A novel method for testing adverse selection with IoT data
Evidence from China's auto insurance market
Bibliographic Data
| ID | 21466754 |
|---|---|
| Authors | Esther Yanfei Jin (Shanghai Jiao Tong University), Wei Jiang (0000-0002-0811-0648, Shanghai Jiao Tong University, corresponding author), Zhiqiang Zheng (0000-0001-8483-8713, The University of Texas at Dallas) |
| Year | 2026 |
| Volume | 200 |
| Pages | 114576 |
| Publication date | 2026-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Decision Support Systems (JOURNAL) |
| Journal identifiers | ISSN: 0167-9236 • E-ISSN: 1873-5797 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.dss.2025.114576 |
| OpenAlex | W4416416084 |
| Language | EN |
| References cited | 47 |
Adverse selection · Insurance policy · Mixture model · Rare events · Risk management · Telematics · Imbalanced Data Classification Techniques · Insurance and Financial Risk Management · Probability and Risk Models
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Social media meets FinTech platforms
Nonparametric Testing for Asymmetric Information
Asymmetric Information and Learning
Evidence on Adverse Selection
Testing for Evidence of Adverse Selection in the Automobile Insurance Market
The Efficiency Effects of Categorical Discrimination in the Insurance Industry
Testing for Asymmetric Information in Insurance Markets
"The Market for "Lemons
| Highly cited | No |
|---|