Guelman L.,Royal Bank of Canada
Expert Systems with Applications | Year: 2012
Gradient Boosting (GB) is an iterative algorithm that combines simple parameterized functions with "poor" performance (high prediction error) to produce a highly accurate prediction rule. In contrast to other statistical learning methods usually providing comparable accuracy (e.g.; neural networks and support vector machines), GB gives interpretable results, while requiring little data preprocessing and tuning of the parameters. The method is highly robust to less than clean data and can be applied to classification or regression problems from a variety of response distributions (Gaussian, Bernoulli, Poisson, and Laplace). Complex interactions are modeled simply, missing values in the predictors are managed almost without loss of information, and feature selection is performed as an integral part of the procedure. These properties make GB a good candidate for insurance loss cost modeling. However, to the best of our knowledge, the application of this method to insurance pricing has not been fully documented to date. This paper presents the theory of GB and its application to the problem of predicting auto "at-fault" accident loss cost using data from a major Canadian insurer. The predictive accuracy of the model is compared against the conventional Generalized Linear Model (GLM) approach. © 2011 Elsevier Ltd. All rights reserved.
Guelman L.,Royal Bank of Canada |
Guillen M.,University of Barcelona
Expert Systems with Applications | Year: 2014
Understanding the precise nature of price sensitivities at the individual policyholder level is extremely valuable for insurers. A rate increase has a direct impact on the premium customers are paying, but there is also the indirect impact as a result of the "causal" effect of the rate change on the customer's decision to renew the policy term. A rate increase may impair its intended impact on the overall profitability of the portfolio if it causes a large number of policyholders to lapse their policy and switch to an alternative insurer. The difficulty in measuring price elasticity from most insurance databases is that historical rate changes are reflective of a risk-based pricing exercise. As a result, the specific rate change at which a customer is exposed is a deterministic function of her observed covariates. The nature of the data is thus observational, rather than experimental. In this context, measuring the causal effect of a rate change on the policyholder's lapse outcome requires special modeling considerations. Conventional modeling approaches aimed to directly fit the lapse outcome as a function of the rate change and background covariates are likely to be inappropriate for the problem at hand. In this paper, we propose a causal inference framework to measure price elasticity in the context of Auto Insurance. One of the strengths of our approach is the transparency about the extent to which the database can support causal effects from rate changes. The model also allows us to more reliably estimate price-elasticity functions at the individual policyholder level. As the causal effect of a rate change varies across individuals, making an accurate rate change choice at the individual subject level is essential. The rate at which each subject is exposed could be optimized on the basis of the individual characteristics, for the purpose of maximizing the overall expected profitability of the portfolio. © 2012 Elsevier B.V. All rights reserved.
Royal Bank Of Canada | Date: 2012-05-18
Methods, systems, apparatus, and programming product for creating, maintaining, and otherwise administering new types of fixed income benchmarks and exchange-traded funds. Such benchmarks can be defined and/or occasionally, continually, or periodically redefined by the inclusion of instruments such as new bond issues as they are issued, reweighting of mixes of bond issues used in defining the benchmark(s), and/or removal of bond issues used in such definition, without other changes to the fund(s) and/or benchmark(s). Such benchmark(s) can also be modified through controlled or otherwise selective modification of characteristics used to define the benchmark(s), such as yield to maturity (YTM), maturity date, coupon value, and par value of the aggregated fund(s).
Royal Bank Of Canada | Date: 2015-05-06
Systems, methods, and non-transient machine-interpretable data representing executable instruction sets and/or other products for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of payment transactions and other secure data processes. In various aspects and embodiments the disclosure provides secure means for the authorization of sensitive and other data processes subject to controlled access. Such processes include, for example the creation, administration, authorization, virtualization, storage, and other manipulation or processing of electronic data representing characteristics of, instructions for, and information associated with consumer, business, and other payment accounts, and other forms of secure payment elements, such as payment tokens; and data useful in processing transactions using such accounts and elements. Information associated with particular payment means, such as accounts or payment tokens, can be stored, for example, in a data set, usually secure, sometimes referred to as a virtual or electronic wallet, or a secure payment token.
Royal Bank Of Canada | Date: 2015-10-09
A computer-implemented platform (