Results 11 to 20 of about 1,040 (169)
ASTIN Bulletin, 2017 AbstractWe start by describing how, in some cases, we can use variance-related premium principles in ratemaking, when the claim numbers and individual claim amounts are independent. We use quasi-likelihood generalized linear models, under the assumption that the variance function is a power function of the mean of the underlying random variable.
Silva, João Andrade e +1 more
openaire +3 more sources
The Future of Road Safety: Challenges and Opportunities. [PDF]
Milbank Q, 2023 Policy Points Traditional approaches to addressing motor vehicle crashes are yielding diminishing returns. A comprehensive strategy known as the Safe Systems approach shows promise in both advancing safety and equity and reducing motor vehicle crashes. In addition, a range of emerging technologies, enabled by artificial intelligence, such as automated ...
Ehsani JP, Michael JP, MacKENZIE EJ.
europepmc +2 more sources
New techniques in railroad ratemaking. [PDF]
, 1968 Recent events suggest that the marketing of railroad services, if some thing less than aggressive, is not wholly devoid of ideas to meet new competitive situations. Several significant departures from traditional ratemaking practices have been proposed, and, having been to a degree accepted by a more enlightened Commission, have stemmed diversion and ...
McCallum, George +2 more
openaire +3 more sources
Ratemaking as Rulemaking: The New Approach at the FPC: Ad Hoc Rulemaking in the Ratemaking Process [PDF]
Duke Law Journal, 1973 The federal Administrative Procedure Act has been on the statute books for a quarter of a century. However, only now are the compromises agreed upon in order to soften the rigors of adjudicatory procedures being fully exploited by the Federal Power ...
Dakin, Melvin G.
core +5 more sources
Precautionary Ratemaking [PDF]
, 2022 For more than one hundred years, states have relied on ratemaking to ensure that electric utilities deliver affordable and reliable power to their customers.
Monast, Jonas J.
core +2 more sources
Machine Learning in P&C Insurance: A Review for Pricing and Reserving
Risks, 2020 In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features.
Christopher Blier-Wong +3 more
doaj +1 more source
Approximation of Zero-Inflated Poisson Credibility Premium via Variational Bayes Approach
Risks, 2022 While both zero-inflation and the unobserved heterogeneity in risks are prevalent issues in modeling insurance claim counts, determination of Bayesian credibility premium of the claim counts with these features are often demanding due to high ...
Minwoo Kim, Himchan Jeong, Dipak Dey
doaj +1 more source
Risk classification with on‐demand insurance
Journal of Risk and Insurance, Volume 90, Issue 4, Page 975-990, December 2023., 2023 Abstract On‐demand insurance is an innovative business model from the InsurTech space, which provides coverage for episodic risks. It makes use of a simple fact in a practical way: People differ in their frequency of exposure as well as the probability of loss.
Alexander Braun +2 more
wiley +1 more source
Ratemaking Model of Usage Based Insurance Based on Driving Behaviors Classification
International Journal of Crowd Science, 2022 Based on the present situation of usage based insurance (UBI) research and application, this paper puts forward the UBI rating model based on driving behavior classification, and applies the technology of data mining to the evaluation of driving behavior.
Zhishuo Liu, Mengjun Hao, Fang Tian
doaj +1 more source
Improving risk classification and ratemaking using mixture‐of‐experts models with random effects
Journal of Risk and Insurance, Volume 90, Issue 3, Page 789-820, September 2023., 2023 Abstract In the underwriting and pricing of nonlife insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. In this paper, we apply a flexible regression model with random effects, called the Mixed Logit‐weighted Reduced Mixture‐of‐Experts, which ...
Spark C. Tseung +4 more
wiley +1 more source