AI ANALYSIS FOR DeFi Risk Analytics Inc.
Unlocking Systemic Stability in Decentralized Finance with DeXposure-FM
DeXposure-FM is the first time-series, graph foundation model for decentralized finance (DeFi), designed to measure and forecast inter-protocol credit exposure and systemic stability. It leverages a graph-tabular encoder with pre-trained weights and multi-task heads, trained on a massive dataset of 43.7 million entries across 4,300+ protocols and 602 blockchains. The model significantly outperforms state-of-the-art competitors in multi-step forecasting of edge-level exposures and network-level statistics like concentration and sector-to-sector links. Crucially, DeXposure-FM provides advanced financial economics tools for macroprudential monitoring and scenario-based DeFi stress testing, enabling precise measurement of protocol-level systemic-importance, sector-level spillover, and concentration. Its open-source availability ensures transparency and reproducibility for future developments.
Executive Impact for Chief Risk Officer
Key Challenges for a Chief Risk Officer
Lack of real-time, comprehensive tools for monitoring systemic risk in DeFi.
Difficulty in forecasting inter-protocol credit exposures and contagion effects.
Inability to perform scenario-based stress testing for DeFi networks due to data and model limitations.
Challenges in quantifying protocol-level systemic importance and sector-level spillover.
Need for transparent and reproducible models for regulatory reporting and internal risk management.
How DeXposure-FM Transforms Risk Management
A time-series, graph foundation model (DeXposure-FM) for measuring and forecasting DeFi credit exposures.
Multi-step forecasting capabilities for edge-level exposures and network-level statistics.
Macroprudential monitoring tools, including protocol-level systemic-importance scores, sector-level spillover, and concentration measures.
Scenario-based DeFi stress testing functionality with a forecast-then-measure pipeline.
Open-source model and code for transparency, reproducibility, and community-driven development.
Significant
Outperformance in Forecasting
4,300+
Protocols Covered
43.7M
Data Entries
12 weeks
Prediction Horizons
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
DeXposure-FM provides unparalleled insights into the intricate web of inter-protocol dependencies in DeFi, enabling early detection and forecasting of systemic risk and contagion pathways. It identifies which parts of the network amplify shocks and along which pathways distress travels, crucial for macroprudential monitoring.
Empirical validation shows DeXposure-FM significantly outperforms strong competitors, including graph foundation models and temporal graph neural networks, in multi-step forecasts of edge-level exposures and network-level statistics like concentration, density, and sector-to-sector links. Its ability to predict future network states with high accuracy is a game-changer.
The model generates practical financial economics tools, such as protocol-level systemic-importance scores, sector-level spillover, and concentration measures, all integrated into a forecast-then-measure pipeline. These tools empower institutions to conduct scenario-based DeFi stress testing and derive actionable signals for risk management.
With both the model and code publicly available, DeXposure-FM sets a new standard for transparency and reproducibility in decentralized finance research and application. This commitment fosters community-driven innovation and ensures that the measurement process is robust and verifiable.
98.5%
Mean Edge Overlap Ratio (DeFi Network Stability)
The DeXposure dataset reveals a high mean edge overlap ratio, indicating significant week-to-week stability in DeFi protocol interconnections. This highlights the foundational persistence of credit relationships, yet also underscores the importance of detecting critical shifts during turbulent regimes. DeXposure-FM excels particularly in these 'tail' events, where the network structure deviates most from the previous week, adding significant value over persistence baselines.
| Metric | DeXposure-FM (AUROC) | SOTA Competitor (AUROC) |
|---|---|---|
| Edge Existence (1 week) | 0.995 | 0.988 |
| Edge Existence (4 weeks) | 0.995 | 0.988 |
| Edge Existence (8 weeks) | 0.994 | 0.987 |
| Edge Existence (12 weeks) | 0.993 | 0.986 |
Across all forecast horizons, DeXposure-FM consistently demonstrates superior performance in predicting edge existence compared to state-of-the-art competitors like GraphPFN. This empirical validation underscores the model's robustness and accuracy in forecasting crucial network-level dynamics, providing a reliable foundation for systemic risk assessment.
Enterprise Process Flow
User stakes ETH with Lido
→
Receives stETH (Lido's liability)
→
Wraps stETH into wstETH (Wrapper's liability)
→
Deposits wstETH into Pendle (Pendle's liability)
→
Creates chain of credit exposures: Pendle to Wrapper to Lido
This multi-layer credit exposure chain illustrates how DeXposure-FM captures token-mediated dependencies in DeFi. By tracking these intricate relationships, the model can identify how shocks, such as a de-pegging event or a slashing incident, would cascade through the network, affecting various protocols and token holders. Understanding these pathways is critical for effective macroprudential monitoring and stress testing.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could realize by implementing DeXposure-FM.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Implementation Roadmap
A phased approach to integrating DeXposure-FM into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Data Pool Expansion
Enlarging the training data to include CEX balances, OTC derivatives, and off-chain stablecoin reserves, along with higher-frequency data (daily/intraday).
Phase 2: Proxy Refinement
Moving beyond raw TVL by incorporating asset-level risk weights, liquidity/volatility proxies, depeg risk, and collateral rules for risk-adjusted exposure estimates.
Phase 3: Model Architecture Innovation
Integrating diffusion-based generative modeling with multi-horizon forecasting for improved network trajectory predictions under stress.
Phase 4: Open-Source Community Development
Fostering an open-source community, organizing benchmarking competitions, and maintaining public code/model weights for continuous improvement.
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