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Actuarial Modelling of Claim Counts Risk Classification, Credibility ...

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154 <strong>Actuarial</strong> <strong>Modelling</strong> <strong>of</strong> <strong>Claim</strong> <strong>Counts</strong><br />

Table 3.15 Values <strong>of</strong> the a posteriori corrections obtained from (3.17) for<br />

different combinations <strong>of</strong> observed periods T i and number <strong>of</strong> past claims k • for a<br />

bad driver (expected annual claim frequency equal to 28.40 %) from Portfolio A,<br />

with c = 1.<br />

T i<br />

Number <strong>of</strong> claims k •<br />

0 1 2 3 4 5<br />

1 80.9 % 1482 % 2154 % 282.7 % 350.0 % 417.2 %<br />

2 67.9 % 1244 % 1808 % 237.3 % 293.8 % 350.2 %<br />

3 58.6 % 1072 % 1558 % 204.5 % 253.1 % 301.8 %<br />

4 51.5 % 942 % 1369 % 179.6 % 222.4 % 265.1 %<br />

5 45.9 % 840 % 1221 % 160.2 % 198.3 % 236.4 %<br />

6 41.4 % 758 % 1102 % 144.5 % 178.9 % 213.3 %<br />

7 37.7 % 691 % 1004 % 131.7 % 163.0 % 194.3 %<br />

8 34.7 % 634% 922 % 120.9 % 149.7 % 178.4 %<br />

9 32.0 % 586% 852 % 111.8 % 138.4 % 165.0 %<br />

10 29.8 % 545% 792 % 103.9 % 128.7 % 153.4 %<br />

Table 3.16 Values <strong>of</strong> the a posteriori corrections obtained from (3.17) for different<br />

combinations <strong>of</strong> observed periods T i and number <strong>of</strong> past claims k • for a good driver<br />

(expected annual claim frequency equal to 9.28 %) from Portfolio A, with c = 5.<br />

T i<br />

Number <strong>of</strong> claims k •<br />

0 1 2 3 4 5<br />

1 93.3 % 165.9 % 238.5 % 311.1 % 383.8 % 456.4 %<br />

2 87.4 % 155.4 % 223.4 % 291.4 % 359.4 % 427.4 %<br />

3 82.2 % 146.1 % 210.1 % 274.0 % 338.0 % 401.9 %<br />

4 77.6 % 137.9 % 198.3 % 258.6 % 318.9 % 379.3 %<br />

5 73.5 % 130.6 % 187.7 % 244.9 % 302.0 % 359.1 %<br />

6 69.8 % 124.0 % 178.3 % 232.5 % 286.7 % 340.9 %<br />

7 66.5 % 118.1 % 169.7 % 221.3 % 272.9 % 324.6 %<br />

8 63.4 % 112.7 % 161.9 % 211.2 % 260.4 % 309.7 %<br />

9 60.7 % 107.8 % 154.8 % 201.9 % 249.0 % 296.1 %<br />

10 58.1 % 103.2 % 148.4 % 193.5 % 238.6 % 283.7 %<br />

If we compare the a posteriori corrections for the different types <strong>of</strong> drivers, we see<br />

that the discounts increase with the average annual claim frequency, as was the case with<br />

the quadratic loss function. Also, the penalties appear to decrease with the average annual<br />

claim frequencies. The fact that a priori bad drivers need a greater premium reduction<br />

when no claim is filed to the insurance company thus remains with exponential loss<br />

functions.

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