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Insurance Solutions

Formerly Moody’s RMS

The property and casualty insurance industry has seen an explosion both in the amount of data and in the accompanying number of applications that generate and utilize this data. The use of data analytics to make informed risk management decisions has also increased as data flows grow in volume and variety.

As business leaders demand timely insights from analytics, the need to collect and process data faster has grown. With this paradigm shift in data usage, data mobility has become a progressively larger challenge. Data must be readily accessible – anytime, anywhere.

Looking back at how data has evolved for insurers, most started with one data pipeline feeding one application that performed all the analysis. Now, insurers are typically building and maintaining hundreds of data pipelines across multiple different applications. Ensuring data mobility across all applications is not an insignificant challenge and presents a major concern for data integrity.

One of the main problems is that data mobility typically results in organizations creating manual processes for moving data from one application to the other. This type of data mobility comes at a price, as these manual processes are error prone, costly to maintain, and can raise questions about data quality.

Manual data processes often result in risk stakeholders, such as portfolio managers or underwriters, not getting the information they need in a timely manner. This places the business at risk as any delays could be exacerbated due to the volume of requests, especially during the renewal period.

Impact of Manual Data Processing on Insurance Workflows

Let’s look at a typical workflow that relies on manual data processing. Line-of-business owners need to understand their cat exposure for individual risks and manage it at the portfolio level, by performing portfolio-level analysis and accumulations.

There are different applications that specialize in this discrete yet interconnected analysis. Data needs to be extracted from system(s) where exposure information is stored, transformed into application-specific schemas, and fed into these applications to execute various analyses. 

To support these activities, cat modelers typically use tools that leverage a structured query language (SQL) query engine to extract the required data elements from their policy administration system, and they will often try leveraging existing SQL queries for each data pull. However, the SQL queries often need to be updated to reflect new parameters for the analysis.

For example, cat modelers might want to extract accounts based on a different date range and/or different geographic region. Unless the SQL queries are properly parameterized, they will find themselves updating the queries manually to run these extract jobs. These manual edits are particularly prone to errors.

After extracting the data using SQL queries, cat modelers need to transform that data to fit into the schema required by various applications. Typically, tools such as Excel are relied on to perform these transformations. Again, the manual steps of copying and pasting data into Excel and data transformations are very time-consuming, error prone, and notoriously difficult to scale.

In situations where business users, such as an underwriter or a portfolio manager, need to perform an ad hoc analysis and/or need the results quickly, they find themselves running these manual steps, which becomes challenging.

Users are not experts and are being taken away from their core strengths. Imagine race-car drivers having to change their own tires during a race. Not only would they be slower than the pit crew but there would also be more chance for error, resulting in a poor finish or even putting drivers at risk.

Workflow Automation for RMS Risk Modeler and RMS ExposureIQ Applications

Addressing the challenge of manual data processes, the RMS® Intelligent Risk Platform™ (IRP) and its cloud-native data architecture present an enormous opportunity for risk management organizations to automate key workflows while reducing the risk of human error.

First, for the transfer of data, the RMS IRP provides a suite of application programming interfaces (APIs) to streamline data mobility and workflow execution. Using these APIs, processes such as importing data into applications, such as RMS Risk Modeler™, can be fully automated.

Clients can develop processes to extract data from exposure systems and transform it into schemas supported by the IRP. Once developed and tested, workflows can be scheduled to run on predefined schedules or on an ad hoc basis.

Second, with more automation, business leaders no longer need to depend on teams to execute manual steps to perform various analyses. Periodic jobs, such as renewal processing, can be fully automated. Business leaders will get analysis results in a timely manner and can start utilizing them to improve risk pricing or reduce portfolio exposure.

Finally, the IRP also provides a unified data store, meaning that users do not need to build multiple data pipelines for each application running on the platform. Data imported into the IRP is available to all applications deployed on the platform in a common “data store.”

Workflow automation can have a significant impact on building a unified view of risk across your applications. For example, because RMS utilizes a common geocoding engine in applications such as Risk Modeler and ExposureIQ™, a given property will have the same exact location in the two applications. For high-gradient perils such as flood, the difference of a few feet in elevation could be the difference between a near miss and significant portfolio losses.

As a business leader, you want your team to utilize advanced analytics and make data-driven decisions. Currently, a lack of trust in the data and inefficient/manual processes are the biggest obstacles to achieving this goal. The IRP addresses many of these challenges and provides tools, such as APIs and a unified data store, that enable data automation, increase process efficiency, and improve data quality.

If you would like to know more about the RMS Intelligent Risk Platform or see it in action, please contact us

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Kirtan Dave
Kirtan Dave
Senior Director, Product Management, Moody's RMS

Kirtan is a member of Moody's RMS Intelligent Risk Platform™ product management team and leads Moody's RMS Risk Data Lake product initiative. Kirtan has over 15 years of experience delivering data and analytics products in the P&C insurance domain.

Prior to joining Moody's RMS, Kirtan held leadership positions at companies such as Insurity, CNA Insurance, and LexisNexis Risk Solutions.

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