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Reinsurance industry ready

Reinsurers; are they keeping up in analytics with Insurers & is the Reinsurance industry ready - Part 2

In part one of this two-part blog, we looked at the analytics approach retail insurers are taking organizations to attain customers, ingest data and align the portfolio and why Reinsurers must consider the context of the organizational strategic objectives and benchmarks against the current analytical capabilities which support information needs. In the last part of this blog, we look at the advantages descriptive analytics and diagnostic analytics provide to Reinsurers.

Is the Reinsurance industry ready?

With the advancement in technology outside of the insurance industry moving at a pace yet to be capitalized on within the industry, the 2020 Capgemini report identifies that from a primary insurance perspective, real-time data insights are currently realistic for only 38% of respondents. However, over a third of the respondents to Capgemini’s survey said they are investing in advanced analytical techniques to generate deeper insights from captured data.

However, from a reinsurance perspective, Deloitte reveals that Reinsurers are not yet fully leveraging automation to increase transparency, controls and analytic capabilities that the complexity of reinsurance contract structures demand. Arguably, Reinsurers more than direct Insurers, require this due to the complexities of the layering and nested inurring reinsurance structures that hinder rapid marginal and contract impact analysis across the portfolio.

reinsurance automation journey – Deloitte

Source: Jump-starting your reinsurance automation journey – Deloitte

The insurance industry remains somewhat in the paradigm of hindsight analytics. In McKinsey’s Covid survey, results show a strong recognition that investment in technology is a focus for the insurance industry as a whole. However, the role of predictive analytics and its application to reinsurance (in particular) has been a more protracted application than the innovation we have seen across the primary markets supporting the retail insurance segment.  

As underlined above, a gradual increase in capability begins with ensuring that the ingested data is worthy of analysis.  We repeatedly witness tunnel vision on this point; any data focus has objective metrics tied to the data points that measure increasing improvements in data veracity linked to objective business outcomes (to indicate relative progress and success). 

data quality

Descriptive analytics and diagnostic analytics provide insights into the current book of business at whichever dimension is required. Typically, a self-service BI tool or MIS reporting will suffice based on canned reporting requirements across business units, personas and groups.  However, the evidence from leading consultants suggests that the differentiator that enables the Reinsurer to consider market rate changes grows, whilst closely monitoring exposures depends on the Reinsurers ability to delve into a multi-dimensional view across multi-line business, geography, and complex contract structures.  

As a segment, Reinsurers are not just playing catch-up on data orchestration, augmentation, and forward-looking predictive data analytics to support interrogating drivers of profit and loss. A proportion of the market remains competitively disadvantaged against their peers as their foundational analytics maturity is yet to attain a qualitative position for descriptive (what) and diagnostic (why) analytics that in itself would provide differentiating insights into the portfolio.  There will always be a place for this type of reporting, and it should be considered a foundational building block that all organizations aspire towards achieving.

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