Archives: Agenda

How day 1’s digital backbone enables day 2’s human and machine capability.  Why 2026-2030 will redefine roles, skills and decision making and the shift from digital transformation to digital performance leading to precision and proof.

• How leading insurers are designing data for speed, accuracy and scale and connecting the dots across functions
• The operational impact and gains of simplified data, faster decisions, fewer errors and better customer outcomes.
• What changes when teams see the same truth and what a modern data backbone looks like in 2026.

The first pricing, claims and liability shifts insurers must prepare for

From driver risk to risk system, what disappears first.

How pricing models will change.

What changes with autonomous vehicles in insurance.

The market has moved on from ambition to accountability, and the firms pulling ahead are the ones turning digital investment into measurable performance

• Underwriting, pricing and claims decisions are being reshaped in real time, with new pressure on governance and control
• Insurers are consolidating vendors and building ecosystems to move faster and reduce operational drag
• Resilience, auditability and compliance are now built in from day one

The question is no longer whether AI belongs in the underwriting process. It is how to combine machine intelligence and human expertise to make faster, better and more consistent risk decisions. We examine:

• How leading insurers are redesigning underwriting workflows around AI, and where human judgement remains the critical differentiator
• How the rhythm of risk selection is changing, from submission triage and pricing support to referral logic and portfolio steering
• What the shift means for key roles, team structures, and the skills carriers need to build for the next phase of transformation

MODERATOR: Aadil Bundeally, Transformation Director, Lloyds (Former)

Three automation projects that paid back in under 3 months.

What made them work: scope, governance, change management and more.

How to replicate these wins and scale automation.

Senior representative, UNIPRO

• What areas of insurance are customers comfortable using AI tools in at present?
• What are the main barriers for AI usage/ What do consumers see as the advantages of using AI tools?
• How do attitudes towards AI in insurance vary between personal and commercial lines?
• Is there an opportunity for insurers with AI liability insurance?
• Does increased AI usage heighten cyber risk and how wary of the cyber threat are customers?

Poor data quality continues to slow down underwriting, distort risk selection and limit the impact of AI. We examine where the real bottlenecks sit in the data pipeline and what insurers are doing to fix them.

• Where time is really spent: scrubbing schedules of values and extracting usable data from messy inputs
• Why data quality directly impacts loss modelling, pricing accuracy and portfolio exposure
• How AI is being applied to automate data cleaning and document extraction, and where it still falls short