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Case study data for joint modeling of insurance claims and lapsation [PDF]
Data in Brief, 2021 The dataset tracks 40,284 insurance clients over five years, between 2010 and 2015, who subscribed to both automobile and homeowners insurance. We have combined information on these customers.
Montserrat Guillen +3 more
doaj +2 more sources
Ratemaking as Climate Adaptation Governance [PDF]
Frontiers in Climate, 2021 Electric utilities are directly affected by, and in some cases are a source of, many pressing climate adaptation challenges: wildfires, vulnerable infrastructure, extreme storms, and drought. The state Public Utilities Commission (PUC) is one of the most
Jonas J. Monast
doaj +2 more sources
Cyber Insurance Ratemaking: A Graph Mining Approach [PDF]
Risks, 2021 Cyber insurance ratemaking (CIRM) is a procedure used to set rates (or prices) for cyber insurance products provided by insurance companies. Rate estimation is a critical issue for cyber insurance products.
Yeftanus Antonio +2 more
doaj +3 more sources
Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data [PDF]
HeliyonTelematics boxes integrated into vehicles are instrumental in capturing driving data encompassing behavioral and contextual information, including speed, distance travelled by road type, and time of day.
Montserrat Guillen +2 more
doaj +2 more sources
GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS [PDF]
ASTIN Bulletin, 2021 AbstractSpatial data are a rich source of information for actuarial applications: knowledge of a risk’s location could improve an insurance company’s ratemaking, reserving or risk management processes. Relying on historical geolocated loss data is problematic for areas where it is limited or unavailable.
Christopher Blier-Wong +3 more
openaire +3 more sources
RISK FACTORS SELECTION WITH DATA MINING METHODS FOR INSURANCE PREMIUM RATEMAKING [PDF]
Zbornik radova Ekonomskog fakulteta u Rijeci : časopis za ekonomsku teoriju i praksu, 2020 Insurance companies that have adopted the application of data mining methods in their business have become more competitive in the insurance market. Data mining methods provides the insurance industry with numerous advantages: shorter data processing ...
Amela Omerašević, Jasmina Selimović
doaj +1 more source
Multiple Bonus–Malus Scale Models for Insureds of Different Sizes
Risks, 2022 How to consider the a priori risks in experience-rating models has been questioned in the actuarial community for a long time. Classic past-claim-rating models, such as the Buhlmann–Straub credibility model, normalize the past experience of each insured ...
Jean-Philippe Boucher
doaj +1 more source
Machine Learning in Ratemaking, an Application in Commercial Auto Insurance
Risks, 2022 This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy ...
Spencer Matthews, Brian Hartman
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mSHAP: SHAP Values for Two-Part Models
Risks, 2021 Two-part models are important to and used throughout insurance and actuarial science. Since insurance is required for registering a car, obtaining a mortgage, and participating in certain businesses, it is especially important that the models that price ...
Spencer Matthews, Brian Hartman
doaj +1 more source
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