-Beyond loss mitigation: where would customers welcome greater insurer intervention in their lives? -A significant revenue stream? Assessing the commercial potential of value-added services -From data to insights to actions: understanding the data requirements -Countering rising prices: could effective and engaging value-added services help to bring down premiums? -Using services to sell insurance: a distribution approach of the future? -Addressing exclusionary personalised pricing: to what extent are value-added services the solution?

-Are insurers keeping pace with changing customer product demands? -Are factors beyond price becoming determinative for customers? Which ones? -Enabling proposition agility: how do IT systems need to adapt? -How can products be modularised to allow for more personalised offerings? -From subscriptions to UBI: assessing the potential of emerging product types -Cross-lines connected policies: the insurer as a life partner

-From fraud to risk management: where have AI-driven analytics shown the greatest return so far? -Where have third-party data sources had the biggest impact? How will this develop in future? -Realising real-time analytics: what are the keys to success? -Avoiding bias: how can insurers make sure to maintain ethical standards? -How are increasing pressures for explainable AI impacting development? -Quantum computing: is this the next step in analytics sophistication?

-What more needs to be done to fully align AI innovation with business goals? -Moving beyond the lab: achieving true scale with AI -Cultural resistance: helping staff through disruption -Democratisation of knowledge: how must the role of frontline employees change in an AI-powered organisation? -Ethical AI: what controls need to be in place to ensure fair and equitable AI outcomes? -What is the timeline for AI unlocking completely new and innovative business models?

-Were is low-code, no-code likely to have the most transformational impact? -Addressing the IT skills gap: is low-code, no-code the answer? -The rise of the citizen developer: empowering non-IT staff while maintaining oversight -Avoiding ‘spaghetti architecture’ and ‘shadow IT’: what management and precautions are required? -Security and compliance: how can risks be mitigated?

-How must transformation strategies adapt around ‘uncertainty as a new normal’? -Digital as a core aspect of the insurance product: what are the implications for transformation? -Where are the next upheavals likely to come from, and how can insurers prepare? -Balancing short-term profitability with long-term transformation: what are the key considerations? -More than a buzzword: what does genuine customer-centricity entail? -How is the role of insurtech evolving in helping to secure digital transformation? -Achieving harmonious transformation of the business model and IT: what are the keys to success?

-How are the regulation and customer expectations changing the pressures on the SIU? -Doing more with less: where are key efficiency gains in the SIU? -How can new data sources be leveraged to better empower the SIU? -How can triaging and automation be leveraged to focus investigators on high value tasks?

-Old dog, new tricks: understanding the changing face of fraud -A migration to commercial lines? Where are the main threats? -How must the fraud function support digital transformation of the business? -Busting synthetic IDs: what are the keys to success? -Fighting fraud and maintaining CX: resolving the tensions

-The ROI from automation: how much can loss ratios be improved? And what are the other potential benefits? -How can fragmented systems and processes best be unified to enable greater automation? -How are the skills required of underwriters evolving in an increasingly automated workflow? -How can automation be built out into more complex products and risks?

-How can AI be optimised to navigate an uncertain risk landscape? -Learning from the best: training AI to mimic the result of the highest performing underwriters -What role can new and alternative data sources play in unlocking greater accuracy? -Generative AI: to what extent could this represent a step change in underwriting capability? -Art and science: how can the role of human expertise be maintained in an AI world?

-How are changing demographic and health trends impacting underwriting? -Digital health, genetic testing, pollution monitoring: how are new data sources transforming risk modelling? -From understanding to CX: how will wearables unlock the future of underwriting? -Navigating AI risks: assessing privacy concerns, bias, and regulatory compliance -Continuous underwriting: is this the future of life and health insurance?