The question we get asked most frequently by our clients goes something like this:
“Things are evolving so fast in the world of data and technology, what should we be doing?”
The truth is, that while there are plenty of ways to work smarter with AI, the first place to start is to challenge the assumptions of the way things should be done.
Specifically, the idea that to grow, you need more people and more expensive technology.
So how are the most successful firms breaking this pattern?
What’s the alternative to the endless hiring cycle?
The reinsurance ecosystem approach
Most reinsurers’ functions still operate as if data starts and ends in spreadsheets. Month-end close processes involve dozens of manual touchpoints, creating delays, errors, and control gaps.
We know from our work that the most innovative reinsurers are taking a different approach – targeting the connective tissue between their existing systems and redesigning how data flows through their organisations. This changes everything.
By mapping how information needs to move between functions, you can identify the right integration points before even making technology decisions. Saving huge amounts of time and money
This is what we call a Reinsurance Ecosystem – an integrated environment where data, systems, and workflows function as a coherent whole rather than disconnected parts.
Instead of treating each book as a unique project requiring bespoke processes, build standardised, templated workflows that accommodate variations within a consistent framework:
- Treaty onboarding workflows as modular components that can be reconfigured rather than rebuilt
- Well-established data sources with automated feeds from all key cornerstone systems (e.g. liability cash flows, ABOR/IBOR asset data)
- Reporting frameworks with common structures that extend for specific business needs
Critically, this approach doesn’t sacrifice flexibility for efficiency – it’s designed to build upon, instead of having to create something new from scratch each time.
Resolution Re case study
Resolution Re is a great case in point.
They’ve grown from 1 to 7 books in under 4 years while maintaining a team of approximately 50 people – far below what traditional scaling should require.
Their journey began with a clear vision:
Build operational architecture that could accommodate growth without proportional headcount increases.
We supported them in mapping their target operating model and core data flows that would need to scale. Their approach centred on three pillars:
1 – A central data ecosystem that eliminated functional silos
Instead of separate systems for actuarial, finance, investments, and risk, they created a unified data architecture with standardised interfaces. Their data lake stores over 2.2 million data points, creating a single source of truth.
2 – Automated key processes
Resolution Re’s settlement statement process, which once took days of manual effort, now completes in minutes. With 80% of other core business processes automated, they’ve reduced key person risk while enabling scale without workforce growth.
3 – Reusable components rather than one-off solutions
Dashboards, integration patterns, and control frameworks are designed for extensibility, enabling rapid adaptation to new books without rebuilding core processes.
The result?
Resolution Re reduced their onboarding timeline to just 3 months and dramatically reduced the need to increase headcount.
Along the way, they’ve unlocked other business results, like improving cash flow by approximately $2m per month through machine learning models for lapse prediction and hedge factor optimization.
But the benefits extend beyond faster onboarding – these systems make books easier to manage after onboarding, creating a compounding efficiency effect as the portfolio grows.
Our partnership with Toucanberry has been a key enabler of our growth. We now operate with thoughtfully designed processes and a robust technology ecosystem that gives us the confidence to onboard new books of business at scale. I’m especially proud of how we’ve already begun harnessing AI and machine learning — not as future ambitions, but as tools we’re actively leveraging to drive smarter, faster decision-making across the business. – Chioke Lodge , Head of Operations | Data & Reinsurance Technology, Resolution Re
Practical steps to scale without adding headcount
So, your path forward starts with mapping your current operational workflows.
Not just systems, but how data and decisions actually flow through your organisation.
Next, build standardised data interfaces between functional groups. Rather than forcing teams to abandon their specialised tools, create structured data flows that enable seamless information exchange. The ecosystem should augment your team’s capabilities and extend their work, not burden them with new things to learn.
Finally, start with one high-impact process to demonstrate the approach. Treaty onboarding, settlement statements, or valuation workflows often offer the clearest path to demonstrating value.
Future positioning
With clean, structured data flows and standardised processes, you can more easily implement advanced capabilities like predictive analytics and automated reporting.
But these technologies require a solid operational foundation – they can’t simply be layered on top of fragmented processes.
So, if you want to gain a competitive advantage, don’t necessarily think about how you can implement the latest AI tool.
While we support the use of AI when it’s done right – more on some exciting opportunities this is creating for our clients over the coming weeks – it’s better to solve the root causes first. Systematically eliminate the operational friction that will slow your growth. Rethink how work flows across functions, not just how individual teams operate.
Have you found a way to sustainably scale?
Or are you struggling with this exact challenge?
We’d be interested to hear your experiences, so please reach out.