6 citations · 9 across the 6 of their papers we have counts for
7 papers
A Portfolio-Anchored Frequency-Severity Risk Index for Trip and Driver Assessment Using Telematics Signals
Jongtaek Lee, Andrei Badescu, X. Sheldon Lin
In this paper, we propose a novel frequency-severity joint trip-level risk index that combines the frequency of abnormal driving patterns with a severity component reflecting how e…
Static marginal expected shortfall: Systemic risk measurement under dependence uncertainty
Jinghui Chen, Edward Furman, X. Sheldon Lin
Measuring the contribution of a bank or an insurance company to overall systemic risk is a key concern, particularly in the aftermath of the 2007--2009 financial crisis and the 202…
A Population Sampling Framework for Claim Reserving in General Insurance
Sebastian Calcetero Vanegas, Andrei L. Badescu, X. Sheldon Lin
Claim reserving in insurance has been studied through two primary frameworks: the macro-level approach, which estimates reserves at an aggregate level (e.g., Chain-Ladder), and the…
Assessing Driving Risk Through Unsupervised Detection of Anomalies in Telematics Time Series Data
Ian Weng Chan, Andrei L. Badescu, X. Sheldon Lin
Vehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics (e.g., harsh braking counts) and do not fully ex…
Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method
Sebastian Calcetero-Vanegas, Andrei L. Badescu, X. Sheldon Lin
Claim reserving primarily relies on macro-level models, with the Chain-Ladder method being the most widely adopted. These methods were heuristically developed without minimal stati…
Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour
Ian Weng Chan, Spark C. Tseung, Andrei L. Badescu +1
With the advancement in technology, telematics data which capture vehicle movements information are becoming available to more insurers. As these data capture the actual driving be…
A Posteriori Risk Classification and Ratemaking with Random Effects in the Mixture-of-Experts Model
Spark C. Tseung, Ian Weng Chan, Tsz Chai Fung +2
A well-designed framework for risk classification and ratemaking in automobile insurance is key to insurers' profitability and risk management, while also ensuring that policyholde…