InsurTech Impact Technologies: What Really are They and Where and How Will They be Used? “Big Data
The rise in the use of “Big Data” has been discussed for well over a decade in the insurance industry. Over the last 24 months the emphasis on big data has increased with the rise of InsurTech.
The insurance industry has been using data across the policy life cycle for decades – long before “InsurTech” became a buzzword. This has been true across the entire insurance value chain: marketing; quote; bind; claims; policy administration; and policy renewals. In fact, even more than in its distant relative industry of banking and lending, the pure foundation of insurance rests in the ability to use information (data) to underwrite risk.
Over the years the use of the data has given rise to core industry analytics such as:
Pricing models – e.g. multivariate and predictive models
Customer Acquisition and Valuation – e.g. lifetime value
Fraud Analysis – e.g. behavioral analysis and real-time detection
The InsurTech revolution – or perhaps better viewed as “evolution” – has provided various incremental enhancements for “big data” in key areas of every insurance company.
Status – Accelerating
Many startups have leveraged direct-to-consumer viral marketing through a digital media presence, e.g. use of Facebook, YouTube videos, and banner ads.
This approach combined with web tools such as Google Analytics has improved the targeting capabilities of the ad spend to acquire a customer.
The use of Artificial Intelligence (AI) and Bots (RPA) continues to be in vogue; however, at this point, these are most often just a series of rules and texting user interfaces that only provide a consumer a user experience (UX) that they use every day in their smart phone texting lives.
Status – Developing
Data assets used for pricing a policy have been around for quite some time: credit and D&B scores; MVRs; historical loss reports; building characteristics; roof data; and even telecommunications information (telematics), such as Snapshot® by Progressive and other Usage-Based Insurance (UBI) models.
There are a few startups looking to create new data assets that can be used in pricing and segmentation, many of them around “internet of things” (iOT) and digitizing imagery such as rood condition.
Still early days as carriers have to experiment with these data assets and receive regulatory approval.
This is a key area of partnership with regulators as the industry evolves their Home & auto products and begin to leverage data into various LOBs in Commercial insurance.
Status – Accelerating
Inspections, inspection models, post-bind MVR type data are actively being leveraged in the market today.
The data startups are building out property data capture capabilities via drones and internet-enabled devices to validate submission data to assess the risk.
In auto, telematics continues to be the dominate tool for post bind risk management, taking on many forms including pricing changes, geo-fencing (i.e. use of GPS for location identification), adjustments to coverages and loss prevention.
Status – Developing
Another area that bots are being used to give that UX to the consumer. No real change in claims.
For most startups, the ability to “pay” claims in a matter of moments is classic rules based approach. These claims have been identified by rules a as fast path claim and a CBA model has identified the claim not to be escalated to a adjuster.
Most startups are using classic 3rd party administrators at this point. They have a handful of claims but cannot justify carrying the staffing costs since the claims volumes have not materialized yet.
Key areas of “Big Data” will be in the areas of process automation, data capture (using drones, internet-enabled devices, and auto telematics) and transparency of claim management (to the claimant) to transform the claims experience and decrease loss costs.
The next blog will dive into the applications of Blockchain as a mechanism for current, mid-term, and long-term industry transformation.