Dishing on Data – Advice from Data Scientists on How to Leverage Traditional and Non-Traditional Data Sources
Insurers are wrapping their analysis in layers of data, including bankruptcy information, judgments and more.
Insurers are utilizing police reports for machine learning and better claims triaging. The biggest challenge is bringing them into conformance.
Insurers need to scrub data for bias, determine accuracy and look for missing elements.
Properly organized data is understandable and archivable.
In life insurance, measuring prospective customers by one dimension alone can lead to highly inaccurate results.
Assessing test information over time can show which results are outliers and which define trends.
Police reports could hold the key to better machine learning and determining claims complexity.
Combing claims data with weather information can help insurers more quickly forecast loss and evaluate risk.
Looking at a full range of information about damaged property can help insurers more quickly move to loss determinations.
The pandemic helped to escalate the share of claims that are filed with photos and police diagrams.