1.3 Insurance Company Operations

Armed with insurance data and a method of organizing the data into variable types, the end goal is to use data to make decisions. Of course, we will need to learn more about methods of analyzing and extrapolating data but that is the purpose of the remaining chapters in the text. To motivate this future study, let us think about why we wish to do the analysis. To be concrete, we take the insurer’s viewpoint (not a person).

An insurer, like any organization, needs ways of bringing money in, paying it out, managing costs, and making sure that it has enough money to meet obligations. It is customary to aggregate the detailed insurance processes that we learned about in Section 1.1 into larger so-called “operational” units; many companies use these functional areas to segregate employee activities and areas of responsibilities. Actuaries and other financial analysts work within these units and use data for the following activities:

1. Initiating Insurance. At this stage, the company makes a decision as to whether or not to take on a risk (the underwriting stage) and assign an appropriate premium (or rate). Insurance analytics has its actuarial roots in ratemaking, where analysts seek to determine the right price for the right risk.
2. Renewing Insurance. Many contracts, particularly in general insurance, have relatively short durations such as six months or a year. Although there is an implicit expectation that contracts will be renewed, the insurer has the opportunity to decline coverage and to adjust the premium. Analytics can be used to retain profitable customers at the policy renewal stage.
3. Portfolio Management. Analytics has long been used in (1) detecting and preventing claims fraud, (2) managing claim costs, including identifying the appropriate support for claims handling expenses, as well as (3) understanding excess layers for reinsurance and retention.
4. Loss Reserving. Analytic tools are used to provide management with an appropriate estimate of future obligations and to quantify the uncertainty of the estimates.
5. Solvency and Capital Allocation. Deciding on the requisite amount of capital and ways of allocating capital to alternative investment activities represent other important analytics activities. Companies must understand how much capital is needed so that they will have sufficient flow of cash available to meet their obligations. This is an important question that concerns not only company managers but also customers, company shareholders, regulatory authorities, as well as the public at large. Related to issues of how much capital is the question of how to allocate capital to differing financial projects, typically to maximize an investor’s return. Although this question can arise at several levels, insurance companies are typically concerned with how to allocate capital to different lines of business within a firm and to different subsidiaries of a parent firm.

Although data is a critical component of solvency and capital allocation, other components including an economic framework and financial investments environment are also important. Because of the background needed to address these components, we do not address solvency and capital allocation issues further in this text.

Nonetheless, for all operating functions, we emphasize that analytics in the insurance industry is not an exercise that a small group of analysts can do by themselves. It requires an insurer to make significant investments in their information technology, marketing, underwriting, and actuarial functions. As these areas represent the primary end goals of the analysis of data, additional background on each operational unit is provided in the following subsections.