Workshop Description
Quantum Amplitude Estimation (QAE) offers a theoretical quadratic speed-up for Monte Carlo simulation, the computational backbone of catastrophe modelling, reserve calculation, and pricing. QAOA (Quantum Approximate Optimisation Algorithm) addresses combinatorial problems in reinsurance treaty structuring and capital allocation. Quantum-enhanced machine learning shows early results in anomaly detection on claims datasets. None of these are production-ready today. A responsible executive briefing separates the credible near-term pilots from the speculative, and gives leaders a framework for evaluating vendor claims.
The security side is more urgent. Harvest-now-decrypt-later attacks mean that policyholder data encrypted today with RSA or ECDSA may be decrypted within the policy term of a long-tail liability product. Solvency II ORSA already requires insurers to assess material risks to solvency. PRA SS2/21 requires identification of important business services and their dependencies, which includes cryptographic infrastructure. This briefing connects both sides into a single strategic framework.
What participants cover
- Quantum Amplitude Estimation for Monte Carlo pricing: what the quadratic speed-up means for catastrophe model run times and where current hardware falls short
- QAOA for reinsurance portfolio optimisation: formulating treaty structures as combinatorial problems
- Quantum machine learning for fraud detection: QSVM anomaly scoring, quantum graph algorithms for organised fraud rings
- Harvest-now-decrypt-later risk assessment: which policyholder data classes face exposure within policy term horizons
- Regulatory obligations: Solvency II ORSA quantum risk integration, PRA SS2/21 cryptographic dependency mapping, Lloyd's Y5381 cyber governance
- Investment framework: quantum R&D pilot selection, PQC migration budgeting, build vs buy vs partner decision criteria