One of the largest items on an insurance company's balance
sheet, often the largest, is the loss reserve, the liability for
future claim costs for which the company is already obligated. It
isn't possible to operate a risk business without a thorough
understanding of its liabilities. Modern insurance businesses rely
on reserve models to:
- establish bottom-line profit for each accounting period;
- price new business effectively;
- report to relevant statutory authorities;
- understand the risk associated with the estimates of liability;
and
- manage the capital commitment to the business of its
owners.
Loss reserving methodology has evolved over 50 years or so, from
models of a strictly heuristic nature in the early years to
properly formulated stochastic models more recently, and has
evolved further in the very recent past into machine learning
models. Loss reserving is often viewed as a necessary evil,
divorced from the excitement of the marketplace, and dull in
nature. Greg Taylor shows you that the statistical processes
underlying it, and the associated modelling challenges, can lead
you down stimulating by-ways.
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