Across the insurance industry, point of quote data checks have become increasingly common as a means of ensuring the correct risk assessment is undertaken, safeguarding both the customer and insurance provider.
Data enrichment is an automated process whereby insurers can validate applicant details by cross referencing multiple data sources including eg. claims history, bureau data and possible linked addresses gathered from multiple sources. The data can also be used to enhance the customer experience by auto filling fields in the application process.
Customer expectation dictates that whichever buying route selected, speed is of the essence, and similarly, those insurers selling through price comparison sites must provide quotes in seconds.
Data enrichment is a powerful risk selection and fraud detection tool which when used effectively can shave operating ratios and allow insurers to price keenly, gaining competitive advantage over their peers.
Conversely, those insurers that don’t embrace data enrichment are left with the crumbs from the table – potentially poor risks which they may unwittingly be priced too low and result in costly claims leaking profit It comes as no surprise therefore that as insurers’ appetite for data grows, the supply chain of technology companies and third party data providers servicing their needs has exploded, with new data sources and new entrants to the market witnessed on a regular basis. It could be argued that as the market adopts data enrichment as standard, the ability to differentiate will be lost.
So with the volume of data available, how can insurers prioritise what they access and use the information effectively, to ensure they outperform the competition?
The key is for insurers to call on the services of expert data providers and data processors. Many providers, such as CRIF, have access to a wealth of data sources via a centralised and automated data hub. The services can be called on at the point of quote or the point of sale and data can then be aggregated and analytic models applied to deliver a risk rating or response to assist in pricing. The data and data models used for risk analysis and pricing should be regularly reviewed to ensure risk models are performing as expected.
CRIF risk models and the data used to construct them is transparent and accessible to support audit and subject access requests. CRIF provides bespoke solutions which respond to individual insurer needs at point of quote and sale, with a suite of data products that can be tailored accordingly. CRIF solutions generate responses which help the underwriter make an informed decision with full control.
Insurers using ‘black box’ third party ‘off the shelf’ approaches for risk rating and pricing models however could face difficulties in maintaining a competitive edge. By comparison, they have far less control over how the data is used to reflect their insurance products and risk tolerance, and they lack the flexibility and speed to access and change their risk models.
In the future, might we see a bank of insurers using black box solutions with no ability to differentiate their underwriting strategies?
Whilst they have wisely embraced data enrichment, they have followed the flock and as a result, become one of a number of undistinguishable sheep. To outperform the competition, insurers need the technical ability to process point of quote and sale data and the vision to prioritise the data they wish to mine and how it should be used.
There are a number of potential pitfalls to navigate in adopting data enrichment as part of any underwriting strategy - so what are the Top 5?
1. Blaze your own trail: don’t follow the sheep and do what everyone else is doing – use data enrichment to gain real ‘standout’.
2. Remember speed is of the essence: don’t gather data via solutions that are not quick to react or easy to change.
3. Underestimate at your peril: the complexity of the data and how it should be interpreted.
4. Don’t put all your eggs in one basket: by outsourcing to a single provider via a black box solution.
5. Quality is priceless: don’t buy data just based on price alone – ensure a retro analysis of quality is conducted.
