# Can On-Chain Data Be Used to More Accurately Model the Probability of Default for Crypto-Native Firms? ▴ Question

**Published:** 2025-08-10
**Categories:** Question

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## Concept

The examination of a crypto-native firm’s viability introduces a direct challenge to legacy frameworks of risk assessment. [Traditional credit](https://prime.greeks.live/question/how-does-the-isda-credit-support-annex-function-to-reduce-daily-credit-exposure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) analysis, built upon periodic financial statements and qualitative management assessments, operates on a latency that is fundamentally misaligned with the real-time, fluid nature of the digital asset economy. The core question is not whether on-chain data can be used to model [default probability](https://prime.greeks.live/question/what-are-the-primary-differences-between-a-ccp-default-and-a-bilateral-counterparty-default/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), but rather, how its structural transparency fundamentally redefines the nature of credit analysis itself. It provides a verifiable, high-frequency ledger of economic activity, moving risk assessment from a practice of forensic accounting to one of systems analysis.

For [crypto-native firms](https://prime.greeks.live/area/crypto-native-firms/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) ▴ a category encompassing everything from decentralized finance (DeFi) protocols governed by smart contracts to centralized exchanges (CeFi) with on-chain treasury reserves ▴ the blockchain ledger is the ultimate source of truth. It is the real-time cash flow statement, the balance sheet, and the record of operational cadence. Analyzing this data allows for a direct observation of the financial health and operational integrity of an entity, bypassing the curated and often delayed disclosures of traditional finance. This represents a systemic shift from assessing a narrative to analyzing verifiable facts on a public infrastructure.

> On-chain data offers a direct, real-time observation of a firm’s financial activities, forming the basis for a more dynamic and verifiable model of default risk.

The utility of this data is rooted in its granularity and immutability. Every transaction, every interaction with a smart contract, every transfer of assets is recorded and verifiable. This allows for the construction of novel risk indicators that have no parallel in traditional finance.

One can monitor the concentration of assets in a protocol’s treasury, track the flow of funds between related wallets, observe the collateralization levels of lending platforms in real time, and analyze the governance participation of key stakeholders. These are not proxies for financial health; they are direct measurements of it.

This approach moves beyond the static, point-in-time analysis of a balance sheet. It enables a dynamic view of a firm’s liquidity, its operational tempo, and its systemic dependencies within the broader crypto ecosystem. The [probability of default](https://prime.greeks.live/area/probability-of-default/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), in this context, becomes a function of observable on-chain behaviors rather than an inference from opaque, off-chain reporting. The challenge, therefore, lies in developing the analytical frameworks and quantitative models capable of translating this vast, high-dimensional data into a coherent and predictive measure of creditworthiness.

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## Strategy

A strategic framework for modeling the default probability of crypto-native firms using [on-chain data](https://prime.greeks.live/area/on-chain-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) requires a multi-layered approach. It moves from raw data acquisition to sophisticated [feature engineering](https://prime.greeks.live/question/what-is-the-role-of-feature-engineering-in-the-performance-of-illiquidity-prediction-models/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and finally to quantitative modeling. This process is designed to distill the signal from the noise of blockchain transactions, creating a clear, data-driven assessment of credit risk. The objective is to build a system that not only assesses current risk but also provides leading indicators of potential financial distress.

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## Data Sourcing and Feature Engineering

The foundation of any [on-chain credit](https://prime.greeks.live/guide/a-comparative-guide-to-on-chain-vs-off-chain-rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) model is the systematic collection and processing of data. This involves more than simply querying a blockchain explorer; it requires a robust data pipeline capable of ingesting, decoding, and structuring data from multiple blockchain networks. Raw transaction data, while granular, is insufficient on its own. The critical step is feature engineering ▴ transforming this raw data into a set of indicators that correlate with financial health and operational stability.

These features can be grouped into several key categories:

- **Treasury and Asset Management** ▴ This involves the continuous monitoring of a firm’s known wallets. Key metrics include the total value of assets, the composition of those assets (e.g. percentage of stablecoins vs. volatile assets), and the flow of funds in and out of the treasury. A sudden depletion of reserves or a shift towards more speculative assets can be a significant red flag.

- **Protocol Health and User Activity (for DeFi)** ▴ For decentralized applications, metrics such as Total Value Locked (TVL), daily active users, and transaction volume are vital signs of operational health. A sustained decline in TVL or user engagement may indicate a loss of confidence in the protocol, increasing its risk profile.

- **Network and [Counterparty Risk](https://prime.greeks.live/area/counterparty-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)** ▴ Analyzing the transaction graph reveals a firm’s interconnectedness within the crypto ecosystem. This includes its exposure to other protocols, its reliance on specific liquidity pools, and the concentration of its users. A high degree of dependency on a single, potentially unstable, counterparty represents a significant systemic risk.

- **Governance and Tokenomics** ▴ In many crypto-native organizations, governance rights are encoded in tokens. Analyzing the distribution of these tokens, voter participation rates, and the nature of governance proposals can provide insight into the stability and decentralization of the project. A concentration of voting power or contentious governance votes can signal internal instability.

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## Comparing Modeling Paradigms

The adoption of on-chain data necessitates a shift in modeling paradigms. Traditional credit models are built on a foundation of audited, standardized financial data. On-chain models, by contrast, are built on a high-frequency, transparent, but unstructured data stream. The table below outlines the key differences in these approaches.

| Feature | Traditional Credit Model | On-Chain Credit Model |
| --- | --- | --- |
| Primary Data Source | Audited Financial Statements (Quarterly/Annually) | Public Blockchain Ledger (Real-Time) |
| Data Frequency | Low (Point-in-Time) | High (Continuous Stream) |
| Data Transparency | Opaque (Relies on Disclosure) | High (Verifiable by Anyone) |
| Key Metrics | Debt-to-Equity Ratio, EBITDA, Net Income | Treasury Stablecoin Ratio, TVL Volatility, Wallet Activity |
| Risk Indicators | Lagging (Based on Past Performance) | Leading (Based on Real-Time Behavior) |
| Primary Challenge | Data Asymmetry and Reporting Lags | Data Noise and Entity Anonymity |

> The transition to on-chain credit modeling involves exchanging the challenges of information asymmetry for the complexities of high-dimensional data analysis.

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## Quantitative Approaches

With engineered features, various quantitative techniques can be employed to model the probability of default. The choice of model depends on the specific context and the complexity of the data.

- **Heuristic-Based Scoring** ▴ The simplest approach involves creating a scorecard based on a set of predefined metrics. For example, a firm might be assigned a risk score based on its stablecoin reserves, the age of its primary wallets, and its TVL relative to competitors. While easy to implement, this method can be subjective and may miss complex, non-linear relationships in the data.

- **Statistical Models** ▴ More rigorous approaches use statistical models like logistic regression to estimate the probability of default. In this framework, the model learns the relationship between a set of on-chain features and a historical record of default events. The output is a probability score, providing a more nuanced assessment of risk than a simple heuristic.

- **Machine Learning Models** ▴ For capturing highly complex patterns, machine learning models such as Gradient Boosting Machines (GBMs) or Graph Neural Networks (GNNs) can be employed. GBMs are effective at identifying non-linear relationships between features, while GNNs are specifically designed to analyze graph-structured data, making them well-suited for modeling counterparty risk and network effects. These models offer higher predictive power but require more data and computational resources.

The strategic implementation of these models allows for a dynamic and responsive credit assessment framework. By continuously monitoring on-chain data and updating model inputs, it becomes possible to detect early warning signs of financial distress, enabling proactive [risk management](https://prime.greeks.live/area/risk-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) in a way that is unattainable through traditional, backward-looking analysis.

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## Execution

The execution of an on-chain default probability model is a systematic process that translates theoretical strategy into an operational risk management system. This requires a robust technological infrastructure, a clear analytical methodology, and a nuanced understanding of the unique characteristics of the crypto ecosystem. The goal is to construct a system that delivers a reliable, quantifiable, and continuously updated assessment of default risk for crypto-native firms.

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## A Framework for Implementation

Building an effective on-chain [credit risk](https://prime.greeks.live/area/credit-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) model involves a series of well-defined stages, from data acquisition to model deployment and monitoring.

- **Entity Identification and Wallet Tagging** ▴ The initial step is to map real-world crypto-native firms to their on-chain addresses. This is a non-trivial task that often requires a combination of public disclosures, on-chain forensics, and data from specialized providers. Maintaining an accurate and comprehensive database of tagged wallets is foundational to the entire process.

- **Data Ingestion and Structuring** ▴ A scalable data pipeline must be established to pull data from relevant blockchains. This involves running full nodes or using blockchain data providers to access raw transaction, block, and log data. This data must then be decoded from its raw format into a structured database that is optimized for analytical queries.

- **Feature Engineering and Calculation** ▴ This is the core analytical task where raw data is transformed into meaningful risk indicators. A library of features should be developed, covering the categories of [treasury management](https://prime.greeks.live/area/treasury-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), protocol activity, and network risk. These features must be calculated on a regular basis (e.g. daily) to ensure the model reflects the most current information.

- **Model Training and Validation** ▴ Using a historical dataset of crypto-native firms that have defaulted, a predictive model is trained. It is critical to use a robust validation framework, such as out-of-time backtesting, to ensure the model generalizes well to new data. The performance of the model should be measured using standard metrics like the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).

- **Deployment and Real-Time Monitoring** ▴ Once validated, the model is deployed into a production environment. The system should automatically ingest new on-chain data, calculate features, and generate an updated probability of default for each firm on a regular schedule. A dashboard and alerting system should be created to flag firms whose risk profiles are deteriorating.

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## A Simplified Quantitative Model

To illustrate the core concept, consider a simplified logistic regression model for predicting the probability of default for a DeFi protocol. The model could be specified as follows:

P(Default) = 1 / (1 + exp(-(β₀ + β₁ TSR + β₂ TVL_Vol + β₃ Gini_Gov)))

In this model:

- **P(Default)** is the probability of the protocol defaulting within a specified time horizon (e.g. 90 days).

- **TSR (Treasury Stablecoin Ratio)** is the proportion of the protocol’s treasury held in high-quality stablecoins. A higher ratio is expected to be associated with lower risk.

- **TVL_Vol (Total Value Locked Volatility)** is the 30-day volatility of the protocol’s TVL. High volatility can indicate instability or reliance on “mercenary capital.”

- **Gini_Gov (Gini Coefficient of Governance Token)** measures the inequality of governance token distribution. A higher Gini coefficient indicates a concentration of power, which can be a risk factor.

- **β₀, β₁, β₂, β₃** are the coefficients learned from the historical data.

The table below presents hypothetical data that could be used to train such a model.

| Protocol | TSR | TVL_Vol | Gini_Gov | Defaulted (in 90 days) |
| --- | --- | --- | --- | --- |
| ProtoLend | 0.75 | 0.12 | 0.45 | 0 |
| YieldFarmX | 0.15 | 0.65 | 0.89 | 1 |
| StableSwap | 0.92 | 0.08 | 0.32 | 0 |
| LeverageDAO | 0.25 | 0.48 | 0.75 | 1 |
| MoneyMarketZ | 0.60 | 0.25 | 0.55 | 0 |

> By translating on-chain behaviors into quantitative features, a systematic and data-driven approach to credit risk becomes executable.

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## Operational Challenges and Mitigations

The execution of this strategy is not without its challenges. The anonymity of the blockchain can make wallet tagging difficult, and sophisticated actors may attempt to obscure their activities through the use of multiple addresses. Smart contract bugs, oracle failures, and hacks represent unique, technology-driven risk factors that are difficult to incorporate into traditional credit models.

A successful implementation requires a hybrid approach. Quantitative models must be supplemented with qualitative analysis, including smart contract audits, reviews of the development team’s track record, and an assessment of the project’s security posture. The on-chain data provides the “what,” but a deep understanding of the technology and the market is required to understand the “why.” This combination of automated, data-driven analysis and expert human oversight provides the most robust framework for modeling and managing the probability of default for crypto-native firms.

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## References

- Sahu, A. K. & Kumar, T. (2024). On-Chain Credit Risk Score in Decentralized Finance. arXiv preprint arXiv:2404.14815.

- Doerr, S. et al. (2021). DeFi ▴ A new financial system?. BIS Quarterly Review, September.

- CreDA. (2022). CreDA White Paper.

- Packin, N. G. & Lev-Aretz, Y. (2024). On the Credibility of On-Chain Credit Scoring. Fordham Law Review, 92(5).

- Moghe, P. & Johri, Y. (n.d.). Credit Scoring Model. International Journal of Scientific Research in Computer Science, Engineering and Information Technology.

- Credora. (2024). Credora Brings Credit Scores On-Chain.

- Huma Finance. (2025). What is On-chain Credit? A Guide to Decentralized Credit Markets.

- Cedar Rose. (2023). Beyond Cryptocurrency ▴ How Blockchain is Redefining Credit Risk.

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## Reflection

The capacity to model default probability using on-chain data marks a significant evolution in financial risk management. It transforms the assessment of creditworthiness from a periodic, archaeology-like exercise into a dynamic, real-time system of analysis. The transparency of the blockchain provides an unprecedented volume of data, but the true advantage is unlocked by building an operational framework that can translate this data into actionable intelligence. The models and strategies discussed are components of a larger system designed to navigate a new financial landscape.

This approach compels a re-evaluation of where risk originates and how it is measured. The insights derived from on-chain analysis are not merely supplementary; they are foundational. As financial systems become increasingly tokenized and automated, the ability to understand and interpret the language of the blockchain will become a definitive factor in capital allocation and risk mitigation. The ultimate objective is the construction of a resilient, adaptive, and intelligent framework for engaging with the future of finance.

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## Glossary

<a href="https://prime.greeks.live/question/what-are-the-primary-differences-between-a-ccp-default-and-a-bilateral-counterparty-default/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Default Probability

A bilateral default is a contained contractual breach; a CCP default triggers a systemic, mutualized loss allocation protocol. </a><a href="https://prime.greeks.live/question/how-does-the-isda-credit-support-annex-function-to-reduce-daily-credit-exposure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Traditional Credit

The ISDA CSA is a protocol that systematically neutralizes daily credit exposure via the margining of mark-to-market portfolio values. </a><a href="https://prime.greeks.live/area/crypto-native-firms/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Crypto-Native Firms

Meaning ▴ Crypto-Native Firms are entities fundamentally architected from inception around blockchain technology and digital assets, operating primarily within decentralized finance (DeFi) and associated infrastructure, rather than adapting legacy financial models. </a><a href="https://prime.greeks.live/area/probability-of-default/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Probability of Default

Meaning ▴ Probability of Default (PD) represents a statistical quantification of the likelihood that a specific counterparty will fail to meet its contractual financial obligations within a defined future period. </a><a href="https://prime.greeks.live/question/what-is-the-role-of-feature-engineering-in-the-performance-of-illiquidity-prediction-models/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Feature Engineering

Feature engineering translates raw market chaos into the precise language a model needs to predict costly illiquidity events. </a><a href="https://prime.greeks.live/area/on-chain-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## On-Chain Data

Meaning ▴ On-chain data refers to all information permanently recorded and validated on a distributed ledger, encompassing transaction details, smart contract states, and protocol-specific metrics, all cryptographically secured and publicly verifiable. </a><a href="https://prime.greeks.live/guide/a-comparative-guide-to-on-chain-vs-off-chain-rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## On-Chain Credit

Command institutional-grade liquidity. </a><a href="https://prime.greeks.live/area/counterparty-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Counterparty Risk

Meaning ▴ Counterparty risk denotes the potential for financial loss stemming from a counterparty's failure to fulfill its contractual obligations in a transaction. </a><a href="https://prime.greeks.live/area/risk-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Risk Management

Meaning ▴ Risk Management is the systematic process of identifying, assessing, and mitigating potential financial exposures and operational vulnerabilities within an institutional trading framework. </a><a href="https://prime.greeks.live/area/credit-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Credit Risk

Meaning ▴ Credit risk quantifies the potential financial loss arising from a counterparty's failure to fulfill its contractual obligations within a transaction. </a><a href="https://prime.greeks.live/area/treasury-management/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Treasury Management

Meaning ▴ Treasury Management represents the strategic and operational discipline focused on optimizing an organization's liquidity, managing its financial risks, and ensuring capital efficiency within its comprehensive financial architecture. </a>

**Tags:** [DeFi Risk Management](https://options.greeks.live/area/defi-risk-management/), [Counterparty Risk](https://options.greeks.live/area/counterparty-risk/), [Governance Tokens](https://options.greeks.live/area/governance-tokens/), [Treasury Management](https://options.greeks.live/area/treasury-management/), [On-Chain Credit Score](https://options.greeks.live/area/on-chain-credit-score/), [Financial Transparency](https://options.greeks.live/area/financial-transparency/)	

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- 
### [How Do Central Counterparties Influence Block Trade Risk Profiles?](https://options.greeks.live/question/how-do-central-counterparties-influence-block-trade-risk-profiles/)
![Central blue-grey modular components precisely interconnect, flanked by two off-white units. This visualizes an institutional grade RFQ protocol hub, enabling high-fidelity execution and atomic settlement. It optimizes capital efficiency within digital asset derivatives market microstructure.](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-core-interconnect-for-high-fidelity-execution.jpg)

CCPs transform bilateral block trade counterparty risk into a managed, multilateral framework, enhancing capital efficiency and systemic stability.

- 
### [What Are the Most Effective XAI Techniques for Validating Credit Risk Models?](https://options.greeks.live/question/what-are-the-most-effective-xai-techniques-for-validating-credit-risk-models/)
![A precision mechanism, potentially a component of a Crypto Derivatives OS, showcases intricate Market Microstructure for High-Fidelity Execution. Transparent elements suggest Price Discovery and Latent Liquidity within RFQ Protocols. Its robust design implies Capital Efficiency and minimized Slippage for Institutional Grade Digital Asset Derivatives trading.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-crypto-derivatives-rfq-engine-high-fidelity-execution.jpg)

XAI techniques provide the auditable transparency required to validate and trust automated credit risk models in high-frequency crypto trading.

- 
### [Can the ISDA Master Agreement Eliminate All Forms of Counterparty Risk in OTC Derivatives Trading?](https://options.greeks.live/question/can-the-isda-master-agreement-eliminate-all-forms-of-counterparty-risk-in-otc-derivatives-trading/)
![Sharp, intersecting metallic silver, teal, blue, and beige planes converge, illustrating complex liquidity pools and order book dynamics in institutional trading. This form embodies high-fidelity execution and atomic settlement for digital asset derivatives via RFQ protocols, optimized by a Principal's operational framework.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-grade-digital-asset-derivatives-market-microstructure-high-fidelity-execution-across-multi-leg-spread-rfq-protocol-orchestration-within-principals-operational-framework.jpg)

The ISDA Master Agreement provides a critical legal framework to mitigate counterparty risk via netting and collateral, but does not eliminate residual legal, operational, and market-gap exposures.

- 
### [What Is the Role of a CVA Trading Desk in Managing Regulatory Capital?](https://options.greeks.live/question/what-is-the-role-of-a-cva-trading-desk-in-managing-regulatory-capital/)
![A reflective surface supports a sharp metallic element, stabilized by a sphere, alongside translucent teal prisms. This abstractly represents institutional-grade digital asset derivatives RFQ protocol price discovery within a Prime RFQ, emphasizing high-fidelity execution and liquidity pool optimization.](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-prime-os-microstructure-optimization.jpg)

The CVA trading desk is a capital optimization engine that transforms counterparty credit risk into a tradable factor to minimize regulatory capital burdens.

- 
### [What Are the Key Data Sources Required to Build an Effective Counterparty Scoring Model?](https://options.greeks.live/question/what-are-the-key-data-sources-required-to-build-an-effective-counterparty-scoring-model/)
![A dark, precision-engineered module with raised circular elements integrates with a smooth beige housing. It signifies high-fidelity execution for institutional RFQ protocols, ensuring robust price discovery and capital efficiency in digital asset derivatives market microstructure.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-system-core-high-speed-price-discovery-module.jpg)

An effective counterparty scoring model synthesizes financial, market, and operational data into a predictive, dynamic risk signal.

- 
### [What Quantitative Metrics Define Block Trade Settlement Risk Exposure?](https://options.greeks.live/question/what-quantitative-metrics-define-block-trade-settlement-risk-exposure/)
![Intersecting teal and dark blue planes, with reflective metallic lines, depict structured pathways for institutional digital asset derivatives trading. This symbolizes high-fidelity execution, RFQ protocol orchestration, and multi-venue liquidity aggregation within a Prime RFQ, reflecting precise market microstructure and optimal price discovery.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-rfq-execution-liquidity-microstructure.jpg)

Quantifying block trade settlement risk demands dynamic metrics for counterparty exposure, operational efficiency, and collateral adequacy.

- 
### [How Can Quantitative Models for Counterparty Scoring Be Adjusted for Qualitative Relationship Factors?](https://options.greeks.live/question/how-can-quantitative-models-for-counterparty-scoring-be-adjusted-for-qualitative-relationship-factors/)
![A polished, dark teal institutional-grade mechanism reveals an internal beige interface, precisely deploying a metallic, arrow-etched component. This signifies high-fidelity execution within an RFQ protocol, enabling atomic settlement and optimized price discovery for institutional digital asset derivatives and multi-leg spreads, ensuring minimal slippage and robust capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/08/quantitative-finance-driven-rfq-workflow-for-best-execution-in-digital-assets.jpg)

A counterparty's risk is a fusion of its financial capacity and its operational character; a hybrid model quantifies both.

- 
### [How Do Central Clearinghouses Alter the Dynamics of Counterparty Risk?](https://options.greeks.live/question/how-do-central-clearinghouses-alter-the-dynamics-of-counterparty-risk/)
![Abstract RFQ engine, transparent blades symbolize multi-leg spread execution and high-fidelity price discovery. The central hub aggregates deep liquidity pools. Red glows indicate real-time market microstructure dynamics and optimal capital efficiency for institutional digital asset derivatives block trades.](https://options.greeks.live/wp-content/uploads/2025/07/principal-rfq-engine-for-crypto-derivatives-high-fidelity-block-trade.jpg)

Central clearinghouses re-architect counterparty risk by substituting bilateral exposures with a standardized, collateralized hub-and-spoke system.

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---

**Original URL:** https://options.greeks.live/question/can-on-chain-data-be-used-to-more-accurately-model-the-probability-of-default-for-crypto-native-firms/
