Artificial intelligence is fast becoming a strategic asset in reinsurance, and we recently explored this transformation in a special joint webinar with ACORD. Joined by global reinsurer SCOR, our panel shared insights on how AI can be practically applied to overcome data challenges and improve operational efficiency in a highly regulated industry. We explored some key themes during this session:
The real-world impact of AI in reinsurance operations
AI is no longer a future ambition for reinsurance — it’s already redefining operational efficiency, risk analysis, and decision-making. At our recent webinar with ACORD and SCOR, we explored how AI is being deployed in real-world reinsurance scenarios to automate traditionally manual, resource-intensive tasks.
For example, AI-powered data ingestion is transforming how reinsurers process bordereaux files, loss data, and submission documents. What previously took weeks of manual data validation can now be completed in hours, freeing underwriters to focus on higher-value activities like portfolio optimisation and risk strategy. The outcomes?
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Faster, more accurate underwriting
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Enhanced loss modelling
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Better pricing accuracy and risk selection
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Reduced operational costs
These efficiencies don't just improve margins — they help reinsurers respond to market volatility and emerging risks with greater agility.
Strategies for improving data quality while maintaining regulatory confidence
Data is the lifeblood of reinsurance, but poor data quality leads to skewed risk assessments, inaccurate pricing, and compliance vulnerabilities. AI brings powerful tools to improve data quality — from automating data cleansing and standardisation to using machine learning models that flag anomalies and inconsistencies.
However, as data governance becomes more complex, reinsurers must balance these capabilities with strict regulatory requirements. Maintaining regulatory confidence means embedding robust audit trails, ensuring transparency in AI models, and adhering to data privacy standards across jurisdictions.
The key is not just cleaning data, but ensuring that every data-driven insight is explainable and defensible — critical when dealing with regulators or conducting internal reviews.
How to prepare your organisation for AI adoption without disrupting business as usual
Adopting AI doesn’t have to mean a complete overhaul of systems and workflows. The most successful reinsurance firms are taking an incremental approach — integrating AI tools into existing processes while upskilling teams to work effectively alongside new technologies.
Key considerations for a smooth transition include:
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Start small: Pilot AI projects within specific processes like data ingestion or claims analytics to demonstrate value quickly.
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Engage cross-functional teams: Involve underwriting, risk, compliance, and IT from the outset to ensure solutions align with both business goals and regulatory frameworks.
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Invest in training: Equip staff with the skills to interpret AI outputs, maintain oversight, and apply human judgement where needed.
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Build a scalable data strategy: AI thrives on clean, structured data — establishing solid data foundations is essential for long-term success.
Ultimately, AI should augment human expertise, not replace it. The goal is to enhance decision-making, reduce friction, and unlock new insights — all while maintaining business continuity.
Press the play button below to view a clip and get a sneak peak into what was discussed at the webinar:
Want more? Watch the full webinar on demand here.
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