A simulator of individual claim experience called SynthETIC is described. It is publicly available, open source and fills a gap in the non-life actuarial toolkit. It simulates, for each claim, occurrence, notification, the timing and magnitude of individual partial payments, and closure. Inflation, including (optionally) superimposed inflation, is incorporated in payments. Superimposed inflation may occur over calendar or accident periods. The claim data are summarized by accident and payment "periods" whose duration is an arbitrary choice (e. g. month, quarter, etc.) available to the user. The code is structured as eight modules (occurrence, notification, etc.), any one or more of which may be re-designed according to the user's requirements. The default version is parameterized so as to include a broad (though not numerically precise) resemblance to the major features of experience of a specific (but anonymous) Auto Bodily Injury portfolio. It thus reflects a number of desirable (but complicated) data features , but the general structure is suitable for most lines of business, with some amendment of modules. The structure of the simulator enables the inclusion of a number of important dependencies between the variables related to an individual claim , e. g. dependence of notification delay on claim size, of the size of a partial payment on the sizes of those preceding, etc . The user has full control of the mechanics of the evolution of an individual claim. As a result, the complexity of the data set generated (meaning the level of difficulty of analysis) may be dialled anywhere from extremely simple to extremely complex . The user may generate a collection of data sets that provide a spectrum of complexity, and the collection may be used to present a model under test with a steadily increasing challenge .
Recent years have seen rapid increase in the application of machine learning to insurance loss reserving. They yield most value when applied to large data sets, such as individual claims, or large claim triangles. In short, they are likely to be useful in the analysis of any data set whose volume is sufficient to obscure a naked-eye view of its features. Unfortunately, such large data sets are in short supply in the actuarial literature. Accordingly, one needs to turn to synthetic data. Although the ultimate objective of these methods is application to real data, the use of synthetic data containing features commonly observed in real data is also to be encouraged. While there are a number of claims simulators in existence, each valuable within its own context, the inclusion of a number of desirable (but complicated) data features requires further development. Accordingly, in this paper we review those desirable features, and propose a new simulator of individual claim experience called SynthETIC. Our simulator is publicly available, open source, and fills a gap in the non-life actuarial toolkit. The simulator specifically allows for desirable (but optionally complicated) data features typically occurring in practice, such as variations in rates of settlements and development patterns; as with superimposed inflation, and various discontinuities, and also enables various dependencies between variables . The user has full control of the mechanics of the evolution of an individual claim. As a result, the complexity of the data set generated (meaning the level of difficulty of analysis) may be dialled anywhere from extremely simple to extremely complex .
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A simulator of individual claim experience called SynthETIC is described. It is publicly available, open source and fills a gap in the non-life actuarial toolkit. It simulates, for each claim, occurrence, notification, the timing and magnitude of individual partial payments, and closure. Inflation, including (optionally) superimposed inflation, is incorporated in payments. Superimposed inflation may occur over calendar or accident periods. The claim data are summarized by accident and payment "periods" whose duration is an arbitrary choice (e. g. month, quarter, etc.) available to the user. The code is structured as eight modules (occurrence, notification, etc.), any one or more of which may be re-designed according to the user's requirements. The default version is parameterized so as to include a broad (though not numerically precise) resemblance to the major features of experience of a specific (but anonymous) Auto Bodily Injury portfolio. It thus reflects a
Recent years have seen rapid increase in the application of machine learning to insurance loss reserving. They yield most value when applied to large data sets, such as individual claims, or large claim triangles. In short, they are likely to be useful in the analysis of any data set whose volume is sufficient to obscure a naked-eye view of its features. Unfortunately, such large data sets are in short supply in the actuarial literature. Accordingly, one needs to turn to synthetic data. Although the ultimate objective of these methods is application to real data, the use of synthetic data containing features commonly observed in real data is also to be encouraged. While there are a number of claims simulators in existence, each valuable within its own context, the inclusion of a
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, but the general structure is suitable for most lines of business, with some amendment of modules. The structure of the simulator enables the inclusion of a number of important dependencies between the variables related to an individual claim , e. g. dependence of notification delay on claim size, of the size of a partial payment on the sizes of those preceding, etc
requires further development. Accordingly, in this paper we review those desirable features, and propose a new simulator of individual claim experience called SynthETIC. Our simulator is publicly available, open source, and fills a gap in the non-life actuarial toolkit. The simulator specifically allows for desirable (but optionally complicated) data features typically occurring in practice, such as variations in rates of settlements and development patterns; as with superimposed inflation, and various discontinuities, and also enables various dependencies between variables
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. The user may generate a collection of data sets that provide a spectrum of complexity, and the collection may be used to present a model under test with a steadily increasing challenge