7,074 research outputs found
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
Learning to Race through Coordinate Descent Bayesian Optimisation
In the automation of many kinds of processes, the observable outcome can
often be described as the combined effect of an entire sequence of actions, or
controls, applied throughout its execution. In these cases, strategies to
optimise control policies for individual stages of the process might not be
applicable, and instead the whole policy might have to be optimised at once. On
the other hand, the cost to evaluate the policy's performance might also be
high, being desirable that a solution can be found with as few interactions as
possible with the real system. We consider the problem of optimising control
policies to allow a robot to complete a given race track within a minimum
amount of time. We assume that the robot has no prior information about the
track or its own dynamical model, just an initial valid driving example.
Localisation is only applied to monitor the robot and to provide an indication
of its position along the track's centre axis. We propose a method for finding
a policy that minimises the time per lap while keeping the vehicle on the track
using a Bayesian optimisation (BO) approach over a reproducing kernel Hilbert
space. We apply an algorithm to search more efficiently over high-dimensional
policy-parameter spaces with BO, by iterating over each dimension individually,
in a sequential coordinate descent-like scheme. Experiments demonstrate the
performance of the algorithm against other methods in a simulated car racing
environment.Comment: Accepted as conference paper for the 2018 IEEE International
Conference on Robotics and Automation (ICRA
Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis research examines how artificial intelligence may contribute to better understanding
and overcoming over-indebtedness in contexts of high poverty risk. This study uses a field
database of 1,654 over-indebted households to identify distinguishable clusters and to
predict its risk factors. First, unsupervised machine learning generated three overindebtedness
clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected
households (31.33%). These served as basis for a better understanding on the complex
issue that is over-indebtedness. Second, a predictive model was developed to serve as a
tool for policymakers and advisory services by streamlining the classification of overindebtedness
profiles. On building such model, an AutoML approach was leveraged
achieving performant results (92.1% accuracy score). Furthermore, within the AutoML
framework, two techniques were employed, leading to a deeper discussion on the benefits
and inner workings of such strategy. Ultimately, this research looks to contribute on three
fronts: theoretical, by unfolding previously unexplored characteristics on the concept of
over-indebtedness; methodological, by proposing AutoML as a powerful research tool
accessible to investigators on many backgrounds; and social, by building real-world
applications that aim at mitigating over-indebtedness and, consequently, poverty risk
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