Boosting insights in insurance tariff plans with tree-based machine learning methods

Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for transparent and interpretable pricing models which are easily explainable to all stakeholders. We therefore focus on machine learning with decision trees: starting from simple regression trees, we work towards more advanced ensembles such as random forests and boosted trees. We show how to choose the optimal tuning parameters for these models in an elaborate cross-validation scheme, we present visualization tools to obtain insights from the resulting models and the economic value of these new modeling approaches is evaluated. Boosted trees outperform the classical GLMs, allowing the insurer to form profitable portfolios and to guard against potential adverse risk selection

Choice of Law Clauses in Consumer Contracts: A Comparative Study of American and E.E.C. Law

The selection of the law applicable to a certain relationship may seem to be the sole purpose of choice of law rules. However, it is questionable whether this choice should be made independent from the content of the various laws available. The selection of the most appropriate law cannot disregard the social, economic and political values that form the basis of substantive rules. In modern legal systems, social values such as consumer protection are recognized to a growing extent. The present work explores the concept of choice of law – namely party autonomy with a focus on consumer contracts in the U.S. and the E.E.C. The present work compares choice of law conflicts in four U.S. states: Illinois, Georgia, California and New York with the 1980 E.E.C. Convention on the Law Applicable to Contractual Obligations. This thesis concludes that choice of law has an important function in preventing the evasion of a large part of consumer protective provisions

Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff

Insurers usually turn to generalized linear models for modelling claim frequency and severity data. Due to their success in other fields, machine learning techniques are gaining popularity within the actuarial toolbox. Our paper contributes to the literature on frequency-severity insurance pricing with machine learning via deep learning structures. We present a benchmark study on four insurance data sets with frequency and severity targets in the presence of multiple types of input features. We compare in detail the performance of: a generalized linear model on binned input data, a gradient-boosted tree model, a feed-forward neural network (FFNN), and the combined actuarial neural network (CANN). Our CANNs combine a baseline prediction established with a GLM and GBM, respectively, with a neural network correction. We explain the data preprocessing steps with specific focus on the multiple types of input features typically present in tabular insurance data sets, such as postal codes, numeric and categorical covariates. Autoencoders are used to embed the categorical variables into the neural network and we explore their potential advantages in a frequency-severity setting. Finally, we construct global surrogate models for the neural nets' frequency and severity models. These surrogates enable the translation of the essential insights captured by the FFNNs or CANNs to GLMs. As such, a technical tariff table results that can easily be deployed in practice

The Updated ICRC Commentary on the Second Geneva Convention: Demystifying the Law of Armed Conflict at Sea

Since their publication in the 1950s and 1980s respectively, the Commentaries on the Geneva Conventions of 1949 and their Additional Protocols of 1977 have become a major reference for the application and interpretation of those treaties. The International Committee of the Red Cross, together with a team of renowned experts, is currently updating these Commentaries in order to document developments and provide up-to-date interpretations of the treaty texts. Following a brief overview of the methodology and process of the update as well as a historical background to the Second Geneva Convention, this article addresses the scope of applicability of the Convention, the type of vessels it protects (in particular hospital ships and coastal rescue craft), and its relationship with other sources of international humanitarian law and international law conferring protection to persons in distress at sea. It also outlines differences and commonalities between the First and the Second Conventions, including how these have been reflected in the updated Commentary on the Second Convention. Finally, the article highlights certain substantive obligations under the Convention and how the updated Commentary addresses some of the interpretive questions they raise

Cyber warfare: applying the principle of distinction in an interconnected space

While the rules of the jus in bello are generally operative in cyberspace, it appears to be problematic to apply the fundamental principle of distinction because of the systemic interconnection of military and civilian infrastructure in the cyber realm. In this regard, the application of the accepted legal definition of military objectives will make various components of the civilian cyber infrastructure a legitimate military objective. In order to avoid serious repercussions for the civilian population that might follow from this inherent interconnectedness, different concepts are analysed that could provide potential solutions for a clearer separation of legitimate military targets and protected civilian installations and networks. The approaches discussed range from the exemption of central cyber infrastructure components that serve important civilian functions, to the creation of ‘digital safe havens’ and possible precautionary obligations regarding the segregation of military and civilian networks. As a solution, the authors propose a dynamic interpretation of the wording ‘damage to civilian objects’ within the principle of proportionality of Article 51(5)(b) of Additional Protocol I, an interpretation that would comprise the degradation of the functionality of systems that serve important civilian functions