4 ways insurers are becoming more competitive by increasing data confidence

4 ways insurers are becoming more competitive by increasing data confidence

4 ways insurers are becoming more competitive by increasing data confidence

Most insurers recognise the importance of data, but too many lack the confidence to incorporate it into their company culture and business critical processes, where it would have the biggest impact.

Here are 4 ways leading insurers are increasing their data confidence to become more competitive:

1. Automating data standardisation

Seeing the whole picture is crucial to have confidence in your insights, but inconsistent data sources stand in the way of insurers building a harmonised, granular view of their premiums, claims and exposure.

Automating the standardisation of data allows you to bring any amount of it into your single source of truth as soon as you receive it, regardless of inconsistencies. You can even deploy machine learning to benefit from mapping suggestions whenever you receive unpredicted inconsistencies, creating a reliable near straight-through process.

2. Reducing manual checks

A major lack of confidence stems from insurers analysing datasets that have been through hundreds of manual transformation steps. These are commonly riddled with inconsistencies and require several manual ‘sanity checks’, which creates unnecessary doubt, consumes valuable resources and exposes the business to needless risk.

An automated validation layer enables insurers to automatically track whether their incoming and outgoing data meets their business standards. This can be as simple as checking whether data is in the correct format or more complex checks against policy limitations and risk appetite measures. This considerably reduces time spent questioning and correcting unexpected or erroneous data.

We have seen a remarkable improvement in data confidence after implementing data pipelines with automated validations. Being able to trust their data meant that Aegon Blue Square Re was able to use their insights to set critical pricing assumptions.

3. Reducing ‘key person’ risk

Your team knows your data and business best, but how much of that knowledge resides in the minds of key individuals? What if they were unavailable?

To minimise concern about the absence of key people, insurers are anchoring their data processing in highly automated, collaborative systems. Not only does this assist team members by speeding up the process, but it also acts as a safety net, which a new team member or partner could use without dependence on key individuals.

4. Always including a human in the loop

It’s not only about having automated processes, but building a robust and efficient user experience around them to enrich existing business workflows. Over-optimised automated solutions can be just as devastating as human errors, so for mission critical applications it’s still necessary to have a human in the loop.

In fact, most team members welcome the change that automation and machine learning brings, freeing them up to do value-creating work, such as value adding data analysis and business decision-making.

Request a demo

How it works

Learn more about Quantemplate

Machine Learning

Harness the power of machine learning to automate data

See the demo

We'd love to show you more in a 30 minute demo