DevOps for Insurance — secure, resilient multi-cloud platforms

What regulates the infrastructure

What actually goes wrong here

• Catastrophe-driven claims surges. A single windstorm, flood or earthquake can generate more first-notice-of-loss volume in seventy-two hours than a normal quarter, arriving through call centres, claims portals and adjuster mobile apps simultaneously. The trigger is external and unschedulable, and it lands precisely when regulatory and press scrutiny of the insurer is highest.
• Correlation between the loss event and the insurer's own operations. Because insurance exposure is geographic, the storm generating the claims can be the same storm taking out the regional office, the staff and the connectivity of the team meant to handle them — a failure mode that does not arise in industries whose demand is uncorrelated with their location risk.
• Policy administration systems that cannot be retired on a technology timetable. A whole-of-life or annuity book obliges the insurer to administer contracts written decades ago under their original terms, so data formats and business logic must remain readable and executable for thirty years or more; migration is constrained by contract duration rather than by architecture.
• Failed or slow actuarial and capital calculation runs. Solvency capital, stochastic valuation and IFRS 17 runs are compute-heavy, sit on the critical path to a hard regulatory filing date, and cannot simply be rerun later — a missed window is a reporting breach rather than an inconvenience.
• Renewal and premium-collection batch errors. Renewals, lapse notices and direct debit collections run as large periodic batches; a fault means coverage effective dates, non-renewal notices or collections are wrong, and because coverage is a contractual state rather than a database row, the consequence is legal exposure and unpaid claims, not a data correction.
• Silent loss of distribution through broker and aggregator integrations. Much personal-lines business arrives through comparison sites and broker panels with strict quote timeouts; a degraded quoting service is dropped from the panel and the insurer loses business without receiving an error it can see.

How demand behaves

A steady baseline with two different kinds of peak. Quoting and new business follow the renewal calendar — motor and household renewals cluster around predictable dates and US health plans concentrate on the open enrolment window — while valuation and reporting compute spikes at quarter and year end. Claims volume, by contrast, is exogenous: it is driven by weather and catastrophe events and cannot be scheduled or forecast the way renewal traffic can.

Data you will be holding

Medical records and health questionnaires, claims narratives, driving and criminal history, beneficiary and dependant details, and increasingly telematics and biometric underwriting inputs — much of which is special-category personal data in EU terms and protected health information in the US. Retention is unavoidably long: life and annuity contracts and long-tail liability claims require records to remain retrievable for decades, so erasure requests collide directly with reserving, reinsurance recovery and legal-defence obligations.

Architecture this pushes you toward

Elastic capacity for claims intake and for periodic actuarial and valuation compute, sitting alongside long-lived policy administration platforms that change slowly and cannot be rewritten on a normal modernisation cycle. Unstructured content dominates storage volume — claims photographs, medical reports, surveyor evidence, correspondence — and must stay retrievable for decades under retention and reserving rules. Because catastrophe exposure is geographic, resilience planning has to account for whether operational sites sit in the same peril zone as the insured portfolio.

Availability expectation

Lower continuous-availability pressure than payments in normal operation — most policy and quoting transactions tolerate seconds or minutes rather than milliseconds — but claims intake and quoting must hold up precisely during catastrophe events and enrolment windows, when load is at its highest and public tolerance at its lowest. Under DORA the expectation is now expressed as recovery time and recovery point objectives for named critical functions rather than a single availability figure, so a sector-wide percentage would be misleading.