Three recent surveys with excellent representative samples research different aspects of business and all come up with the alarming confession by insurers that their ambitions to leverage enterprise and third-party data to drive transformation and innovation are thwarted by a lack of data processing and management capabilities.  I draw together the lessons to learn and actions to remedy this inability to ‘connect the data dots’.

Cap Gemini- 70% of trailblazers integrate third-party data effectively with traditional data compared with 12% of typical insurers.

Aiimi Ltd- 70% of those we surveyed agreed that their business understands the importance of good data in operationalising AI, but 27% felt that their company's data was of insufficient quality for use in AI model training or tuning 

Bain & Company- Only 5% to 10% of carriers consistently capture value from their data and technology investments. The big challenge is analyzing new forms of data and extracting useful insights.

Cap Gemini focused on underwriting and the learning points are equally valid for other parts of the insurance value chain- especially claims, supply chain management, and counter fraud.

Download Cap Gemini's report;  WORLD PROPERTY AND CASUALTY INSURANCE REPORT 2024 

Aiimi focuses on its core strength- helping companies operationalise AI, from extractive and conversational to generative AI with Large and Small Language Models (LLMs, SLMs). 

Download Aiimi Ltd's report-  AI NOW- how UK business leaders can operationalise AI

Bain and Company's study explores why data management and analytics have become essential for carriers looking to tap emerging opportunities.

The three studies conclude that most insures lack data maturity which means the wish to transform is held back by a lack of trust in their data. 

Cap Gemini reports that: -

Aiimi's report has similar findings: -

 

Insurers that overcome this data challenge show remarkably better outcomes than the majority that do not as you can see in the diagram above and in that below.

Years of investments in data warehouse projects and data lakes and still insurers find it a challenge to connect the data dots to give underwriters, claims teams, supply chain teams and others the insights to make better decisions. Or, to train and fine-tune AI models.

The speed with which new data sources appear, new technologies, and new AI models bypass these inflexible data warehouse models. Insurers deserve adaptability from systems- not a feature of the many legacy systems that still permeate an insurer. New risks and regulations add to the challenges, so Aiimi proposes an adaptive and iterative approach to setting strategy. THat will help keep pace with change make the optimal use of tools available. 

Data is one of the potential competitive advantages insurers can leverage.

 

 

Bain & Company illustrates that Insurance carriers that are technology leaders have out-invested in several key areas.

 

The surveys offer practical playbooks to ensure insurers can leverage data from across internal and external sources whatever the source and format. In Aiimi's case without the need to integrate the data but still leverage AI to interconnect your data and information, improve data quality and governance, reduce risk, and deliver the right answers to the right people. 

Connecting the data dots

You would think this was a fundamental priority achieved by most insurers investing heavily in big data management, data lakes, and AI-driven projects. 

Data silos still prevail, and these grand projects continue to go over budget, over time, and invariably under-achieve. Not just carriers, but also brokers and MGAS including the growth of global conglomerates that have acquired diverse businesses covering every kind of risk from aviation to zoos. So many applications, platforms, and core record systems are still based wholly or partly on legacy technology, hindering data connectivity. 

Too many core platforms are policy-focused rather than customer-centric hindering personalising solutions and optimising lifetime premium potential. 

Cap Gemini urges insurers to master the intersection between underwriting-specific technologies, CoreTech platforms, and data lakes to turn brilliant dynamic underwriting into the products customers want now, and in the future, plus world-beating customer experiences.

Digital workbenches can help evolve the underwriting role and boost productivity, customer experience, and discipline. ( page 20 in the report). 

It is no good jumping in the deep end without a sound plan, strategy, and the resources to execute transformation projects. Lack of data mastery is just one business challenge and, unless insurers know all of the enterprise’s predictive analytics gaps, transformation advance will be limited. We must first know our customers’ challenges and opportunities before looking at our own. That's an essential first step before you start planning underwriting transformation so that the right customers, markets, and risk cover can be optimised for lifetime customer revenue/premiums and profitability. That requires relevant, actionable, and accurate insights.

Unlocking actionable insights

Modern  MACH-architected core systems, automated data insight engines, and predictive analytics are key to unlocking the insights identified as vital to meet an insurer’s goals, strategy, and business plans. They can enable insurers to ingest diverse data and power AI from extractive and conversational to full-blown generative AI. 

Combining Cap Gemini’s examples with those described in InsurtechWorld.org you can see examples of good practice.

  • Munich-based Allianz with UK Insurtech Cytora to drive AI-powered risk processing and management (page 19 of the report) 
  • Cytora and hyperexponential partnering to “provide commercial insurers with a more streamlined and informed understanding of risk. The move improves underwriting decisions by basing pricing on real-time data and driving profitable growth.”
  • CGI’s Elements 360 underwriter’s workbench plus Ratabase360 rating technologies power major insurers- over 200 worldwide 
  • Quantee partnering with Zurich Insurance to “provide state of the art, flexible platform that blends traditional and next generation AI-driven techniques for Pricing”
  • MGA The Demex Group analysing and transferring climate-related risk with innovative reinsurance solutions for US carriers and self-insured
  • Predictive insurance analytics-as-a-service provider Giroux.ai found a niche providing data mastery and predictive underwriting insights for MGAs large and small. . 
  • Aiimi Limited with its Insight Data Platform delivering enterprise knowledge into the hands of customer-facing employees at the FCA, blue chip companies, and insurers. Ensuring data mastery vital for effective AI deployment including LLMs, SLMs, and teams of both LLMs; an essential first step.
  • Modern MACH-architectured core platforms (see description in further reading at the intersection of PAS Systems, Data Lakes, and underwriting technology e.g. Genasys, Instanda, EIS, Novidea, and ICE
  • UK carrier Esure had the vision, courage, and leadership buy-in to re-platform to the MACH architected EIS CoreTech combined with AWS, Amazon Connect, RightIndem Claims platforms, and GenAI with implementation partner EY

If you want to delve into the themes above please drop me a line here

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