Computational intelligence techniques for general insurance

This paper is an attempt to answer the question “What is the proper framework for understanding risk? ” in the context of general insurance. It argues that although actuaries and other risk professionals tend to deal with risk in the context of classical statistics and by resorting to subjective judgment to compensate the inadequacies of this framework, understanding risk is actually an “ecological” problem and it is more fruitful to look at risk in the context of computational intelligence. Computational intelligence (a.k.a. artificial intelligence) is the discipline that deals with designing intelligent agents – agents that are able to learn from data, integrate multiple sources of (often uncertain) knowledge, deal with changes in an environment, make decisions, compete against each other. These are much the same problems that players in the insurance market, whether they be customers, insurers, reinsurers, or the regulator, face daily – and therefore it is quite natural to describe these players as risk agents that have to survive and thrive in an environment (the market). Most actuarial problems, such as pricing, reserving, capital modelling, DFA, pricing optimisation, can be naturally framed as computational intelligence problems. This is not only a theoretical issue: it also allows a technological transfer from the computational intelligence discipline to general insurance, wherever techniques have been developed for problems which are common to both contexts. This has already happened, at least in part, as some computational intelligence techniques such as neural networks, k-means clustering, data mining have found a place in actuarial literature and practice. Others (such as sparsity-based regularisation or dynamic decision networks) are perhaps less familiar to risk professionals, and one of the goals of this paper is indeed to “fill the gaps”, introducing cutting-edge techniques such as the elastic net or spectral clustering to the wider actuarial community. In this paper, computational intelligence techniques are always illustrated through simple insurance applications and are systematically compared to one another to determine their adequacy to our needs: regularisation, neural networks and generalised linear modelling are compared as tools for predictive modelling; fuzzy set theory and Bayesian analysis are compared as tools for dealing with uncertain and soft knowledge; dynamic Bayesian networks and Kalman filtering are compared as tools to understand changes in the environment; and so on. Apart from the methodological findings, some practical recommendations also emerge from this investigation: (i) that the use of regularisation techniques should be expanded in actuarial practice, as these solve the problem of variable selection efficiently; (ii) that cross-validation and the Bayesian Information Criterion should be used for model validation alongside more traditional methods such as the Akaike Information Criterion and the holdout sample method, especially in situations where data is scant; (iii) that Bayesian analysis should be the preferred tool for dealing with data uncertainty, model uncertainty and soft knowledge (rather than fuzzy set theory or rule-based systems), and that Bayesian networks should be used where we have complex chains of dependency; (iv) that dynamic Bayesian networks are a more general alternative to Kalman filtering (applied in the past to reserving and pricing) to deal with environment changes, and can also be extended to incorporate decisions and utility (dynamic decision networks), allowing to deal with problems such as DFA and pricing optimisation; (v) that multiagent systems may be used to simulate markets and design optimal strategies in the face of competition, or to design regulation that achieves some overall goal. In all these techniques, the Bayesian framework is ubiquitous. Computational intelligence techniques are powerful and (unlike artificial intelligence in the 70s or 80s) are now on a more rigorous basis. However, there are some obvious limitations, which boil down to the failing of artificial intelligence to achieve its goals stated in the early manifestos in the 1960s and beyond: that of producing agents which exhibited truly intelligent behaviour. None of the techniques above truly replace human judgment, but rather support it and enhance our ability to justify decisions quantitatively. A, when they are used for prediction they work only when the environment exhibits some sort of stationarity – and the non-stationarity introduced by humans changing the rules of the game is especially difficult to tackle. Finally, some of the techniques – especially those involving multiple agents – are so complex and rich in variables that they often offer only a formal, rather than practical, solution.