Toward a Risk Assessment Framework for Institutional DeFi: A Nine-Dimension Approach
Eva Oberholzer, Valeriy Zamaraiev
TLDR
This paper introduces a nine-dimension risk assessment framework for institutional DeFi, addressing gaps in existing methodologies with novel risk dimensions.
Key contributions
- Proposes a nine-dimension risk assessment framework for institutional DeFi, extending existing taxonomies.
- Introduces three novel dimensions: composability risk, comprehension debt, and temporal risk dynamics.
- Adds a transparency confidence modifier to separate assessment reliability from risk severity.
- Validated by analyzing 12 major DeFi incidents, showing novel dimensions' importance for root-cause analysis.
Why it matters
This framework is crucial for institutional adoption of DeFi by providing a rigorous, explainable, and composability-aware risk assessment. It addresses current gaps, offering a more comprehensive approach to understanding and mitigating complex DeFi risks, as demonstrated by its ability to characterize major incidents.
Original Abstract
Decentralized finance (DeFi) protocols now intermediate over USD 100 billion in value, including regulated stablecoins and tokenized assets deployed as collateral, yet no widely adopted framework operationalizes risk assessment at the rigor institutional adoption demands. Existing approaches emphasize protocol-specific parameter optimization or conceptual taxonomies without providing explainable, composability-aware, and structurally independent assessment methodologies. We propose a nine-dimension DeFi risk assessment framework extending the six-dimension taxonomy introduced by Moody's Analytics and Gauntlet with three novel dimensions: composability risk, comprehension debt, and temporal risk dynamics. We additionally introduce a transparency confidence modifier separating assessment reliability from risk severity. The framework is grounded in structural analysis of protocol dependencies conducted through an ontology-based protocol intelligence infrastructure covering more than 8,000 DeFi protocols. We retrospectively analyze 12 major DeFi-related incidents from 2024-2026 representing approximately USD 2.5 billion in direct losses. Five of the 12 incidents require at least one novel dimension for complete root-cause characterization, including the two highest-systemic-impact events in the dataset.
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