Improving home insurance pricing with synthetic geolocation data
公司规模
1,000+
地区
- America
国家
- United States
产品
- Synthetic Geolocation Data
技术栈
- Synthetic Data Generation
- Data Modeling
- Public Databases
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Digital Expertise
- Productivity Improvements
技术
- 分析与建模 - 数据即服务
- 分析与建模 - 预测分析
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 金融与保险
适用功能
- 商业运营
- 质量保证
用例
- 监管合规监控
服务
- 数据科学服务
- 系统集成
关于客户
The customer is a large insurance company operating across the United States, providing home insurance to a diverse range of clients. The company faces the challenge of pricing insurance policies accurately due to the varying climate features and risk profiles of different regions. They are also bound by strict regulations such as CCPA and HIPPA, which prevent them from using personal data like customer addresses in their risk assessment models. This limitation has made it difficult for the company to accurately assess risk and set appropriate pricing for their insurance policies.
挑战
Home insurance pricing was a risky business for our client. The insurance company catered to homes across the United States in areas with vastly different climate features and risk profiles. CCPA and HIPPA forbade the data science team to use the customers’ personal data, such as their addresses, in their modeling, so they could not assess risk and reflect that in their pricing.
解决方案
The insurance company served modeling teams with synthetic geolocation data. The team could use synthetic home addresses to look up five climate features, such as fire and flood hazards, in public databases. The pricing model trained on synthetic data scored as good as the model trained on real data. Using synthetic home addresses eliminated the risk of re-identification and unlocked new insights. The team established a synthetization framework tailored to modeling based on privacy-risk classification and shortened time-to-data from 6 months to 3 days. The process kept 100% utility of the data, perfectly retaining the statistical dispersion of the original and providing an as-good-as real data alternative for training.
运营影响
数量效益
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