Sectors we serve
Database integration, Monte Carlo simulation for default probability, and one enterprise-wide picture of risk - built inside your environment and handed over for your team to own.
What we do
We bring the quantitative rig of commodity risk management to healthcare and life insurance - from unified data to simulated default probabilities and board-grade reporting.
Policy administration, claims, underwriting, reinsurance and finance assimilated into one governed layer - documented lineage, enforced quality controls, one version of the truth.
Probability of default and recovery simulated across reinsurers, counterparties and issuers - capital sized on the whole distribution, not a single point estimate.
Experience analysis against your own portfolio data, so assumptions move with emerging claims behaviour rather than stale industry tables.
Emerging trends visible in days, not quarters - with anomaly detection on high-cost claims and portfolio concentration.
Pandemic, lapse, credit-spread and counterparty-failure scenarios with P&L and capital impact side by side.
One consolidated view of capital, claims and exposure that leadership, auditors and regulators can all read.
Why QD-Mjolnir
Assimilate, model, decide - the architecture we build for commodity houses, pointed at policy, claims and capital.
Your policy admin and claims systems keep running. We integrate them into one governed layer instead of migrating them.
Built in your environment, documented for your auditor, and run by your team when we leave.
The Monte Carlo core
Monte Carlo simulation replaces single-point guesses with thousands of simulated futures - a full probability distribution you can size capital, reserves and decisions against. It is the same engine discipline we apply on trading desks, pointed at the questions that matter in healthcare and insurance.
Aggregate claims outcomes simulated thousands of times over - reserve ranges, tail probabilities and reinsurance attachment points read off the whole distribution, not one central estimate.
Distribution in - distribution outTrial scenarios - enrolment, dropout, effect size, cost - simulated before the study runs, putting a probability on trial success before capital is committed across a pipeline.
Go / no-go with the odds statedProbability of specific clinical events - recurrence, readmission, high-cost episodes - estimated from your own portfolio data, so risk is stratified by evidence rather than averages.
Who is actually at riskAsset, lapse and claims shocks propagated through thousands of forward scenarios - solvency and funding headroom seen years ahead, not at the last valuation date.
Risk evolution, forecastA 30-minute introductory call. We'll walk through your current risk and data setup, and show you where a unified view would take you first. No obligation.