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Monte Carlo 2022

Could data inconsistency be catastrophic?

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Satellite image of a tropical storm - hurricane or cyclone or typhoon. Elements of this image furnished by NASA.

Not if you unify systems to stop problems from snowballing, says RMS product expert Cihan Biyikoglu

Let’s go back a year to September 2021. Category 4 Hurricane Ida is unfolding. The clock ticks as your business attempts to understand potential event losses. Teams try to set underwriting moratoriums. Risk analysts scramble to find and implement the latest event tracks and shapefiles to understand portfolio losses for various business lines.

And your claims teams are figuring out loss adjuster allocation. Your CEO is also expected to report on losses to shareholders and the board.

But both the cat modelling and exposure management systems report concerningly different loss expectations. Both systems use copies of the same portfolios – copied at different times between various systems, with different exposures and policies. And with multiple edits across systems, they have experienced significant information drift.

Meanwhile, the hurricane gets closer, changing direction from the initial tracks, and the pressure for accuracy is high.

To help resolve the issues of siloed risk systems, the RMS Intelligent Risk Platform (IRP) unites all teams – from exposure managers to underwriters and treaty managers – with a unified data store containing a shared copy of your data: your portfolio, account information, policies, treaties and more.

Importing a new account snapshot into RMS Risk Modeler enables data to be immediately available via our ExposureIQ and UnderwriteIQ applications to get answers to your questions. The shared data model enables these applications to speak the same language, which can be extended to other in-house and third-party applications to store portfolios and accounts of exposures, policies and treaties.

Data inconsistency issues – which are not exclusive to risk management – can quickly snowball into bigger problems, from missed opportunities to costly financial mistakes. An IBM study estimated that data quality issues cost the US $3.1tn per year.

The industry can avoid these mistakes with integrated data system designs. Learn more about the Intelligent Risk Platform and IQ applications that it runs, on the RMS website.


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