RMS’ Cihan Biyikoglu highlights the importance of using data in the right way…

Data stop

Logic suggests by increasing the volume of data you increase the potential to make better decisions. However, rather than data volume, it is how data is used, assessed and analysed that leads to better insights. Get this wrong and it has the potential to cause problems.

At RMS, we see insurers that use a separate data and analytics system for each decision-making centre – from underwriting, exposure management and risk analytics to cat modelling and actuarial. And typically, these systems are built by their in-house teams, or have various vendors playing a role, with each system creating and storing its own version of data. Then, with every update, each system creates a distinct version of data that other systems can’t access.

Data inconsistency issues are certainly not specific to insurers or risk analytics. From geocoding accuracy inconsistencies that demolish the wrong building or simple spelling mistakes that cause loss of life, there are many examples of how data quality widely impacts critical outcomes.

So, imagine it’s September 2021, and Category 4 Hurricane Ida is unfolding. The clock is ticking as your teams try to understand losses from the event. As the event unfolds:

  • Teams try to set underwriting moratoriums.
  • Analysts scramble to use the latest event tracks and shapefiles to understand potential losses against portfolios in various business lines.
  • Claims teams try to figure out how to deploy loss adjusters.

Your CEO must report to investors and the board on the losses to shareholders, but the cat modelling and exposure management systems report different loss expectations. These systems have copies of the same portfolios but from different times and separate systems. And with multiple edits across various systems, they contain different exposures and policies – which can cause significant information drift.

 

Meanwhile, the hurricane changes direction from the initial tracks and as it gets closer, the pressure for accuracy is building. But sadly, the data struggles to deliver insights.

We can do better

Data inconsistency issues can quickly snowball into bigger problems. These range from missed opportunities and lost revenue to reputational risk and costly financial mistakes. An IBM study refers to data quality issues as a $3trn problem in the US alone. We can surely do better!

The RMS Intelligent Risk Platform (IRP) resolves issues arising from siloed risk systems as all teams have access to a unified data store and a shared copy of your data: your portfolio, account information, policies, treaties and more. Import a snapshot of new accounts into the RMS Risk Modeler modelling application on the IRP, and the data is immediately available to both the RMS ExposureIQ and RMS UnderwriteIQ applications.

With a shared data model all applications speak the same language. The power of this platform comes alive even more when in-house and third-party applications also use it to store portfolios and accounts of exposures, policies and treaties. Tackling data inconsistency, using a unified data platform with applications that share the same data, will increase the speed, accuracy and flexibility around data analytics when you need it most.

Cihan Biyikoglu is Executive Vice President at RMS