How to prepare for renewals and manage data effectively

Avoid uncertainty 

Insurers’ pricing models use loadings for incomplete datasets. Where a data point is unknown, many models assume the worst case. The compound effect of multiple unknown data points can result in high premiums and unsuitable coverage provisions.

Accuracy is king

The completeness of your data is very important in avoiding uncertainty in models. But never sacrifice accuracy for completeness. Basing insurance coverage and pricing on inaccurate data (e.g. incorrect geocoding), risks under or over-insurance, and a potential lack of suitable coverage.

Following this guidance, I asked the audience a second poll question asking whether they knew the data points which made a difference to the insurance price. Unsurprisingly, the majority (78%) were unsure or didn’t know which data points mattered most.

However, there are barriers to overcome in enabling better renewal preparation and data management:

• Data is held in multiple disparate systems and spreadsheets.
• The process of updating and consolidating data into one suitable format ready for submission is often riddled with inefficiencies.
• Ongoing data maintenance is not prioritised, resulting in hugely time-consuming annual processes.
• Investment in systems to support risk management and insurance across organisations has traditionally been insufficient, and legacy systems are not fit for purpose.

When it comes to insurance outcomes, three key areas pose a challenge: 

• Communication is a crucial part of the process from collecting data internally, to submission, negotiation and purchase.
• The use of data as a baseline to define priorities and processes.
• Deciding which data points will make the biggest impact in terms of price and basis for coverage selection is a key aspect of an effective data management framework.

What makes up a good data management framework can be split into two key components: technology and process. 

Technology

When it comes to technology, having a single source of truth to maintain asset and related data in a single place, minimises the need for mass consolidation exercises while a unified data model can standardise the way asset and insurance-related information is captured across the business. Last but not least, as outlined already, data accuracy is king, so take advantage of tooling that can validate your data.

Process

Beyond technology, having a solid process in place that underpins your framework is crucial. To establish this, individuals must be able to access and own their data, aligning stakeholders by prioritising the areas of biggest impact like reducing admin or increasing data completeness. With your data consolidated, the true power of decision-informing analytics can be unlocked.

Collaborate internally & externally

For your insurance data to make a difference to your insurance outcomes it needs to be properly procured and managed and you need to be sure it's accurate. To achieve this level of certainty, you must collaborate internally and externally to be confident that it's complete and accurate.

When it comes to data quality and use, you should be having conversations with your insurers about the types of data that will lead to better outcomes.

Data quality rules

It’s known that the quality of data shared with the insurance market needs to improve. However, not many people know how to improve. One way is to introduce minimum standards and data quality checks, starting with priority areas.

Use available tooling

Explore using tooling to help improve the relevance, completeness and accuracy of your data and to assess overall renewal preparedness. 

For those who may not know where to start, we have created a valuable tool to help you effectively prioritise the collection of your data in an order which will make the biggest impact on your insurance renewal, in terms of pricing and suitable coverage.