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\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ZdU5b5AiLhPbXtkhqn7bI3oELy\/e103WPDbT2ojDzwxnPRZrhnsw30\/a2wJnuSVBLU7KHWNnVASGHE0Je7aeEF3j+QbmTcNUPwz0GQUGodtCrExAu1JsJeXvF2x7kmS5az7lQbe1+wtkExF3o1ZzLw+YlGUaJx2vdy9VSfQ07CMSwU4FEhczHR83jIVhYVe4rlnLYvlQT5pg+z3xPg74uScCzjCewY+TTbcw8PxzRIkfu\/s1czYiXWp4ImvA+8kOqCd4E7UHSV8HMlgTsuJVhcs+SKbDpsySacGGSojOZ6A\/1WuETQ2xiAVasI5h5mHOUJ2ToB4EsvpRWvd7MUU\/u46N6smYEbNq2BN9WzZZR2WIb184LYRszaggKeVM6cfhD3SsIGTGPQPBoU90jSykqceBiImElpSVIeBAJD52IPECCP9RLAbVIPiaG0RWzXstSdS50dEwONjNV\/UUmMFFPMVFPMVGPPNjSopLnkpvWyVJCvICzRIgzhqAtgdzKKX1PJO3WBX94B1NSIX5GUs4UbvV3gpSzOVcV+rFKakw0WP8bSyQtQfTPAJ7wNIsS6KFYds1ubm+62e+qx41FUlghZsKg0bXkb9H5B2nm0Pich9EAzGX+UH+QRTbH3CwnzRlVQVgWb8XKbaDiaQMJ8UEnuJ1GUTb2w9F5yPoBH0gikJBBwhZCadDfEg+\/BR3hLSgIb8nS7BSv8LQymmgTMtgsHMCWVg96gt\/jWQM1YnjCvvJ0eBNeRwuYYJrDe1C2lmp\/FmXFoU5zelumvZVIjIKoz4JTYRsmRwfVKihr3wAy883W5JHSRnPkZb0qQLqaS2GietplkbxZx1G9DllQ4hVmBVh6uSlkkkJwLrdILXuzNXGU9tz6f6caASGjKTfItKIVECvsgMst6QFpSvj9jKfZaehPwcQavgGriM54ZWWcEBb6U0E55KOOfl5QnhH6\/FlLdeqxAKzUC0593vP5BtifjvAMG0460SyjmoR2\/1Q2vOX2QnD4ZDVNHDBOwQJ9Xqe8pf3O50QasdXGqewdnH4DARKDsonTwlYk7D3F857bqZIRSBzAp6SuOaXvCQjleorwkr7lumkau1Owc9Q0sWVriuyqC1JYALc\/jtYLXvdvo\/ryhJq9f3VEhv6SD\/QlNpBVfzURiFiTqQbjW3K0XnJ11iM1QyF6aTXHiVmBGiZ+bn41oWXBf0C\/8EAB5GSJV2BpkMoeJw\/YxBPQFL9H0fTx8Ugqdf6vuIoteAp0qGplzKVgLXnkkn7T4cWWuofQth9afLM5\/BWUgl3IO7X\/0ddLawmGcD2VFoSveGUtHdPt6FNpRPiKH6zTvNZqxGFwknk9r7IrwwQH4szD7FsnJSCO4iLhn05KsiiuCQGfeOOIYUJi35v05I8Y53tlby5LxLyumvEenJRO25qltTnxB5amKZPGbe+2zLlVObdULKPpH5QuxeRytA6JUD90UQ+GOrA2ZbGmOGWRLfaX+KNyZ4orSFEyhYoyPz7N9BRzeEJ5WYE\/4Hd+rKN8VByyzpI0SijvaXHkC3nS0rRqiHcC2WcOJyr7YuBSMQPCX3QEOti1zDljcTZLYImVoEh5e56itTJQfur\/4F5GJnyV6meCvKVK0KaUmmg9puvYEvVjj02t9dKKxAKOyCrHoLYq3wqpl1ZFu8UsnDmpY7igDMGT6bo2VOmH3tgSu3q8iqNMGA86y8KC0FkKU8qDFZEHPPUHliMA2kthbeliHYDaS0lBXNUNIEb1Lfeqtiwlgvyh3kTcE0SmMIpx0fWnuBkT0XMxrdMng4UhTsVgT8WT6cI5Vm2UYmMJXV7sKKHQ2\/J465sONh\/wQcDyAbJgx\/e5PiYPra+7lT3LwEU\/Nvey0t9AyQfq8UFs5KJiuZ9fC3ryQezooiW5sV8LklKQF7GGa7RdGIUNtzMmMXQygVdTvppuQ6yec+Wep4zAJ3toPRYmDiVqbfo6CLY1SNioPBUV0yCLFKpeMaYx8diULDtzvvtDDEUmrDoJJNRHEHOxM5EYSsGmxLYspvxuFXNQgrNo5o1L16w85vonrsNocSoWIq9Ru7PNZYWndNxTnZV7wR7wgGd8p7m4AEfNFVUtIAM2muj0QDkEbEWPwMsfJXlvdFJolkBpUsHa9aWgWcuKZtk\/G3lBoDhe1giUFGO8wPcm59M4W4F4W73pDQ53XbDGMVoXmK9lv+aKGv6hc+JlSfCBrx4fOZnyjH3gK1Qin5M4Adk3e82HbBZkeqEXyT0MLI4MeJCxf+BcgewdWOYu\/OAKFVWrD09ogCifjv1h9oGvelD0yDLJ0dbSb2qlOUwantJDY3chxcAU8NfWHh\/FA9XQesx1kxzgQgIqUSgAOgXkvwpy93lYo4B9F03BZfAf\/XwD4jRJosUlmHMgV1LlzlTSZUk3820lboS1pyrS\/nWRL3ENXkJ3pj+Bfx0twicl2s0Sz891PiJsMDiHtEs\/zXjIE71ghAOoGd9x4TPxHBDsHQ2\/+jnQLNbw2c9BPBZ6PNBw3T68fVv\/3sb9aWMBZ9Dr62e6tBhz6MprjtcxS1N\/zhXP3Q4+6Adik2r44ZkKJ3wlkfmGy911+0TpOA2CmkLBvOz\/qPYQRdMOaBSVruA1tog3SxIeZncsGXFxsWUTGbIqrdo\/oko\/LDbP4TMbQoBFs6zaOhuQcovkhdHwHIyG5\/z4trQangur4S13zrm7BSMKYR6Xhx7DrfjQSnxMWTzzXa1ykp9Cg1N+PCwbnIoGh9yZcrehrIgGrjhVzjo3HIzDn\/opT+Y86cF5QjOpfgYPh2OSMyulDfguR+srTiIJro9QLqC2MtnAh3VWuB6xii+u76z3OOGgWVszjhOejfmU1+3uhfMRL7uAh8HqLtqw6Re8SZ0OlfqE0iLwgzV9fCxFGZDvuuQAgaV\/6GevouVzR01QVngGYFG3swevXbdQbkp5Q4hYUwH4tZ5qQmolbpVnbPVjvNqJ4WlLTx8fTbKPEK6d7tVBvlYgapxNIXVlNcRU\/GDVCn9A8nCjiWOxiHEcjhrHUiDXX5EsehVE\/WoxpGjN9RSqkSK9eFdcF4ODaJQ26r6pVGWG1pK8beqsyGbUyKs1NfBTLwpDocDlI0Xgfk2cfwZXp88\/g6tI9M+gGoRzK2BJO5+F2CSXW4EaFLPY9LDn6zSmIDHc3V5JjcgAjuFOHg+zj9EAsF17a\/iEXqH8OdYidnMhHtXZrWK2xYFdmmNlrGysK6mggkzNlZWBKysDJw9Skhb7vC7lDbPi5oh0KP4dp8rRr90n+9Jw0HRv8YHigD8mXCMzcEINfE0NnEklQt7Y+k3nx9HveGJOGp6YoxqXm6vj0zM6eda+cfWMw8FZ6ZKQPAtRuiSMnrokXLVmkP7UJSFpzeqXcMouxoSlMfcyfEOv8C292j2DPX97kqD1LU3sG5q0zpRiBzTrqnOjqFXSuYWnhXWDx9ZtfdJ1tL5WhzFZKaj3m8e9M3HcO5OuNv2N440rnICntTDc9nGgI+zX+r5SmsM1NLPdnDas6xKRfiYHe\/OT+VD2prPS3nQj7U1npb3pZsPedF5VCy3gWxpLk5P80W+Ajd0AIwp0VPHf28pOJKxK+LamcxV61Rm+lXrVTw1Hdt0gVHXM09HvFanOz\/4\/hqL0xydjyfPJr8Tuc7GJfiHlej8FArrPJE0b\/1ci0lxH9qohII3B\/FCIRxOUz39POhpWwpESjFbb5KKKSolmm9z7THLv0RPufYPWZ\/pNxb3F+0+4d1Zx7+tnuPc1yovBNvj25Df4sZi432C03q\/g1PT5Q33UYG+j59jb6JfsrbjTCpbep3wMrRtmdt5kT01XghE14M6pYtedrj06Ll7sPh3hUemp49PYGbn4Cn7aposTGjt9F1\/DT1uZu04Gf1B6fTKQF\/yO9aTjo5Y+6FyhXf26c4XaPlpP6B\/gWFBYsKuOpo3L1yEJ1cXrxk3puLjavpnar13YjRH2xQjhnrt\/HNt+4R5xRQdwnZdlju\/ikAThyPFdZE8c36VwjcQeySfTVfdtncq3Bcj99fGhfV1UlonKwB3K8eG+83WrC5U2EoRPU6KunAOFEU5neR+aSSqWM8Era4THfGn1ccyymtvWTPmf0r4zc3FQd64KCh+pOVQ8F6J80dkh7drDY7NrD8UEBoXX1NwZAugQ+pUHDQfuyj1s6Ie1O9QzcaK9lrV2TLyiQau6hRBRw46OYzsqsHJOJ07kdmbYoyN4mGNGz1vnba\/lCUH7eCVWxz9cVNx3IhdVPm75ijJ7SKNyfQzVEeBHTtevuzfWC3xzbq047q8ynlqXHA+4dyMc0k7F8xkPgtRKMjzwR36WWmGGJ1OLZTiBy+IZ9gI2ja0+x+lsagUZnrIJv06iH9YHjvn9jAXnLMnGcAEVarqAm3JWmuE4uwivwd\/tH45D3+MfptZbntubvuAhdbp4Dx\/gl9iEi+u4a+DuAd47wAcGfnmATQNSDdw1IN3AewcGPjAgy8Am35OLbkAdgISCkGxU4ABagsXU+TXQhDra6ZQnvsfgvuT5LIlijndOh5CiYe02mmXjndPUZ3jn7SwYAgxLM5EC2ecq75PHWegzzVWX2hxHY0GgYe00CHZinvhBqrnY0bwVXM68S6LY91iw4628AK7jQRa\/F5Un2fh+BrctXVfddjvg+5VY6esZLpd8p7zyZHbM3RmC2z21FW7Yw+OsIFZDWH9Bm2bO0FXW9+CEzqvbOJBuuOXCMirCc1Vvs2oAGguqBgJoAM58nQAqOoES0FwAzUkZRjlNFe6XJ0bP3NVNOQq+jPXOHCHrIhz6oZ+talEn9FKC3PHTN5DL9Qz1shNq8v2e1uerKBzsmDt+uGMa2DAMzdLEm9aujm4zOMnVQj7niZa\/447msazDY82l62k0qDsa1kY7I8We0TPiwS\/OSASInpMQDyFf7CM9IxzhVf1dzDrCUT3tIQqV9ez8KZEOEPaogRkeCxSDKxM7DmwGeD13mNu49zZ0mLv7Etlem0Jevr0E\/GmZfG\/Xy6Wgv6XRp42dlY3BFee5YAfMbc3I6+6NtHXdgJNEONK1dNgRaATGDkrZLdzuuKAZ4XMxzEvqOC52lAdkoxV\/qK8c5v6PidaXjuFKFsCEQCLSu5BuVunqFpREYnFqiN\/UOdADNeyH4679UNDad\/Shd0FCbl2Q0MOfqQ6vQ3gdemj3nSg4poY9Pn5nj4tCH+ml8+A6tZuFNzpqQVoR50LuzO9Uouaj2waIDjlALf2hJw4HDhF+Bdnh6JlsdVHkRkeYc9HLe+uCeHhBOSe+0dbNjnhQ4Qhg1t9izknGfOX1dQ+QidGGv2YFtQCoJEYtnbxsk8PWjY4k\/BeAn+Nv8BPjjFMDT8CDDr9\/uio+IZzyagl8V3O\/dRLlbOkP4jolkgdT4MojnypPSEG64TKIWk+d76hlmibpYsYVqpjbeSV4ID8RV48Y79C9Q2FdYvy4o9LaIk35vTAuK2npKW\/DwhXqKKe6z1s+bw94a8DRrn7fukfS4+Pkrw3v0SWnX+ro+6ZQLgjSQh4BL7ny92uUDLjcYEtuZ7xNA25PlLsnw0tZMHKYe\/wJrd878OQCUJ7fKvflifUgIrV8F5FaXuGEDax7fGEtcMCm1md8aWUcD2NrAlb7N7LQe9R0LbmTa\/bv+jYIwcgU8uPbgjSHXM3VnX7rhBwECtDc4JFcggfn37JyRyVh+RuwKQboPM\/\/3vCIvuB4XhHlOUhunQuwtealnPS3wNE\/dM2CwJpx\/ak\/tjh9R9hbeY38uxLgjkxgFeUI8\/ufgpg5kq7dY75BmVacJUCYIi478mSp92UHtqXz+23JtSBfn2r4r9bMHcIwA\/iCU9MeROuQt6lpX\/AWHYNpTMaVuuAnf1d6Cu+Y5TlEDLMX8+O+HZdey3ccDm4F1cKvimeO8Bm0c80VU5aU7I5LUnbG2\/S2TsWg9RYQQuSSy7wq8EoVuBYFAKC9pRQXpSJOvJXnxNylZ9yOOOH34uVavLAgUFnta7lFXsuoaAV95sCUI2lHVOfacRJlUbaKOUQs+7QIr0ECS7IV8VgQ6BEHCytav+bOA3epmINaERECqYAECLSxTu\/w3+VS+btzl9d3zxsus9WWec2dOxcmrbQPhoWVtmPW75mXybt\/o9oUOhe8Zhi65Y22SyueahT7+j\/Q3N9IMlSP03XEY2udYyZ+KqQV51Kj5rGUOnIaweEUKGYeJxGPnSF3aUimLN6yWZw70eyQu\/gOritCGfZMmarvzl154VG\/5fqQ4zuEq9acv0G+E9XVDrZMONiqqm8ecFXpcNAFoc3KBp5k4UZKx3TlhRZ1Hia2tCE2sAHb1cgx387NCvoouVcR0E0yMXVl1DGwAUf55cYo6ee4uqVx64zlJY2ATVtTcmlfiWXfpkv7ijtTMumBIG8JQd+F1AIp8mLrJ3mv1R\/qb5yx65y6aM2dU7etKlSJduooMcc5dZ0914WKN4GU4vd123i3MuqCF30teJEQHHN5uKXQ0+ilutjh2s+hpBoGMJHXCo0qCQOqoMMwX69\/ti+BedxJ3vFBsUbfOsUhm3KrhgfDFdyylgK2yHBUT+mCl9HIj0JrsoFCzJex9RX6xVhgAdJxPA0sX38N2iFY1ZTH0gcVWO2npIQwFnTu4K9a9xJx8\/JcRyIQmiEJCyf0tG3m6mrKFi5WM9Hftbpotyu5GdyX1X39H1hhuAuxLG54PQk6jvDXRlr3wFAC33dOnfXE0hKeaeoulXbDwcvVj0INM8vAfesH9iwI3ZNjAA3MEvKSrXiyYwLcD9y3brgA3CsAuxuAXQC84bhvfZWQ+woyi+IS9LQfzflONuY7cRKNEjadcij2FYoVah8UHu9peSXOvAcC+J4ffy+R+76kgCNOv3PnPXfxtyYXbC7Ub4K1yeXYKomNUcUqE5mXeAQXrzsjThjKR5zwgH7j9oiTmFFIPDF6VwrNAsYyRSanpVYqKqgD9WsKLXT4M6dAZ+Cf1CJe8J9xtfOfrcK7zt95favvy63+mYP+9oLXef+LAnctB\/yeyCEmR5j8teeCxCwjY\/7gm2E5t+mG4gwzU6iW+ta+1Lf8oQ4E5YR+5s4D0LWMPrTNPP8husOzwpiXWhkWmrQ1x6EV4IV1joWubK0wbFgrwtwLUusHx3xu3eJb6x\/szZI5Ty2Pi817JX5urFTAp9YHDFuMD6w5xwGsxtT6zoGPnmbWLcdj9mBlZMwewEzOE+EKs0XVHzgzktRi1u1b8A4mxZnU5R8fpU0HtP4+S\/308VGLeKypMClDcDqB956vZ+TWCVw8R1ZGRD\/0AM\/BFtAx8TktFoQo6FEJXC53iW6vwaHk0UZ1mchhQA47K+lgenyO1ud0bEdUZHTdPM\/Ar4au76w5jq0Ai+5aQxxEaWqtAK\/RCTV6oKY7kSs9onM89\/kitdY8buAHz2X7ARWV4iEN5PDxioJ1Bcx9V2ypl9tKgM156gxdIRGGpRQlKHZG5Bw5XZf0WyYxu4peRaCMntcVGY8atlcJPl6BCkZHjicYwpgGBOaM2VGpVDFFbgQMSBAWg6mBueuJCGryHTh0DwKRWSIQWZylVq3nDnPx0DLxgKVja9wThwIW+0PVgxfWuAdYszS4FaTlUmyQ\/S0G+KTbZ7QcuwfXqYsOn5EJnvAVxNdb8cTS2pCwMqwzwiDCQElczoC2nJG+BUTyjHhqnGdE\/OblVV6YFTBn62vBQjUeQ4fnWHlPqzPigvTt7RuVgNfdN\/C8RQ67EMdwajldA5v7eN\/A+4cuzvws4JZ2GaXpDl96nA\/gTGlH4Gxn4WdjQdoHPPVHYZ3CLy0RPDAaaXhgOV1hfcWZ701SeNu05dZMtHhoaUlcZ14iKhRP\/GiAd6Rql+OVtR5YjoFXRaUwfvGkG3iFDxAeWkbJraI0xTuz0M+gKETr4qkV4T4LB6l1jqcsmQD9kIwzhr4H5K4o\/MSmCHkGyl08FmdZqpw3S4qCKysgsOmAq4nAfIm1Tw5yFzx5QPzYRojkNgvqG0HacuOGLVdSnRjOKyIqSc6cxC5eIXxOC7IzJzFeITso1hqssiS2tPagjHK3QjgKrRUQPnKnKPM6icWcPRnwChsIp3NrhdMpyN6aNxZroA5j7q7wHsqx0Gey2uWZCJuicKSUnHreuco7z4U038gTgwO9E8YmwepJOUj+Wwrw+014kZLnzZ2SwYW+YqvE06BcbtdJ1Ie8nSlb+tPZdAcmcodlO2nGwgFLBjtJfUGmuFj6cnVhLwpm01CsCYn27QtZyEkAALyjgPjkSb9Ij2s4nE3lRQUFx2pwp6NRwkcs2wImjhoU2Jk6Z3gCBIcOCuazOHWoIFycRIvUArqC+7MkrK1VufhmQK9huTIWAKF\/ejDQIINBqbVWZDBw7Vl9aRYEcEgmxUoMYGqHksThdGENiafWXkkXh0AXq9kfEoZNuKSVRY11OSR9mS7nx9KYEEebIKwOkuOYyeZB9Ev0IYkZsrTTYMFWqQbrSuZAOuieMQfoZr9EKQ4nD7tMFKllxxxBWW6Z\/K8c86C5iocQVq6LZDM8yHE6ttZ9loi33Tn2BC68dL5RZncO2+9nqxyms5z38yV4\/vDBDgvDGQvkKu+vdsRc7ETDZ4j6xuoOmvpAuahjVq3VLGPemKdPV2HMa70Zs1mabQHiwZYuCybwFDYdl7C3Y5ZwGMbp6aWGAX0iNB1I1cojWi30maDJQhDdLv48oceT5tlayc+1RGsPVfsToM8pHxWsATbl3BJ75kaeuAFr8EylLcF+rGebMrurCZYRbCgDKxzVwp9AK3CQMm+rZ9Ml846+qmWsqgxUWWphffRZkpZEEJAgRQZJAvDQ6sLjO8s8xCMWW2YXhIN9vL+Pu\/uVFmXuGbgRGYfsHSLkYrb000u1COY8YSNeX2wFOy7kiy0gsB6lXr\/DwoE8UAWhSykAz1ImqRfA2BnbpE9Vrw1cQJaBzBvEStWySayEsgK0yocAwET2ryBboaQbgFChnQAACyeKdBXQOebLJ5t+GSsmK55zofJswDBWUQawQQhZYgMmngaK+YnnHPcL6sGYJB8iUO8m\/YCqf0E+BNKrbe15s+ksYPLyDGTtSITBpLGnc\/mEdFSk6DvwqHK\/L8v0v1kw28YIWUUSxO5+Qldq3FydDhtPofolzC\/pQ44nsZ\/+VGoDiWtI56WKVMKel1t1i6Z4XtcUz9VhRkRrulLhnLhl6NtwPAeU1SZWkBRoqYswbDdLE5uuoqBRxq2g0pOUxNA8QRecHoSuLoRs3RF+Clsg+D3uIkv7NNySJ0xUwGGHUbLDKueIcsrEFG3MmaQB+vXVJXo6spUOZjBY6rVxqQFVmNYu4YJJmu2Io2qoV43c0m5nU+AP8v3PdEcdZpc96h5s9Kh78KsegRFue4800VIcJdkwCvxoZxEl2XjHFE4LRYtPjWs7rOSdsrFEvyqWTkQYKlqSKpFqiWUN\/EcN2UYcxIRcDha4C45m2VZKqqxVYK8qCCh4gcyPM\/i2B9DJmZMRMNk4c7AqSztnZS1Ya0ncARdt7VkFCmvprG\/xWLN0ZVuo5q0mCdfFXRiIlMchhF+aNWVx\/BN1tDpZWVHDXh0f2Ctx68bRvGgWZhbX2iuXVl2cOSsXlLyhAz6tnWiuuTQgfN7T4S8Y\/GtOO9ZT3x7oqVx0IWfJzjMogP0kxtPM4nNy2RRaNbv0\/MJRKJ3oNwQW8EaDqETgjSviNcF1PJHgg\/+hcBlVzE1FIIccZEO0lkDd5JWxLa9YMkl1mZPpQoUZljd86008iJFCI8H2plaF44ZTb9K1AyJuBOkr8ArAK3ADwHukuKEViEB08j4BBqn5GdFs\/mxYC7BpUbCAuH8iPCwG5yVRmhIeV9R5hdZDuiq\/BCAlka3qb1tTlgfJFkbNg7OocPIrbUniQa5q14nEVW+v5kXFqGmz4\/OGuQ9cYI7F8YyIRyWtf1V86xWqHL\/OHQZ2NcNFaFevp4q0rXC2R9UbBNkQoKbbKZNQa2w3HLWcCMTQCglefUFiGNGGea1IalrY3BwpwnQGd1pAO0t5JvZo0tSHRTCLACTqZy\/dNT\/fBWReLrXa5fMVNXBU2VrL41Q4Ta2+LHVeCHpeZV4tkTZwzus4HCIZy+U4QuuIevaKnud5bWuskBARpizeKiHICMryZ7gRInYFEWJg31RDoNc6IqWTZel+5pElGHTq2oi0GAM5ZjVFZE7AUVZn8NkVui58HKRrbOHaIN8qiijPToSx\/MnZ55noUOFOqgsgODXp44ykIvI4iarHe2QPnRJmIu5PCj9aT\/j\/pvqZDBbdvKPhFfbdGUkzloFPwENB71iPOZ5bRDGUElztHoaH2dPT9n1yUNNOXtY+1uMJKXhDQ0At8yVS7j+iRFFcFGHkQYdP\/CgaoUIoWKaBhad\/OeUeuPCh9UpXdxLOKGuE54p0sTMQWnu1kE5jGYFfF5\/GGIv4\/T1ivLSIcYhsCagPhfBVi07lNQIXba1i78Ai+wcAWkUzOoOCPLv0Q\/6awZHtfusM77XO3KpG2RbaBHSFf16kg6L67AC6tQEcWcR4WRsAv6+HqZK+hJJC8rmKF48vRNywoCf+NjhMTzEPmeMPkNUxrY6JLyE4XZT1xN9nSkBOWULNwzDWUBGV57buqfKJGvan4xsyLG2un4Sf+K0Dac4n16XyoW0WB\/FvqPPch1A2PRRhq9Z8FC\/kWQz013mAFX6hYkKIA0M6dt4UAtaD67rq0o1w4HqofzBQ+i5Cog2FPp4Qs9uDz61YH0+IcdiDz6+Ix+5BD45YLfgyi5vnXiOA17se6cKHTupT+7mYvwBO9jZW3efm0mqRv6pFJOA3GjCrAdzITn+njCxX+o0QAW5ABIA2+nzkh8r13iMs8fTvYC35DszohiRs0IIb3crgcH0BH817ZjuIr+aQg81N0DJJd3N97z3dCNv2QJ7jZoxRtefhRlqkKwUZNdjR2X+8qG\/+40UtruWXBovqA3FG5ZtzQW\/L4zgbFp590enIabgE7y8Xf6KMXOuXYjIuxWTgN\/Rcv8QM4Qd6SXxK6Rl+p55uts7UJ5ipTzBTbzZnaAvBUF83At\/YA0vNk1huzQl76HVJ13rXk+E3zI3ZfuiNCfM8awyhJJuzJ8KnageGARFR35yYZs80LRMkOREkdSyCo250TVZSRewsA3V6G4E6p\/5gEHBNFRexK9WXhC7FPCBcIqNNDgQDuDg+7AH7sh7kzzu0ZtLArV9KixEUab9p78ti+3gtwoVaMhgrjhPf6hqdi7b+0DswLDAW+Q8cjHIyEKz10Ds0DAtO0WR4TbEDcrFs4SrftmVbX7XFWjmjhn32czvZWUHFbio72ZmLb+mYXDc2NL6g5\/oNHgsE6F7nVkTsLx7aOuvcCtmzeDim+kV7D7XE3\/IDaAN\/Ks1PGnzF8Ib4OPPhIEmI6zeiA22N7GjtG2V5AwndcQoDUiXD3igzWynHwg2MrYaUehlpdgPQwjKBd7Kxnwqz1w6TRyz1AtIG57pS8L0prH4qbOwf1e0LcQIvN+FYCgtCoMGf6JzAbSMdXOQx+MNj81B+yuLTsfG0+BulZjmfXPxA3xw3veV7hQ72Rp7543fU0T5yPxvzRBpilJVVw6UBCBQXeL\/lfjr1vZ0+D7LNbGGUTesArqMuQACrLCS4h97GBL6pT99Dc8pA8t2Zb85bRhYwtG5z4t6xB5YMNPyuwHRdxQCVUGgZotk8V94Oec2P97rySf7Ic\/Edh+oi7cbt9XBbjJJ0qKHHR03DI5pluhYueWcUdNJhR2tP4OrdO\/hClbgLPZLXZbfU0BESr4a1IQuCvvhOr11p2n\/0\/9uCzc8P\/ze1yNuNG58xXvhJcXFSffCoT8Q9Gn2EP6pLLomU567lT3EfVdqV6mGt+kS4nkD8brRhZJoV8pewNFV69gKtfVFKX1TK4H05japGZ+HqV9iXygS+hxvkKEf6zJm7G\/fgBYoJWHcbXHtBZWKtFr\/q4j017PvjRdHF+4IYfqHhLwwSk9i3\/mwvnHsXMND+U0TIgY9Aw7ASLsLBfhHrfhL7r6LBSgdYhNeCCxWhfGB+RR1pzBL10apvv2xbAG9t\/Vut9W+idQFbawOvvVliVe9nsyRHMsC8SITdVxNJfRLNMh1GG1pa2SAuQRvRwIblNIAp8sk0QOIz0wDEePH7TuULfI+Kvt2LzjT6sdJlXX0ivcXQunhqNJ8DqoYJT8f6QljZxPVyTboFySByIMEMpdLXJ1F4NmbhSFRXPEOFYhZFYG95ZRysXT6JQl34Vox4w9ixKlPh\/E6CSRJXAyu6X5gHZYvyGVrEC5yIVn9\/hI2xbHS26IcINrC1G+9krNzysdGJZ2oTJrqttZ1BjsIiPFa1lYXZM\/g4LZBxWsdErmYIDEWQDb8iN\/wPsSTI3HowS8Abvb5RS+zJY53w+QgoGlKfVrMboRzkvpiyuHhMWCrDwVchCv8oD4NzCCCQ0PPeue7jCI9wH18hqyjUG2YiuXgXixBZs6wAFhZNkahiB1SBqyFgAInDEa0jN4EUfSGD0AJ2r6Cf6kEgMkH5r9hOwtlgpRWxDaBWiYbfZ1d5PZaFvIoKERLkJzMStPb0WojCRMQNISpsAtzu\/r3mfBD21QWEcREroIzzKJsdS7G+znk\/ha\/5FKJcFzM0lhFpQBgO2dwfsSxKiArA4EehYIUiODl8jagstaVGOJwd81BvWkRrgLdvbrPZwI\/QekuiHuJQJkC0lDqlyQvrzJPF6hQ4AXbfETHNwHLzbOwzgT0eA9gFGL6rr\/0tnpFGoGINL4p7jj+tGmDrUdW+UMP+cnxf8OQvwJPvnS\/utmg+ccLTFL6hKwC2ynWyL3CtcdGTUT4sTcy69nsb\/wmB8wmf+pmuyTGKq8fWoh4a+ZOiV0Bhovg6YBAPoHzWUX5Bt3qwV3eVFmhTRLltiCj+UL+nlF6AOet+K2JUUD6wXmW8jF0soJ\/E3WgUkLwLeD7E8gk78BFhpQ5ftM3yJioYGp7Bd8ozJUt\/Axv3F5LGAeDL1uCLqMk588bVWl+qQILweQleQFIZKzDgjWC7IgXuGsE3tcOBhtbfpJ08ECHibF\/Y8cULNqRtWo66nuGnH9lHXVlfRHUI9cSv1UhU5RVn+dLk28XUwBVcO+PH34rJycprDRP+EykuDfwBT6j2Z\/ubk3G3EN9EN0UKnvDCQjThG2gWsyXUqilbaghZxVhB\/CvU+kZ35QedfjJj8FlhTVwYft97vtfMy6gmQN0ybRiJYbyXQyiD76S8l27td7FfEfhhy82oPmmUchlkVn2AasB\/1mHxMQi5SAZqATFOB7z68LMFuSp60oCXhjTGES5fGW+bCGujmQ8EolgrTyIuFfnqvvIzCEqHCkEJn4ujysUvQUM4UleCO0Td90XsPfl1igtxbXZ7hHvIq4hFT\/sU81Dox3wJMvIOKAmapX2E6ktqJcgAXsOPdaG+GDrgsE3rpOsNkC5JdRbbcA+xgzSEt6hHg8wpIWBJ3Mslgb\/RL70vBIaqAsDd+v3AD0flWhEqS+bHb6JEX2BlSvhSH7CyKCjLwLd6Vi41l5tegY8To1hQ0lj\/S87TaYb0fEcN+91xeXb2Dvbzg\/NuSzDPz1vrVnJgzEIeqCh9H6n+jMmhI0msUHQlyUQFCdwBYukHWT3M1aL6WFmOmofWZdamqvhneyEmIkfkR+SHuoZrg\/1O4fuhn4vBfles5Y\/Pznf3eQEXO43uu1pb\/9iD7z9\/hK9ZIbQWxbcLYcsq2Pez8uSPtArHFf7GAXJtxWYyBq2I+ZNmvc2ETaED1\/eji2GI6VCt8XJ9\/nFf\/x7GPeijzwoZaH2p3\/9UDKkbdp6rSwTLLewQz1VXRtT9AvxQkJJekzAUG+FS1yRhqOxBn\/SLdr0gnD1UXXuj34NueqlEAA3Zn3RD0r9Xzy76EnHl6npLDfvt8atidb2F1fXKebu5NJIoALG8P8uyCNraApKxviDsGtaMAuIn8Y031MYFWCxgrOfC\/i9F+zJxR0PrxdPo42\/0RTOwsVAyn2m5H8ySpkdDLTyf0kAzNuFsIUXB4pk+jahQAUrLgiz8I41CKAi\/WwtBRlVAqQkXMN5UqiNF0Dy0Bq+BbTkNYvIz+1hGhfXHT6tawlHOIAzeOomi7IolI7AW7RlGvNwRny9DZSS+UPFZFc+TKXbPOW2qP0rkFoH3QOuzq2FdzTL2dEibqTrnVasDvGbFckqle2j5\/kZQWctRDA4aBE+XGh6noNBe8YHPZGC\/J8m6pscJH\/Ik7Yjjmk7qiYCBO3CEjjS0jYgpyxDnNYUZaKJU5YqIRldNX5T6hyzfWD4e8KHVxxD40xrhy4tX1kcOn9yET61Z97wR+qseRUx+5d6KMZ\/2+cAa4Fk8YFkjvl5dfsSjWrznBVqDfgOfARnwRvKlSJ6l\/Kre5QXcbFzYhcIuP5b8RGsXyyfPG\/rtaz5M6TtuNxIv\/T79uJFWLR1hzcaTTQVaWM03rN9TcBUUBSaVQD+iRj084Egs92S19jJdhAYUwHK6+mgNSf+BbUMeS\/w\/<\/script><script 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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-nm8khtoa.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"console\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>A-P11 \u00b7 TOPIC \u00b7 loss-distributions<\/strong>GRA dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GRA<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Actuarial and Quantitative Risk<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 Actuarial and Quantitative Risk<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Loss distributions<\/h1><p class=\"nx-tagline\">A loss distribution describes how often losses occur and how large they are, for one policy, a portfolio or a whole line of business. Actuaries combine frequency and severity models into aggregate loss distributions that drive premiums, reserves, reinsurance and capital. Choices about data, distribution family and the tail change results substantially. This page sets out how loss distributions are built, where they mislead, the data behind them and who signs off.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Dossier<\/strong><\/span><span class=\"nx-eyebrow\">A-P11 \u00b7 topic<\/span><\/div><ol class=\"nx-register-rows\"><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-register-n\">01<\/span><span class=\"nx-register-k\">In brief<\/span><span class=\"nx-register-v\">Definition and scope<\/span><\/a><\/li><li><a href=\"#nx-sec-question\"><span class=\"nx-register-n\">02<\/span><span class=\"nx-register-k\">The question<\/span><span class=\"nx-register-v\">4 mechanisms<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-register-n\">03<\/span><span class=\"nx-register-k\">Key terms<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-field\"><span class=\"nx-register-n\">04<\/span><span class=\"nx-register-k\">Field<\/span><span class=\"nx-register-v\">H3 field plate<\/span><\/a><\/li><li><a href=\"#nx-sec-indicators\"><span class=\"nx-register-n\">05<\/span><span class=\"nx-register-k\">Indicators<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">6<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-pipeline\"><span class=\"nx-register-n\">06<\/span><span class=\"nx-register-k\">Pipeline<\/span><span class=\"nx-register-v\">Run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-sources\"><span class=\"nx-register-n\">07<\/span><span class=\"nx-register-k\">Sources<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">5<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-decisions\"><span class=\"nx-register-n\">08<\/span><span class=\"nx-register-k\">Decision holders<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-methods\"><span class=\"nx-register-n\">09<\/span><span class=\"nx-register-k\">Methods<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GRA \u00b7 In development \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>Exposure, hazard and the exceedance\u2026<\/a><a href=\"#nx-sec-question\"><span>03<\/span>Loss distributions: how the risk\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>05<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>06<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>07<\/span>Where the evidence comes from<\/a><a href=\"#nx-sec-decisions\"><span>08<\/span>Who decides, and what the evidence\u2026<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Loss distributions are statistical descriptions of how often losses occur and how large they are, combined into aggregate distributions for pricing, reserving, reinsurance and capital. Data adjustment, distribution choice and tail treatment decide results. Pricing and reserving actuaries, chief actuaries, reinsurance buyers and supervisors hold the decisions.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Spatial finance<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Exposure, hazard and the exceedance curve.<\/h2><p class=\"nx-lede\">Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><figure class=\"nx-fk nx-sf\" data-fk=\"cat-ep\" data-gl=\"sf:cat-ep\" data-sf=\"cat-ep\" data-sx-credit=\"Natural Earth (public domain) | design event set, not a model output\" data-sx-src=\"Natural Earth ~ public domain ~ https:\/\/www.naturalearthdata.com\/ | Design scenario values by the Global Risks Alliance ~ illustrative, not data\" data-sf-learn-views=\"ep\" aria-label=\"Interactive map: coastal exposure under cyclone basins and earthquake belts, linked to a loss exceedance curve. Choosing a return period on the curve marks the places that lose at it.\" 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\"><figcaption class=\"nx-fk-cap\"><span>Coastal exposure under cyclone and earthquake zones, beside the loss curve it produces.<\/span><span data-honesty=\"true\">Illustrative design values, not a model output or a price.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/loss-distributions\/#map\" data-nxf-title=\"Actuarial and Quantitative Risk live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">The question<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Loss distributions: how the risk develops.<\/h2><p class=\"nx-lede\">Frequency, severity and aggregate loss, and how tails are estimated. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 3v18h18\"\/><path d=\"M18 17V9\"\/><path d=\"M13 17V5\"\/><path d=\"M8 17v-3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Frequency and severity separate<\/h3><p class=\"nx-p nx-mt-1\">Claim counts and claim sizes are modelled separately and combined. Each depends on exposure, risk factors and trends that must be projected to the period being priced.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">The tail drives capital<\/h3><p class=\"nx-p nx-mt-1\">Most claims are small, but capital and reinsurance depend on rare large losses. A distribution that fits the body well can understate the tail badly.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M5 22h14\"\/><path d=\"M5 2h14\"\/><path d=\"M17 22v-4.172a2 2 0 0 0-.586-1.414L12 12l-4.414 4.414A2 2 0 0 0 7 17.828V22\"\/><path d=\"M7 2v4.172a2 2 0 0 0 .586 1.414L12 12l4.414-4.414A2 2 0 0 0 17 6.172V2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Data must be brought to today<\/h3><p class=\"nx-p nx-mt-1\">Past losses must be adjusted for inflation, exposure growth, policy changes and claims practice. Errors in trending carry straight into prices.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Truncation and censoring distort<\/h3><p class=\"nx-p nx-mt-1\">Deductibles hide small losses and policy limits cap large ones. Fitting without accounting for this biases both frequency and severity.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Frequency distribution<\/td><td data-label=\"Definition\">A probability distribution of the number of claims in a period.<\/td><\/tr><tr><td data-label=\"Term\">Severity distribution<\/td><td data-label=\"Definition\">A probability distribution of the size of individual claims, given that a claim occurs.<\/td><\/tr><tr><td data-label=\"Term\">Censoring<\/td><td data-label=\"Definition\">Partial observation of losses, such as when policy limits cap the recorded amount.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Indicators<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Claim frequency<\/td><td data-label=\"Unit\">claims per unit of exposure<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Claims and exposure records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Mean claim severity<\/td><td data-label=\"Unit\">currency per claim<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Claims records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Coefficient of variation of aggregate loss<\/td><td data-label=\"Unit\">dimensionless<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Fitted distributions<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Large-loss share of total<\/td><td data-label=\"Unit\">percent of incurred losses<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Claims records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Severity trend<\/td><td data-label=\"Unit\">annual change in mean severity, percent<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Claims analysis<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Goodness-of-fit statistics<\/td><td data-label=\"Unit\">test statistic by distribution<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Model fitting<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/loss-distributions\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Loss distributions as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-a-p11-topics-loss-distributions-reserving-t nx-dag-a-p11-topics-loss-distributions-reserving-d\" data-nx-z=\"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\"><title id=\"nx-dag-a-p11-topics-loss-distributions-reserving-t\">Loss distributions: claims reserving<\/title><desc id=\"nx-dag-a-p11-topics-loss-distributions-reserving-d\">Claims data are built into development triangles; chain ladder and Bornhuetter-Ferguson give IBNR; a selection is made with a risk margin, tested by actual versus expected, and signed off. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Sources<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">Casualty Actuarial Society monographs and study notes<\/td><td data-label=\"What it gives\">Methods for loss distributions and ratemaking<\/td><td data-label=\"Licence class\">Free to read, CAS terms<\/td><\/tr><tr><td data-label=\"Source\">Insurance supervisory statistical returns<\/td><td data-label=\"What it gives\">Aggregate claims and premiums by line<\/td><td data-label=\"Licence class\">Public records, terms per supervisor<\/td><\/tr><tr><td data-label=\"Source\">R actuar and fitdistrplus packages<\/td><td data-label=\"What it gives\">Open-source tools for fitting loss distributions<\/td><td data-label=\"Licence class\">Open source, GPL<\/td><\/tr><tr><td data-label=\"Source\">Society of Actuaries research and data<\/td><td data-label=\"What it gives\">Experience studies and methods<\/td><td data-label=\"Licence class\">Free to read, SOA terms<\/td><\/tr><tr><td data-label=\"Source\">OECD Global Insurance Statistics<\/td><td data-label=\"What it gives\">Market claims and premium aggregates<\/td><td data-label=\"Licence class\">Free, OECD terms<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/loss-distributions\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Decisions<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Actuarial and Quantitative Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Pricing and reserving actuaries<\/td><td data-label=\"The question\">Which distributions and trends to use<\/td><td data-label=\"Evidence this page serves\">Data, fit diagnostics and professional standards<\/td><\/tr><tr><td data-label=\"Who decides\">Chief actuaries and risk committees<\/td><td data-label=\"The question\">Whether loss models are fit for pricing and capital<\/td><td data-label=\"Evidence this page serves\">Validation and sensitivity evidence<\/td><\/tr><tr><td data-label=\"Who decides\">Reinsurance buyers<\/td><td data-label=\"The question\">How much tail protection to buy<\/td><td data-label=\"Evidence this page serves\">Aggregate loss distributions with uncertainty<\/td><\/tr><tr><td data-label=\"Who decides\">Supervisors<\/td><td data-label=\"The question\">Whether loss modelling supports reserves and capital<\/td><td data-label=\"Evidence this page serves\">Actuarial reports and reviews<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Guide to frequency, severity and aggregate loss models<\/li><li>Method note on trending and adjusting historical losses<\/li><li>Brief on fitting with deductibles and limits<\/li><li>Questions for reviewers of loss distribution assumptions<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Core questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">The questions experts ask.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>How often do losses occur, and is the frequency stable over time?<\/li><li>How large are individual losses, and how heavy is the tail of their distribution?<\/li><li>How do frequency and severity combine into the aggregate loss for a portfolio?<\/li><li>How sensitive are tail estimates to the choice of distribution and to a few large observations?<\/li><li>How should uncertainty in the parameters themselves be represented?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Mechanisms<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Frequency, severity and aggregation.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">The classical collective risk model separates claim frequency, often modelled with Poisson or negative binomial distributions, from claim severity, modelled with distributions such as lognormal, gamma, Pareto or Weibull. Aggregate loss is the sum of a random number of random severities, computed through recursive methods, fast Fourier transforms or simulation. For catastrophe risk, event-based models simulate many possible years of events and their losses to build the aggregate distribution.<\/p><p class=\"nx-p\">The tail is where choices matter most. Heavy-tailed severity distributions, common in property catastrophe, liability, cyber and operational risk, mean that the largest losses dominate the total, and that sample averages can be unstable. Small changes in the assumed tail shape can change estimated capital needs substantially. Parameter uncertainty, the fact that distribution parameters are themselves estimated from limited data, adds a further layer that is often neglected.<\/p><p class=\"nx-p\">Inflation, changes in exposure and changes in the legal or physical environment mean that historical losses must be adjusted before they can inform the future, and each adjustment carries its own assumptions.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and data<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Methods and their limits.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"3\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Fitting<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Distribution fitting<\/h3><p class=\"nx-p nx-mt-1\">Maximum likelihood and Bayesian fitting, with goodness-of-fit tests that are weak in the tail.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><rect width=\"20\" height=\"8\" x=\"2\" y=\"2\" rx=\"2\" ry=\"2\"\/><rect width=\"20\" height=\"8\" x=\"2\" y=\"14\" rx=\"2\" ry=\"2\"\/><line x1=\"6\" x2=\"6.01\" y1=\"6\" y2=\"6\"\/><line x1=\"6\" x2=\"6.01\" y1=\"18\" y2=\"18\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Simulation<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Monte Carlo<\/h3><p class=\"nx-p nx-mt-1\">Simulating aggregate losses; tail precision needs many simulations and careful convergence checks.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Uncertainty<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Parameter uncertainty<\/h3><p class=\"nx-p nx-mt-1\">Bayesian and bootstrap methods to carry parameter uncertainty into results.<\/p><\/div><\/div><p class=\"nx-note nx-mt-3\">Limits: the tail is estimated from few observations, so different defensible models can give very different answers. Results should be reported with their sensitivity to model choice.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/actuarial-math\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Actuarial mathematics<\/span><span class=\"nx-mledger-role\">Reserving with development triangles \u00b7 Premium build-up \u00b7 Annuity valuation<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/actuarial-math\/\" aria-label=\"Actuarial mathematics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Generalised linear models in pricing \u00b7 Bayesian credibility \u00b7 Goodness-of-fit testing<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/extreme-value\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Extreme value theory and tail risk<\/span><span class=\"nx-mledger-role\">Excess layer pricing \u00b7 Tail capital measures \u00b7 Non-stationary hazard extremes<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/extreme-value\/\" aria-label=\"Extreme value theory and tail risk: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/computing\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">High-performance, cloud and edge computing<\/span><span class=\"nx-mledger-role\">Monte Carlo simulation \u00b7 Version control and reproducibility \u00b7 End-user computing controls<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/computing\/\" aria-label=\"High-performance, cloud and edge computing: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Loss distributions depend on data that are incomplete, changing and often sparse in the tail. Different distribution choices can fit observed data equally well yet differ widely in the tail. Trends are uncertain and may change. Actuarial and Quantitative Risk explains methods and their limits; it does not fit models for any portfolio or give actuarial opinions.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-14-sec\" class=\"nx-section\" data-hue=\"finance\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What is an aggregate loss distribution?<\/summary><div class=\"nx-answer\">The distribution of total losses over a period, combining how many claims occur with how large each claim is.<\/div><\/details><details><summary>Why model frequency and severity separately?<\/summary><div class=\"nx-answer\">Because they respond to different drivers and trends, and separate models make assumptions clearer and easier to test.<\/div><\/details><details><summary>What does trending mean in pricing?<\/summary><div class=\"nx-answer\">Adjusting past losses to the conditions of the period being priced, for inflation, exposure changes and other shifts.<\/div><\/details><details><summary>Which distribution is best?<\/summary><div class=\"nx-answer\">None in general. The choice depends on the risk, the data and the purpose, and sensitivity to the choice should be reported.<\/div><\/details><details><summary>Does GRA publish loss estimates for markets?<\/summary><div class=\"nx-answer\">Not as pricing or capital guidance. Method notes may use public data to illustrate techniques, with limits stated.<\/div><\/details><details><summary>Why are tails so uncertain?<\/summary><div class=\"nx-answer\">Because extreme losses are rare by definition, so few observations inform the shape of the tail.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-15\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-15\" class=\"nx-h3 nx-mt-2\">Around Actuarial and Quantitative Risk.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/extreme-value-methods\/\">Extreme value methods <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/climate-adjusted-pricing\/\">Climate-adjusted pricing questions <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/credibility-and-sparse-data\/\">Credibility and sparse data <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/dependence-and-correlation\/\">Dependence and correlation <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/capital-and-solvency-models\/\">Capital and solvency models <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/model-validation-quant\/\">Quantitative model validation <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/\">Actuarial and Quantitative Risk home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/\">The Global Risks Alliance home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/trust\/\">GRA governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Actuarial and Quantitative Risk<\/span><h2 id=\"nx-close-16\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"mailto:contact@globalriskalliance.com?subject=Actuarial%20and%20Quantitative%20Risk%3A%20Loss%20distributions\">Write to the team <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/loss-distributions\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/loss-distributions\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is an aggregate loss distribution?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The distribution of total losses over a period, combining how many claims occur with how large each claim is.\"}},{\"@type\":\"Question\",\"name\":\"Why model frequency and severity separately?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Because they respond to different drivers and trends, and separate models make assumptions clearer and easier to test.\"}},{\"@type\":\"Question\",\"name\":\"What does trending mean in pricing?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Adjusting past losses to the conditions of the period being priced, for inflation, exposure changes and other shifts.\"}},{\"@type\":\"Question\",\"name\":\"Which distribution is best?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"None in general. 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