This blog is based on a webinar presented by Carpe Data CEO Max Drucker for Carrier Management’s Virtual Insurtech Summit on May 6, 2020. You can register to access a recording of the full presentation here.
How did Auto become automated?
In the early days of Carpe Data, we asked ourselves this question a lot. How is it that an entire line of business shifted from the same manual underwriting process as everything else into a fully automated and transactional experience? As it turns out, the answer lies in the data.
Auto insurance transformed, seemingly overnight, into a fully automated and scalable line of business thanks to three key new data elements: The real-time motor vehicle report, the real-time CLUE report, and the credit score. Individually, these elements provide limited insight into a driver’s risk profile, but together they laid the foundation for complete market transformation. By consuming emerging sources of data and developing new techniques along the way, personal auto insurance was able to far surpass other insurance lines by automating key customer interaction points.
Turning art into science
Underwriting and claims handling have long been thought of as an “art,” but is instinct truly honest, equitable, and objective? One of the largest opportunities for enhanced automation is in underwriting and claims handling. Not in the sense that automation can replace all human expertise, but by providing additional insights to adjusters and underwriters they can make informed decisions based on empirical data instead of instinct, and at a much faster rate.
Efficiency is paramount for claims and underwriting, and as expert adjusters and underwriters begin to age out of the workforce, it’s getting harder to justify the expense of replacing these roles 1-for-1. New data alleviates some of the pressure on these organizations by providing veteran-level insights to every member of the team, and in real-time.
Auto was only the beginning
Of course, there are opportunities to leverage new data sources across other insurance lines as well. For small business insurance, Carpe Data has identified three key areas where new data and techniques are most readily applied:
- Classification – Is it a bar, is it a bowling alley, or is it both? Online data is primed for classification with greater specificity, and businesses that market online are publishing the data themselves.
- Risk Characterization – Instead of manually confirming every detail of a business’s risk profile, underwriters can leverage digital data to instantly understand the broad strokes of business’s activities, allowing them to focus only on the items that warrant manual review.
- Segmentation – How do like businesses in similar markets compare? By aggregating and normalizing data from online sources, powerful predictive models arise based on business and consumer behavior: reputation, customer rating, online visibility, and health & sanitation, maintenance & condition; these rating factors can help predict how likely that business is to succeed, allowing you to select the most desirable risks.
A multi-disciplinary approach to data analytics
New and alternative data sources are naturally unstructured, noisy, and biased– they’re new, after all! As we refine previously unstructured data we also corroborate the information across multiple sources; the more sources, the greater the confidence. Ultimately, that means less time spent manually searching for information online (or even in person), and more time identifying new opportunities for portfolio expansion.