riskthinking.AI Inc.

CDTexpress™, VELO App®, VELP Data®, Rankings

riskthinking.AI's VELO combines a large, bias-corrected global climate dataset (1850–2100, dozens of physical and transition variables) with an extensive physical-asset database in a single digital-twin platform. Rather than a few fixed pathways, it draws on a wide ensemble of climate projections to hold stochastic distributions per hazard, location and horizon, and applies a patented algorithm to generate multi-factor stress scenarios that combine several hazards at once. From these it derives Climate Earnings-at-Risk and Value-at-Risk at asset, company and portfolio level, supporting uses from ISSB S2 reporting to climate-impact equity research. Distinctive for its fully stochastic, multi-hazard treatment of compounding physical and transition risk.

Vendor methodology

Transparency Score

Beta

Public transparency, not model quality iThe Transparency Score (0–3) estimates public methodological transparency: the degree to which the vendor's analytical approach to modeling climate and nature risk can be assessed from publicly available sources. It is explicitly not a measure of vendor quality or accuracy.

2

Methodology-relevant: vendor websites or external resources explain methodological details.

Methodology-relevant: a public method paper (Dembo, 'Generating scenarios algorithmically', 2020) sets out the algorithmic multi-factor scenario generation — a scenario-tree construction that needs no prior distributional assumptions and provably spans best and worst cases — and a founder's overview describes the Climate Digital Twin (stochastic pathways, multi-hazard, all IPCC scenarios). The approach is followable; the operational pipeline stays proprietary.

For further information, see the section 'Transparency Score methodology' in the Readme.

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Level 1 signals