The Global Risks Alliance (GRA)
Every disaster finance decision rests on evidence: risk models, loss data, exposure, forecast skill and evaluations of past instruments. Diligence asks whether that evidence is fit for the decision, who produced it and what it leaves out. This page sets out the evidence chain behind disaster risk finance, the questions diligence asks, the data sources with their licences, and who decides what counts.
GRA · In development · service level R0
Disaster finance diligence is the review of the evidence behind a financing decision: risk models, exposure and loss data, forecast skill and evaluations of past instruments. It asks who produced the evidence and what it leaves out. Finance ministries, donors, risk managers and evaluation offices decide what counts.
How a dry season is read against the point where drought cover starts to pay. Synthetic season and design terms.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Financing evidence and diligence · Sovereign C · Season 2
| Attachment | 68 | 0 | Close |
| Mid | 85 | 54 | Not met |
| Exhaustion | 93 | 100 | Not met |
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Independent reviewer information needs. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Parametric triggers, insurance prices and layer sizes depend on catastrophe and forecast models. Their assumptions are often proprietary and hard for buyers to check.
Exposure and loss data are thin in low-income countries. Models calibrated elsewhere may misjudge local vulnerability.
Buyers often rely on the seller's or broker's analysis. Independent review of models and terms protects buyers but adds cost and time.
Evaluations of payouts, timeliness and outcomes show whether instruments worked. Without them, the same designs repeat regardless of results.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Due diligence | A structured review of evidence and terms before a financial commitment. |
| Open risk model | A risk model whose components and assumptions are published for inspection and reuse. |
| Risk Data Library | An open data standard for hazard, exposure, vulnerability and loss information. |
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 |
|---|---|---|---|---|
| Instruments with independent model review | share of transactions | periodic | Programme records | REPORTED |
| Open risk models available for a country | count | on change | Open model registries | OBSERVED |
| Exposure data completeness for public assets | share of assets with location and value | periodic | Asset registers | OBSERVED |
| Published evaluations of disaster risk finance instruments | count | annual | Evaluation libraries | REPORTED |
| Forecast skill for trigger variables | verification score | periodic | Forecast verification | ESTIMATED |
| Disclosure of model assumptions to buyers | categorical | per transaction | Transaction documents | 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 |
|---|---|---|
| Insurance Development Forum Risk Modelling Steering Group tools | Open risk modelling resources | Free to read, terms to check |
| Oasis Loss Modelling Framework | Open platform for catastrophe models | Open source, BSD licence |
| Centre for Disaster Protection evaluations and guidance | Evidence reviews and diligence guidance | Free to read, terms to check |
| Risk Data Library standard | Open data schema for hazard, exposure, vulnerability and loss | Open, terms to check |
| World Bank and partner evaluation reports | Evaluations of disaster risk finance programmes | Free, World Bank terms |
Disaster Risk Finance 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 |
|---|---|---|
| Finance ministries and buyers | Whether evidence supports a purchase and its terms | Model documentation, independent review and evaluations |
| Donors paying premiums | Whether to fund premiums and on what evidence | Diligence findings and outcome evaluations |
| Pool and insurer risk managers | Which models and data to use | Validation and comparison evidence |
| Evaluation offices | Which instruments to evaluate and how | Programme records and evaluation standards |
Reviewers, whether credit committees at development institutions, underwriters, investors or public audit bodies, typically examine several areas. Risk evidence covers hazard data, exposure, vulnerability and modelled losses, with their sources and uncertainty. Operational evidence covers plans, delivery systems and institutional capacity. Governance evidence covers legal authority, decision rules, safeguards and accountability. Financial evidence covers fiscal position, existing arrangements and how a new arrangement would fit.
Evidence quality matters more than volume. Reviewers look for documented methods, traceable sources, independent validation and clear statements of what is not known. Overstated certainty undermines trust; honest uncertainty allows reviewers to price or plan for it.
A disaster finance dossier, as GRA designs it, assembles such evidence as research for independent reviewers. It does not recommend a transaction, assess creditworthiness or certify readiness. Readiness is not approval: the decision remains with the reviewer and the competent authority.
Distinguishing observed data, modelled estimates and expert judgement.
Review of models and data by parties not involved in producing them.
Recording sources, versions and transformations so evidence can be checked.
Limits: evidence can reduce uncertainty but not remove it, and some information reviewers want, such as future political responses, cannot be known.
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.