred 28 No Hit, No Score, No Consent 28 Bankruptcy 28 P-P1 28 P1-P2 28 P2-P3 28 P3-P4 28 P4-P5 28 P5-P6 28 P6-P7 28 P7-P8 28 P8-P9 28 P9-P1 28 Total Distribution Claim frequency cost per claim Pure Loss Ratios Line of Accident Credit Score Earned Earned Number of Credit Score Business Year used in Rating Incurred 29 No Hit, No Score, No Consent 29 Bankruptcy 29 P-P1 29 P1-P2 29 P2-P3 29 P3-P4 29 P4-P5 29 P5-P6 29 P6-P7 29 P7-P8 29 P8-P9 29 P9-P1 29 Total Distribution Claim frequency cost per claim Pure Loss Ratios