Your first Quantum “Use Case” : From Capital Drag to Capital Edge
As AI redefines how we process information, there’s a quieter shift underway that will help us solve the unsolvable.
Quantum computing isn’t faster AI. It’s a a paradigm shift – a fundamentally different way to compute, based on quantum mechanics. While the tech is still 7 to 10 years from large-scale impact, it’s not too early to prepare.
How does this impact the insurance industry?
The Risk Angle: Quantum can break today’s encryption standards, putting data, systems, and digital assets at risk – “Harvest now, decrypt later” is real, and Quantum-safe cryptography must be part of our roadmap before the risk materializes.
But what about opportunity? Can we look at Quantum through an innovation lens, to list Quantum “use cases” for Insurance?
While Quantum ML will most certainly improve pattern recognition in multi-dimensional data (say, detecting fraud or anomalies), we don’t know enough to pinpoint which problems QML will consistently outperform classical ML on.
Optimisation problems would be a better bet, although not every optimisation problem would get quantum acceleration. At our current stage of visibility, it is best to focus on high-complexity problems where classical methods are slow or stuck. We’re looking for combinatorial optimisation problems, involving thousands of variables, rules and constraints, that currently use heuristics or linear approximations (say, greedy algorithms with local optima, scenario sampling).
An obvious candidate is the field of portfolio and capital optimisation – such as reinsurance structuring, asset-liability matching, capital allocation. Treaty design, for example, is a high-dimensional problem involving loss distributions, capital constraints, regulatory rules, and counterparty interactions, typically solved using simulation + heuristics.
With Quantum, we can evaluate exponentially more treaty configurations simultaneously, optimise for global objectives (such as capital relief, risk correlation, reinsurer appetite), optimise placement (which reinsurer takes which layer) and pricing, solve for true global optima across hundreds of interdependent variables (duration, yield, risk capital, liquidity), take into account volatile environments (macro-economic, climate, cyber shifts) and incorporate real-world constraints and jurisdiction-specific regulatory capital formulas.
True optimal solutions in real time may become feasible – changing how insurers manage balance sheets.
The upside? Potential to materially reduce capital drag and over-collateralisation, and improve RoE – especially in complex multi-line, multi-jurisdiction portfolios. Better reserves, better capital efficiency.
How do you prepare today’s data for tomorrow’s Quantum Optimisation Use Case?
1. Decisions, not transactions : Our current world is transactional, our future world is about solving decision problems – we need to log data differently. Rather than just storing which reinsurance treaty was chosen, also log the available alternatives, why one was chosen, and the constraints in play.
2. Capture constraints and dependencies with time-linkages : Start capturing constraints like capacity limits and exclusions, how they change with time, and why.
3. Archive complexity: Stop flattening data to store ‘snapshots’, preserve the complexity in data archival.
