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AI INSURANCE SPOTLIGHT

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AlbanTranchard

This document discusses using AI and data modeling to improve insurance operations. It provides examples of using structured text, customer data, and other sources to build models for risk assessment, fraud detection, and other tasks. The document emphasizes the importance of data quality and having a holistic data strategy. It also stresses an iterative approach to AI development and increasing skills. An example use case describes using natural language processing models to assist underwriters by providing relevant risk expertise and intelligence from various data sources.

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INSURANCE SPOTLIGHT
Use DATA & MODELS to PREDICT the most likely answer to a specific question Today’s AI in Practice DATA Stats Text document IoT, sensors Images Spatial Sound Video Click Metadata… EXAMPLES Structure text documents as statistical records Customer segmentation, scoring models, sentiment analysis, chatbots… Risk modelling, rating, loss damage estimates… Loss fraud classification, process triage… MODEL USE Parse & structure information Find patterns & ‘groups of entities’ without a-priori Calculate best estimates based on set of inputs Learn “rules” for accurate classification tasks [ a.k.a Narrow AI ]
OPERATIONAL EXCELLENCE IMPORTANCE of AI CUSTOMER INTIMACY Advances in data, modelling techniques and computing provide NEW OPPORTUNITIES to GROW REVENUE, REDUCE COST & OPTIMIZE MARGIN RISK EXPERTISE
AI QUEST for... CUSTOMER INTIMACY Real-time Access Personalized Relationship Tailored Products Accurate Risk Assessment Ease of Interaction Proactive & Reliable Service Adequate Pricing Prevention & Assistance Using AI to gain Deeper Understanding of Customers
AI QUEST for... OPERATIONAL EXCELLENCE Deliver Economies of Scale Scale Information Gathering Improve Process Intelligence Improve Service Delivery Automate Workflows Tracking & Monitoring Maximize Customer Experience Minimize Leakages & Costs Using AI automation to Drive Effectiveness & Efficiency
AI QUEST for... RISK EXPERTISE Ensure Quality & Extent of Risks Sales & Distribution Optimize Value Proposition Enterprise Risk Management Underwriting & Engineering Claims Protect Shareholders’ Value Foresee Trending & Emerging Risks Using AI prediction to Manage Risks at next level
AI ENABLERS
POOR DATA > WEAK MODEL > SUPERFICIAL INSIGHT DATA IS OFTEN THE FIRST HURDLE TO OVERCOME Fragmented & constrained legacy systems Missing unified data architecture across sources Dry upkeep investments, lack of data ownership.... GO FORWARD PRINCIPLES Adopt holistic approach to data assets across organization Ensure data flow beyond its traditional silos Stress importance of «cross-functional» data needs Focus capabilities on automated data capture & processing Invest in external data rahter than external technologies Define clear data ownership
AI ROADMAP > ITERATE > UPSKILL ITERATE UPSKILL Heavy lift with the enterprise-wide data strategy Not one single AI, but a portfolio of diverse AI use cases Be selective & balance value-add vs. complexity / AI maturity AI performance is gained through multiple iterations Rethink process entirely rather than «patching» AI on top Third party vendors may be strategic especially around data acquisition Internalize knowlegde & skills to bridge business acumen with science Outreach AI education & data-driven culture beyond technicians Establish AI oversight : AI ethics, privacy, trust Leverage & partner with Open Source for tech flexibility & durability
LARGE COMMERCIAL UNDERWRITING RISK INTELLIGENCE USE CASE EXAMPLE
Underwriting Large Commercial Insurance Complex risks, often with global exposures across different insurance markets Very diverse industries requiring subject matter expertise not always available locally Bespoke coverages with nuanced terms & conditions adding complexity to underwriting analysis Insurance programmes typically exposed to low frequency - high severity loss events Rely heavily on qualitative risk assessment with concise market information Price rarely a lever in current market conditions & does not mitigate alone large risks exposures MOST IMPORTANT FACTOR IS RISK SELECTION to warrant profitability across the whole portfolio
Assist Underwriting with Risk Intelligence By delivering all relevant expertise & know-how at the local «point of underwriting» AI technique based on Natural Language Processing – NLP All qualitative information in a submission automatically analyzed & linked against various internal databases (eg. Corpus of directives, guidance papers, corpus of all portfolio submissions, risk engineering documents, expert documents, claims intel, authority checks etc...) The AI model enables us to extract all the relevant & applicable information based on content semantic similarities Save endless hours of manual work, enable comprehensive and consistent underwriting, speed up response to market requests Unlock all accumulated collective knowledge to augment the quality of the risk assessment KEY OUTCOMES Expand underwriting platforms with targeted risk knowledge & assisted intelligence AI SOLUTION
ALBAN TRANCHARD ACTUARY I hope this is an informative perspective into AI It is an exciting time to be in the insurance industry AI is bound to unlock so much more value and we can all be the architects of it Always feel free to Connect and Message

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AI INSURANCE SPOTLIGHT

  • 2. Use DATA & MODELS to PREDICT the most likely answer to a specific question Today’s AI in Practice DATA Stats Text document IoT, sensors Images Spatial Sound Video Click Metadata… EXAMPLES Structure text documents as statistical records Customer segmentation, scoring models, sentiment analysis, chatbots… Risk modelling, rating, loss damage estimates… Loss fraud classification, process triage… MODEL USE Parse & structure information Find patterns & ‘groups of entities’ without a-priori Calculate best estimates based on set of inputs Learn “rules” for accurate classification tasks [ a.k.a Narrow AI ]
  • 3. OPERATIONAL EXCELLENCE IMPORTANCE of AI CUSTOMER INTIMACY Advances in data, modelling techniques and computing provide NEW OPPORTUNITIES to GROW REVENUE, REDUCE COST & OPTIMIZE MARGIN RISK EXPERTISE
  • 4. AI QUEST for... CUSTOMER INTIMACY Real-time Access Personalized Relationship Tailored Products Accurate Risk Assessment Ease of Interaction Proactive & Reliable Service Adequate Pricing Prevention & Assistance Using AI to gain Deeper Understanding of Customers
  • 5. AI QUEST for... OPERATIONAL EXCELLENCE Deliver Economies of Scale Scale Information Gathering Improve Process Intelligence Improve Service Delivery Automate Workflows Tracking & Monitoring Maximize Customer Experience Minimize Leakages & Costs Using AI automation to Drive Effectiveness & Efficiency
  • 6. AI QUEST for... RISK EXPERTISE Ensure Quality & Extent of Risks Sales & Distribution Optimize Value Proposition Enterprise Risk Management Underwriting & Engineering Claims Protect Shareholders’ Value Foresee Trending & Emerging Risks Using AI prediction to Manage Risks at next level
  • 8. POOR DATA > WEAK MODEL > SUPERFICIAL INSIGHT DATA IS OFTEN THE FIRST HURDLE TO OVERCOME Fragmented & constrained legacy systems Missing unified data architecture across sources Dry upkeep investments, lack of data ownership.... GO FORWARD PRINCIPLES Adopt holistic approach to data assets across organization Ensure data flow beyond its traditional silos Stress importance of «cross-functional» data needs Focus capabilities on automated data capture & processing Invest in external data rahter than external technologies Define clear data ownership
  • 9. AI ROADMAP > ITERATE > UPSKILL ITERATE UPSKILL Heavy lift with the enterprise-wide data strategy Not one single AI, but a portfolio of diverse AI use cases Be selective & balance value-add vs. complexity / AI maturity AI performance is gained through multiple iterations Rethink process entirely rather than «patching» AI on top Third party vendors may be strategic especially around data acquisition Internalize knowlegde & skills to bridge business acumen with science Outreach AI education & data-driven culture beyond technicians Establish AI oversight : AI ethics, privacy, trust Leverage & partner with Open Source for tech flexibility & durability
  • 11. Underwriting Large Commercial Insurance Complex risks, often with global exposures across different insurance markets Very diverse industries requiring subject matter expertise not always available locally Bespoke coverages with nuanced terms & conditions adding complexity to underwriting analysis Insurance programmes typically exposed to low frequency - high severity loss events Rely heavily on qualitative risk assessment with concise market information Price rarely a lever in current market conditions & does not mitigate alone large risks exposures MOST IMPORTANT FACTOR IS RISK SELECTION to warrant profitability across the whole portfolio
  • 12. Assist Underwriting with Risk Intelligence By delivering all relevant expertise & know-how at the local «point of underwriting» AI technique based on Natural Language Processing – NLP All qualitative information in a submission automatically analyzed & linked against various internal databases (eg. Corpus of directives, guidance papers, corpus of all portfolio submissions, risk engineering documents, expert documents, claims intel, authority checks etc...) The AI model enables us to extract all the relevant & applicable information based on content semantic similarities Save endless hours of manual work, enable comprehensive and consistent underwriting, speed up response to market requests Unlock all accumulated collective knowledge to augment the quality of the risk assessment KEY OUTCOMES Expand underwriting platforms with targeted risk knowledge & assisted intelligence AI SOLUTION
  • 13. ALBAN TRANCHARD ACTUARY I hope this is an informative perspective into AI It is an exciting time to be in the insurance industry AI is bound to unlock so much more value and we can all be the architects of it Always feel free to Connect and Message