The Global Risks Alliance (GRA)
A claim is the moment an insurance promise is tested. Paying claims accurately and quickly depends on people, data, systems, suppliers and the control of fraud, and all of them come under strain at once after a catastrophe or a cyber incident. This page sets out how claims and operational risk develop, how they are measured, and which institutions decide on continuity, fraud control and customer outcomes.
GRA · Established · service level R0
Claims handling is the process by which an insurer receives, assesses and settles claims, the moment its promise is tested. Operational resilience is the ability to keep important business services such as claims running within impact tolerances through disruption, from catastrophe surges to cyber incidents and supplier failures.
How satellite radar turns a flood into an exposure update. Synthetic scene; no real event or place.
Continuity, fraud and claims systems. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
A single event can produce more claims in days than a normal year. Adjuster capacity, contractor supply and access to damaged areas set the pace, and delays raise both costs and customer harm.
Policy, claims and payment systems depend on cloud providers, software vendors, outsourced administrators and repair networks. A cyber incident or vendor failure can halt claims payment across a market.
Opportunistic exaggeration, organised fraud rings and internal fraud raise costs for all policyholders. Detection that is too aggressive delays or denies legitimate claims and harms customers.
Coverage ambiguity, underinsurance and slow settlement turn a loss into a dispute. Complaints, ombudsman decisions and litigation feed back into wordings, conduct supervision and trust.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Operational resilience | A firm's ability to prevent, adapt to, respond to, recover from and learn from operational disruption while keeping important business services running. |
| Impact tolerance | The maximum tolerable level of disruption to an important business service, set by the firm and used in supervisory resilience regimes. |
| Claims leakage | The gap between what a claim should have cost and what was paid, through errors, process failures or undetected fraud. |
| Ombudsman | An independent scheme that resolves complaints between insurers and customers, whose decisions are binding on the insurer in many jurisdictions. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Claims settlement time | days from notification to payment | monthly | Insurer claims systems | REPORTED |
| Catastrophe claims count per event | claims | per event | Industry loss estimation services and insurer reports | ESTIMATED |
| Complaints ratio | complaints per thousand policies | annual | Supervisory complaint data | OFFICIAL |
| Ombudsman uphold rate | percent of decided complaints upheld | annual | Ombudsman decisions | OFFICIAL |
| Detected fraud | count and value of detected fraudulent claims | annual | Industry fraud bodies | REPORTED |
| Operational loss events | count and value by event type | annual | Operational risk loss databases | REPORTED |
| Recovery time against impact tolerance | hours | per incident and per test | Operational resilience self-assessments | DECLARED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| NAIC consumer complaint data | Closed complaints by insurer, line and reason in the United States | Public |
| Financial Ombudsman Service decisions (United Kingdom) | Published decisions and complaint data on insurance disputes | Public, Open Government Licence |
| EIOPA consumer trends report | Claims handling, complaints and conduct issues across EU markets | Public, EU reuse terms |
| ORX operational risk loss data | Pooled operational loss events from member insurers and banks | Proprietary, members only |
| Insurance Fraud Bureau and national fraud bodies | Organised fraud intelligence and detected fraud trends | Restricted; summaries public |
| ENISA threat landscape reports | Cyber threat trends affecting the financial sector in Europe | Public, attribution |
Insurance Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Insurer boards and senior managers | Which important business services must stay within impact tolerance, and what investment that requires | Service mapping, dependency evidence and scenario test results against tolerance |
| Conduct and prudential supervisors | Whether claims handling and operational resilience meet rules on fair treatment and continuity | Complaint data, settlement times, incident reports and test evidence |
| Claims directors and special investigation units | How to balance fraud detection against fair and fast settlement | Detection rates shown with false positive and appeal outcomes |
| Ombudsman services and courts | How policy wordings should be read in disputed claims | Wordings, claims handling records and comparable decisions |
Catastrophes create claim surges that can overwhelm adjusters, contractors and systems, and the communities affected may include insurers' own staff. Delays slow recovery and erode trust. Insurers rely increasingly on digital systems and third parties, including cloud providers, data vendors and outsourced adjusters, which creates concentrations of operational risk. Regulators in several jurisdictions have strengthened requirements for operational resilience.
Fraud, from exaggerated claims to organized schemes, raises costs for honest policyholders. Detection relies on data analytics and investigation, but aggressive controls can delay legitimate claims. Parametric products, which pay on measured event parameters rather than assessed loss, can speed settlement but introduce basis risk, the difference between the amount paid and the loss suffered.
Studying claims workflows and bottlenecks after past events.
Mapping critical services and dependencies, drawing on the practice of Financial Cyber and Operational Resilience.
Statistical and machine-learning methods from artificial intelligence and machine learning detect anomalies, with governance of their use.
Operational data is confidential, fraud is hard to measure, and analytic tools can produce false positives that harm legitimate claimants.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.