RMS Data Solutions

Value Driven by Data Quality

Insurers and reinsurers have long recognized that a robust risk management practice requires not only the appropriate and well-informed use of models, but quality data describing the exposures at risk. By employing RMS data solutions in portfolio management and underwriting processes, the resulting insights contribute to:

• Improved metrics used in underwriting risk selection and pricing

• Reduction in portfolio management operational risk

• Increased confidence in reinsurance transactions and portfolios

• Improved accuracy of post-event loss estimation

• Demonstration of effective capital allocation to rating agencies

The impact of data quality flows through the insurance lifecycle from primary underwriting through to reinsurance and retrocession, incrementally impacting pricing, profit, and capital allocation along its course. Systematic data quality measurement in underwriting leads to better risk selection and pricing. Well-informed risk selection, whether for policies or treaties, also leads to fewer surprises in the event of a catastrophe, in the form of fewer unanticipated losses or through the preemptive reduction of difficult to identify risk concentrations. Strong data quality practices build confidence in capital allocation and in responding to rating agency inquiries. Systematic data quality management practices are a critical component of overall enterprise risk management, leaving their mark each year, whether implicitly or explicitly, on a company’s bottom line.

Data Quality Framework

RMS data solutions help insurers and reinsurers assess and improve the quality of exposure data used for catastrophe risk management. Proprietary RMS data quality methodologies are based upon a framework that evaluates data quality across three dimensions: consistency—is data presented in a standard format; completeness—is data present and of appropriate resolution; and accuracy—is the data correct.

Data solutions products and services fit within this framework, satisfying the industry need to implement data quality best practices. The RMS® Data Cleansing Service addresses the consistency aspect of data quality, offering a rigorous process for data formatting, cleansing, and geocoding enhancement. RMS Data Quality Analytics, packaged in a variety of RMS products and services, deliver straightforward metrics informed by RMS models that quantify the quality of exposure data for improved exposure data completeness, accuracy and catastrophic risk modeling.

Data Quality Analytics Introduction

RMS Data Quality Analytics deliver objective and independent insight into the main elements of exposure data―where it is (location), what it is (vulnerability attributes), and how much it is (valuation)—allowing insurers and reinsurers to assess the quality of exposure data input into catastrophe models. Key components of Data Quality Analytics include completeness scores and accuracy assessments—future versions will also include industry benchmarks.

Benefits of RMS Data Quality Analytics

• Inform underwriting, portfolio management and capital allocation decisions
  

• Systematically improve data quality management processes
  

• Increase consistency and confidence in data quality using standardized metrics and reports
  

• Prioritize and focus actions on areas that the greatest impact on model results
   

Data Quality Analytics Delivery Platforms

Data Quality Analytics are currently delivered through a variety of products and services in order to address specific user business requirements.

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