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Competitive Advantage through Analytics

Martina Conlon
Insurance Experts' Forum, May 23, 2014

Insurers have many opportunities to leverage advanced analytics across the enterprise, including underwriting, claims and marketing. But without a solid foundation, analytics can overwhelm companies that have yet to get the most out of structured enterprise data.

The first step in creating an analytics environment is to define a data strategy, which is a major step toward data mastery and avoiding data mayhem. Many consulting firms offer information management and analytics expertise to help insurers build solid foundations for analytics and big data. Plus, a variety of insurance-specific data warehouses, analytics tools and predictive models also are available, as is third-party data, which is abundant and occasionally free.

Analytics is a broad term that comprises the data, technology and tools that help organizations make better decisions, and includes:

• Internal Data. Data leveraged through data warehousing, reporting and business intelligence (BI) tools, integrated and accurate repositories, including producer, quote, customer, policy, billing, claims, financials, reinsurance and workflow metrics used for operational reporting, what-if analysis, dashboards, ad-hoc analysis, data extraction and delivery, profitability analysis, and producer management.

• Third-party Data. Data acquired externally that is used in operations and back-end analysis, including demographic, consumer listings, buying behaviors, property, health, claims, location, geocodes and weather data.

• Standard Predictive Scores. Purchased scores that indicate the likelihood of something happening, that are calculated and acquired externally, such as credit scores, social media scores and wind-hail location scores.

• Custom/Proprietary Predictive Scores. Predictive models developed specifically for a carrier based on internal and external data and specific characteristics of their appetite, geography, regulatory environment and book. Custom predictive models often incorporate standard purchased predictive scores.

• Big Data. Big data is internal and external data, structured and unstructured, collected in enormous volumes at a rapid velocity. Big data typically includes recorded audio, consumer databases, social media feeds, geospatial data, satellite photos, telematics and other sensor data, public records, news services and more.

Data analysis, predictive models and the ability to take action based on the outcome of those models always have been critical to insurers. But powerful BI tools and platforms, big data technologies and the increased the visibility of data analysts and scientists have spurred interest in leveraging analytics across the enterprise.

Marketing departments can use internal and big data to better inform marketing campaigns and develop leads and cross-selling opportunities. With third-party data, micro segmentation and even “pre-underwriting” can improve target prospects and hit ratios. Web usage, social and third-party data can be used to
anticipate purchases and life events that can indicate insurance needs and enable earlier prospect engagement. Consumer, claims, location and risk data can be leveraged to design loyalty and retention programs that deliver the right information to policyholders at the right time via preferred communications channels.

R&D departments can use internal and third-party data when defining, validating and projecting the profitability of new products, rates and rules; and distribution can use analytics to better target prospective agents and manage their sales performance.

Underwriting has long been data and rule centric, but BI and third-party data analysis can uncover dependencies and insights to define better underwriting rules and improve risk selection. Predictive risk models can improve consistency, transparency and automate segments of the underwriting process and ensure the right underwriter sees the right submissions, all while driving profitability. Big data and analytics can offer enriched context around specific risks to more accurately priced personal and commercial lines.

Claims offers fast and quantifiable return on investment for analytics. Using predictive models for claims triage and expert-adjuster or special-investigation-unit assignments can have a big impact on claims severity, especially for long-tail lines of business, such as workers’ compensation. Insurers can use
scoring models to determine which claims are candidates for subrogation, litigation and settlement and more accurate and automated loss reserving. Analysis and predictive models also can help insurers uncover potentially fraudulent claims early in the process, and identify fraud rings and patterns.

Business stakeholders increasingly are hearing competitors’ success stories and are eager to understand how analytics can help them achieve their objectives. Job one is to align business goals with current capabilities and create a solid data foundation. In short, analytics, big data and predictive modeling can have a transformational effect on insurers, but it takes vision, planning and patience. Those who are taking note and investing accordingly will be best positioned to gain real competitive advantage.

Editor's Note: This column first appeared in our June digital issue.

INNSight is exclusive commentary from Novarica.

Martina Conlon is a principal at Novarica, a research and advisory firm focused on insurance technology strategy for insurers.

Comments (1)

Early in the article you mention defining a data strategy. I'd like to hear more on what you view as ture elements of a data strategy.

Posted by: Jon A | May 30, 2014 11:57 AM

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