Nasdaq Risk Modelling for Catastrophes
Financial risk management software
Insurance analytics software
Financial services software
Insurance software
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What is Nasdaq Risk Modelling for Catastrophes
Nasdaq Risk Modelling for Catastrophes is a catastrophe risk analytics solution used to quantify and manage exposure to natural hazard events such as hurricanes, earthquakes, and floods. It supports insurers, reinsurers, brokers, and risk teams with scenario analysis, portfolio loss estimation, and reporting for underwriting and reinsurance decision-making. The product focuses on model-driven catastrophe loss analytics and workflows that connect hazard, vulnerability, and financial terms to produce loss metrics used in risk management and capital planning.
Catastrophe-specific risk analytics
The product is purpose-built for catastrophe risk modelling rather than general market or credit risk. It supports event-based and scenario-driven analysis that aligns with common insurance and reinsurance workflows. This specialization can reduce the need to adapt broader financial risk platforms to catastrophe-specific data structures and outputs.
Portfolio loss estimation workflows
It is designed to estimate portfolio losses using exposure data, hazard assumptions, and policy/contract financial terms. This supports common outputs such as exceedance probability curves and loss metrics used for underwriting, reinsurance structuring, and accumulation management. The workflow orientation can help standardize analysis across teams compared with ad hoc spreadsheet-based approaches.
Supports risk governance reporting
Catastrophe modelling outputs are commonly used for internal risk committees, capital planning, and regulatory or rating-agency discussions. The product’s focus on repeatable modelling and reporting helps create an audit trail around assumptions and results. This can complement broader enterprise risk tooling by providing catastrophe-specific metrics in a consistent format.
Narrower than enterprise GRC
The product addresses catastrophe risk modelling rather than end-to-end compliance, surveillance, or case management. Organizations typically still need separate systems for AML/KYC, trade surveillance, or broader governance workflows. This can increase integration and data reconciliation work across risk domains.
Data quality and mapping effort
Catastrophe modelling depends heavily on exposure data completeness, geocoding accuracy, and consistent policy/contract terms. Implementations often require significant data preparation, enrichment, and ongoing data governance to keep results reliable. If upstream policy administration or data warehouses are inconsistent, model outputs can be difficult to validate and explain.
Model transparency and assumptions
Catastrophe models can be sensitive to underlying hazard, vulnerability, and secondary uncertainty assumptions. Users may need specialist expertise to interpret results, compare model versions, and communicate limitations to stakeholders. Where model components are not fully transparent, it can be harder to perform independent validation or meet internal model risk management standards.
Seller details
Nasdaq, Inc.
New York, NY, USA
1971
Public
https://www.nasdaq.com/
https://x.com/Nasdaq
https://www.linkedin.com/company/nasdaq/
