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analytics with Insurers part 1

Reinsurers; are they keeping up in analytics with Insurers? – Part 1

Insights from a recent McKinsey Covid-19 impact survey across industry verticals show those organizations that are outperforming others on innovation and growth. Of particular note is that 57% of insurance organizations view information silos and lack of cross-functional collaboration impeding success, and 32% view slow decision-making as a secondary factor. While we can view data as the integral driver of these factors, analytics must be positioned as the enabler.

Primary Insurance organizations with a retail focus are naturally motivated to reducing barriers when servicing customers’ needs and streamlining the distribution process. A heavy emphasis is placed on the customer experience, with autonomous decision-making data point collection featuring robust connectivity to third-party data sources, Internet-of-Things components or leveraging non-linear models of Artificial Intelligence, Machine Learning or RPA to anticipate and deliver to the consumer a seamless engagement experience through a web-based portal.

For these organizations, front-end web analytics provide the vehicle to attain customers, ingest data and align the portfolio.  The Capgemini World Insurance Report 2020 highlights predictive analytics techniques, boosting data processing, performance and management, with over two-thirds of respondents committed to advanced analytics in this segment. 

Sitting behind the decision-making process at retail Insurers is a raft of tools that provide an introspective view, exposure assessment, pricing, and portfolio management consummate to the systems and processes which the organization has to negotiate within the extensive data management, risk evaluation and reconciliation process.  Arguably, access to decision-making data is appreciably simpler at the insurance value-chain’s front-end than when risk-transfer occurs downstream.

So, what about the reinsurance industry? Issues vary dependent on factors such as years in business and size.  For instance, in larger incumbent commercial Insurers, legacy process, system and company culture can hinder the adoption of new systems designed to leverage technological capabilities that enhance automation of workflows and the decision-making process. Smaller reinsurance organizations and Insurtechs who have greater flexibility in their operational model are better positioned to adapt to market conditions and expand or contract their book near to real-time, which provides a competitive advantage.

However, Reinsurers also have a different focus and set of challenges from primary Insurers.  Although the level of individual transactions is not comparable to retail insurance, competitive advantage still relies on the ability to analyze large volumes of submission and exposures without either the granularity or veracity of data required to make solid decisions. Timely and accurate decisions, more than ever, must rely on exposure and loss analytics,

Steps to organizational, analytical maturity


Attaining organizational and analytical maturity (in the context of company goals) is paramount before jumping on the latest analytics bandwagon. McKinsey characterizes a five-step analytical maturity process through descriptive, diagnostic, predictive, prescriptive, and cognitive analytics, which moves from hindsight what/why dimensions to the foresight of will/should happen.

However, it is only through the application of advanced cognitive and self-learning of AI and Machine Learning that the identification of unknown-unknowns can be established. From a reinsurance underwriting context, the significant can be made by identifying business opportunities that are currently untapped.   

Reinsurers must consider the context of the organizational strategic objectives and benchmarks against the current analytical capabilities which support information needs. The dimensions of this depend on the company's required organizational agility, growth, and operational needs. McKinsey views this transformation as building insights, capturing value, achieving scale, and becoming an analytics-driven company.

An important point is understanding what is driving the need for analytics in the organization. Typically, this is around supporting decision-making in pre- and post-bind processes, qualifying submission and claims data, and multi-dimensional planning as close to real-time, validating alternative scenarios for the application of capital based on dynamic market changes.

Read Part 2 here is the Reinsurance industry ready?

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