Artificial intelligence has become a fixture of insurance conference agendas. At Swiss Re, however, transformation is not being framed as experimentation or digital ambition. It is being treated as operating model redesign.
For Pravina Ladva (pictured), group chief digital and technology officer, the distinction matters. “We are not implementing AI for AI’s sake,” she said. “We’re really trying to understand how we’re going to drive value from it.”
In a reinsurance business defined by risk selection, capital discipline and long-cycle returns, that value must be operational, measurable and embedded into the mechanics of underwriting and claims.
Ladva describes a two-pronged approach. The first focuses on core insurance processes - underwriting and claims, where vast volumes of structured and unstructured data have historically been underutilised.
“One of the things we have in the insurance industry is a bucket load of data,” she said. The challenge has always been extracting actionable insight from it at scale.
Advances in AI now allow Swiss Re to analyse unstructured material in ways that were previously impractical. In its Corporate Solutions business, an AI-powered platform streamlines the processing of over 40,000 claims annually, accelerating triage and improving decision-making.
The impact is not limited to speed. Faster payouts improve client experience at what Ladva calls the “moment of truth.” At the same time, improved data analysis supports earlier fraud detection and frees claims professionals to focus on complex, high-value cases.
But the deeper transformation lies in questioning the structure of the process itself. “If we are going to have this new capability, do we necessarily need a 20-step process, or can it be done in 10?” she said.
Rather than layering AI onto legacy workflows, Swiss Re is reassessing end-to-end processes from a business perspective. That shift, from automation to simplification, is central to how the organisation defines transformation.
Operationalising innovation also requires clarity about what to build and what to buy. “As an insurer, we are definitely not a tech company,” Ladva said. “The last thing I want to do is build tech that I can get from experts who can do it much better than we can ourselves.”
Swiss Re works with multiple external providers and remains deliberately platform agnostic. Diversifying across solutions reduces concentration risk and allows the business to match tools to specific use cases.
At the same time, Ladva is clear that transformation cannot be outsourced. While software may be delivered through partners, integration, governance and domain expertise must reside internally.
The company is therefore strengthening in-house capabilities in AI, data and transformation. “To bring that into our own systems and integrate it, we need to have our own know-how and our own SMEs,” she said.
This hybrid model of diversified external partnerships combined with internal expertise reflects a broader shift in how insurers are operationalising innovation: not as isolated pilots, but as embedded capability.
If integration into legacy systems is the visible challenge, cultural adoption is the invisible one. “It’s very easy to do a proof of concept when you’ve not got it hooked into anything,” Ladva said. Scaling enterprise-wide requires engineering discipline. But technical integration, she argues, is the more solvable problem.
The harder issue is behavioural change. “I could build the best platform, but if no one’s going to use it, it’s not going to drive value for the company.”
For underwriters and claims handlers accustomed to established processes, change must be tangible. Swiss Re addresses this through early engagement and practical proof of concepts that demonstrate real workflow improvements rather than theoretical promise.
Governance is equally embedded. Human oversight remains in the loop across underwriting and claims, supported by internal AI governance frameworks and compliance with diverse legal regimes across global markets.
“For us, it’s intrinsically important that the human is in the loop,” Ladva said.
Transformation, in this context, is not a standalone technology initiative. It is the alignment of engineering, process redesign, regulatory trust and workforce enablement.
The programme is backed at the highest level. Ladva describes it as the number one priority within the group’s “Built to Lead” strategy, sponsored by the CEO and supported across operations, risk, legal, compliance and HR functions.
Years of investment in data foundations have positioned the organisation to move from preparation to execution. “We have the right data foundations in place, and we are now AI-ready because we know where our data is and we have it in a common platform,” she said.
Crucially, every initiative must justify itself. Each project is assessed through formal business cases with defined financial and operational metrics. Some use cases, such as claims enhancements, deliver faster returns. Others may take four to six years. Intermediate milestones act as checkpoints to accelerate, redirect or halt investment.
For an industry built on risk pricing and capital discipline, that approach may be the clearest signal of transformation maturity. Innovation is no longer conceptual. It is being embedded into underwriting performance, operational scale and portfolio governance.
The next phase, Ladva suggests, is less about proving the technology works and more about ensuring the organisation evolves with it. The insurers that succeed will not simply deploy AI. They will redesign how risk work gets done, and measure its impact with the same rigour they apply to their balance sheets.
