Best practice of risk modelling in motor insurance : using GLM and Machine Learning approach

Mestrado em Actuarial ScienceO pricing na atividade seguradora está a tornar-se cada vez mais interessante e desafi- ador pelo facto de a dimensão dos dados a analisar estar a crescer de forma explosiva. Torna-se assim urgente para as seguradoras reconsiderar a forma de lidar com este vol- ume de dados. Para implementar modelos sofisticados de pricing para produtos de seguro automóvel, aplicámos técnicas de machine learning, incluindo modelos GLM penalizados e métodos de boosting, que ajudam a identificar as características mais importantes de entre uma grande quantidade de variáveis candidatas. Estes métodos também permitem detetar potenciais interações sem testar as inúmeras combinações bidimensionais. Para um uso eficiente desses métodos, é necessário compreender o objetivo do modelo, as hipóteses que o suportam e dominar as metodologias estatísticas. Embora haja alguma evidência de um maior poder preditivo dos modelos baseados em machine learning quando comparados com os tradicionais GLM, estes últimos beneficiam de uma estrutura, mais conveniente e mais interpretável. O modelo GLM é mais fácil de ex- plicar às partes interessadas o que nos levou a utilizar os GLM na modelação do risco, mas absorvendo os ensinamentos dados pelos modelos de machine learning. A avaliação dos modelos é realizada pela análise dos resíduos quer na fase de treino quer de validação quer ainda de teste. Após a revisão pela equipa, aplicam-se alguns ajustes em cada modelo para reforçar a sua significância e a sua robustez. Espera-se que eles tenham alto poder preditivo nos dados fora da amostra e possam, portanto, ser usados no futuro.Insurance pricing nowadays is getting more and more interesting and challenging due to the fact that the dimension of analysable data is evolutionarily exploding. It is an urgent call for insurers to reconsider how to deal with the data more accurately and precisely. To implement pricing sophistication in motor insurance products, we apply cutting edge machine learning techniques including penalized GLM and boosting methods, which help us identify the important features among massive amount of candidate variables, and detect potential interactions without trying the endless two-way combinations manually. In order to sufficiently make use of these methods, we need to deeply understand the research objective, preliminary assumptions and statistical backgrounds. Although there is some evidence indicating the existence of higher predictive power of machine learning models compared with traditional GLM (Generalized Linear Models), GLM is more convenient and interpretable, especially for multiplicative models. GLM model is easier to be demonstrated to stakeholder, therefore we still achieve our risk models in GLM, but absorbing the insights from our machine learning results. The evaluation of models is done by progression, it is generally performed by residual analysis of the training or validation dataset, and testing errors for the holdout dataset. After peer review, we apply some adjustment in each model, to get models that are significant and robust. They are expected to have high predictive power in the out-of- sample data, thus can be used in the future.info:eu-repo/semantics/publishedVersio

A Yaw Stability Control Algorithm for Four-Wheel Independently Actuated Electric Ground Vehicles considering Control Boundaries

A hierarchical control algorithm of direct yaw moment control for four-wheel independently actuated (FWIA) electric ground vehicles is presented. Sliding mode control is adopted to yield the desired yaw moment in the higher layer of the algorithm due to the possible modeling inaccuracies and parametric uncertainties. The conditional integrator approach is employed to overcome the chattering issue, which enables a smooth transition to a proportional + integral-like controller, with antiwindup, when the system is entering the boundary layer. The lower level of the algorithm is given to allocate the desired yaw moment to four wheels by means of slip ratio distribution and control for a better grasp of control boundaries. Simulation results, obtained with a vehicle dynamics simulator, Carsim, and the Matlab/Simulink, show the effectiveness of the control algorithm

Probabilistic Modeling of Brittle and Quasi-Brittle Fracture: First-Passage and Weakest-Link Analyses

University of Minnesota Ph.D. dissertation. May 2019. Major: Civil Engineering. Advisor: Jia-Liang Le. 1 computer file (PDF); xiii, 164 pages.In this research, two new probabilistic models are proposed for strength distributions of brittle and quasi-brittle structures. The first model is a continuous probabilistic model based on first-passage analysis of random fields, which is referred to as the first-passage model. The model is first derived in a 1-dimensional setting and is applied to the strength statistics of poly-Si MEMS structures. Through the comparison with the experimental data, it is shown that the model is able to yield accurate predictions on strength distributions of MEMS structures of different sizes using the same model parameters. To improve the computational efficiency for predicting the strength distribution of MEMS devices, a renewal weakest-link model is developed. The model takes into account the detailed statistical information of the randomly distributed side-wall defects, which includes the random defect geometry, the random spacing between defects, and the local random material strength. The first-passage model is later generalized to higher dimensions for investigating the power-law behavior of strength distribution of brittle and quasi-brittle materials. It is shown that the power-law behavior of the left tail of structural strength distribution stems from the left power-law tail of material strength distribution, which is also mildly affected by the dimensionality of the analysis and the applied stress field. Flaw statistics (or the random stress field) introduces additional randomness to the structural strength, but does not dictate the power-law form of the tail distribution of structural strength. Lastly, the relationship between the internal length scale of the finite weakest-link model and the material length scales is investigated by analyzing the size effect on the mean structural strength. The mathematical form of this relationship is derived through the dimensional analysis, and the relationship is calibrated by matching the size effect curves yielded by the finite weakest-link model and the stochastic finite element simulations. It is shown that the internal length scale of the finite weakest-link model can be explicitly related to the Irwin characteristic length and the crack band width

Mass Transfer Across The Turbulent Gas-Liquid Interface

Ph.DDOCTOR OF PHILOSOPH

Electroneutral quaternization and sulfosuccination of cornstarch for improving the properties of its low-temperature sizing to viscose yarns

The objective of this work is to evaluate the influences of electroneutral quaternization and sulfosuccination(electroneutral QS) on the adhesion of starch to viscose fibres and sizing properties of starch to viscose yarns at lowtemperature in order to study if the derivatization can improve the serviceability of starch applied for sizing viscose yarns atlow temperature. The quaternized and sulfosuccinylated starch (QSS) with electroneutrality has been synthesized in aqueousdispersion by a quaternization of hydrolyzed cornstarch (HS) with N-(3-chloro-2-hydroxypropyl) trimethylammoniumchloride and a further sulfosuccination is done for introducing a fixed mole ratio of 3-(trimethylammonium chloride)-2-hydroxypropyl (TACHP) substituents to sulfosuccinate (SS) substituents onto starch chains. It is found that electroneutralQS is available to ameliorate the adhesion at different temperatures. The amelioration in the adhesion depends on the levelof the modification. In addition, the modification improves the mechanical properties of the sized viscose yarns,and decreases hairiness of sized yarns, even if the temperature of the electroneutral QSS paste is lowered to 60 °C.Electroneutral QSS with a degree of substitution of 0.0438 is expected to show a potential application in sizing viscoseyarns at 60 °C