AI cuts price determination time by 75% at Swiss Re

Context

TL;DR

• AI boosts productivity by up to 80% in Swiss Re processes.
• AI reduces pricing determination time from weeks to a day.
• Humans remain central in decision-making despite AI adoption.
• Data integrity and quality are crucial for AI effectiveness.

Key facts

• AI productivity: Up to 80% increase in efficiency in some business processes.
• Pricing time: Reduced from up to three weeks to one day with AI agents.
• Applications used: Decreased from 14 to fewer than 5 for pricing determination.
• Decision-making: Humans make critical decisions, not AI.
• Data integrity: Central theme for successful AI adoption and process streamlining.
• Cyber risks: Potential damage from AI could exceed single company coverage capacity.
• AI partner: Swiss Re collaborates with Palantir for AI governance and transparency.
• Training needs: Employees need AI and data management skills for effective adoption.

Swiss Re CEO Andreas Berger has stated that artificial intelligence will boost work productivity to levels not seen in decades, potentially increasing efficiency by up to 80% in some business processes. In an interview with the NZZ am Sonntag, Berger explained that the reinsurance giant is redesigning its core processes from scratch using AI agents, without reducing staff. The goal is to free up time for higher-value activities such as claims management, closing new deals, and supporting customers to enhance their resilience.

…

Operational details

The introduction of AI into Swiss Re’s business processes is not just about efficiency—it also involves reallocating human resources and managing risks. For cross-border workers from Ticino who operate in similar sectors, such as financial services or insurance consulting, this innovation could represent a radical shift in their daily work routines. But what does this mean in practice for those who cross the Italy-Switzerland border every day?

Before vs. after: what changes for employees

Before AI, Swiss Re employees took weeks to determine an insurance premium, relying on manual processes that involved 25 steps and the use of 14 different software programs. Today, thanks to AI agents, the same process can be completed in less than a day, reducing complexity and the risk of errors. This not only accelerates operations but also frees up time that can be reinvested in higher-value activities, such as claims management or the development of new insurance products.

📊 Key takeaway: AI transforms time-consuming, error-prone tasks into streamlined, efficient processes.

Implications for Ticino’s insurance sector

Ticino is home to numerous insurance companies and brokerage firms operating at an international level. The adoption of AI could become a competitive advantage, pushing local businesses to implement similar solutions to keep pace with Swiss and global competitors. However, fragmented IT infrastructures remain a significant obstacle, particularly for smaller companies. 'Data integrity and quality are fundamental,' Berger noted, emphasizing that without a robust IT system, AI risks becoming an additional cost rather than an opportunity.

⚠️ Challenge: Smaller firms in Ticino may struggle to adopt AI due to outdated systems and limited resources.

…

Key points

If you are a Swiss Re employee or work in the insurance sector in Ticino and want to understand how AI could impact your work, here's what to do today to prepare for the change, step by step.

Step 1: Assess the current status of the processes

1. Analyze your current processes: Identify procedures that are most time-consuming and error-prone. For example, insurance pricing or claims management. 2. Document each step: Create a detailed map of the 25 steps required before AI implementation, as Berger describes. This will help you identify where AI can bring the most benefit. 3. Involve your colleagues: Discuss with team members to understand what the pain points and specific needs are. AI adoption should be driven by real need, not technological hype.

Step 2: Check the data quality

1. Data cleansing: Make sure your data is complete, up-to-date, and free of duplicates. Swiss Re emphasized that data integrity is critical to the effectiveness of AI. 2. Data structuring: Create a centralized system to store data consistently. This may require the adoption of new software or the updating of existing ones. 3. Reliability testing: Run simulations to verify that the data is robust enough for AI. If the results aren't satisfactory, invest in training or analytics tools to improve the quality of your data.

…