Aiming Big with Data

By Upendra Belhe, Ph.D., Chief Data and Analytics Officer, Gen Re

Upendra Belhe, Ph.D., Chief Data and Analytics Officer, Gen Re

Many P&C insurance carriers/ reinsurers are seeing returns from predictive analytics regarding their rating processes. Their success stems from their ability to reduce a great deal of uncertainty that was attributed to a very manual underwriting and actuarial approach. But what more can be done? Savvy carriers are looking to further the expansion of predictive analytics and gain a cultural and competitive advantage by applying these analytics to marketing, claims and underwriting in new ways.

Get started with data differently

Only a few forward-looking companies (including the one that I belong to) are thinking about establishing large datasets to help connect the dots. Their idea isn’t to seek specific samples of clean data targeted towards a preconceived analytics initiative, but rather to assess a business strategy. Typically, they gather risk, exposure and loss characteristics together in one place in an agile manner. Not all data may be well populated and tremendously credible. But the attempt is to do “reasonability checks” and focus on the ability to draw insights. In order to build such assets quickly and in a cost-effective manner, these organizations are seeking the route of constructing a Data Lake. Moreover, external data that can be economically acquired is very much in scope.

Think Stories Instead of Models

There is now a conscious shift from asking “what model can I build?” to “what insights can I uncover towards our strategic goal?” The insights revealed are so purposeful, that no one needs to ask the “so what?” question. Interdisciplinary teams work on insight generation via a storyboarding approach where interactive visualizations leverage collective intelligence. Business leads are engrossed in gathering different viewpoints based on their deep understanding of the business and the graphical format of the data.

Actions are Mandatory

The “selling” or “adoption” of analytics outcomes isn’t a concern in such an environment. Since the starting point is some strategic issue, the insights are meant to be “actionable.” Senior leadership often gets involved in checking the “plan of action.”The business team’s enthusiasm in crafting those next steps gets accompanied by monitoring dashboards. Old ways of thinking and assumptions are already challenged this way and lead to more ideas aimed at further opportunities.

Tying Loose Ends is Crucial

Throughout the journey, we can’t ignore the importance of tools used for insight generation. Increasing emphasis on open source software and code documentation is helping mitigate over-dependence on expertise. Business teams are being encouraged to think more broadly and push the envelope regarding what data should be captured. Since the purpose is to gain directional insights, data accuracy doesn’t affect the conclusion much. However, data quality issues aren’t ignored, and a plan is put in place to address them. Most importantly, there is an increasing awareness that the Data Lake shouldn’t become a data dump. Data Governance, in the form of both a data dictionary and data lineage, is actively sought after.

The idea is to keep challenging the status quo and expanding strategic initiatives by democratizing data and socializing insights rather than being content with a few models seen through implementation.

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