Computing Power Token Loan Embedded Credit Model

📌 Background

After computing power became a core production factor of the digital economy, the upstream and downstream of computing power operating enterprises have long faced insufficient collateral and financing difficulties. In 2026, Bank of China launched the country's first Computing Power Token Loan, and Agricultural Bank of China, China CITIC Bank, and others quickly followed, using computing power Token transaction data instead of traditional financial statements as the basis for credit assessment, moving computing power finance from concept to application-layer enterprises.

👤 Target Customers

Intelligent computing center operating enterprises, computing power service purchasers, upstream and downstream suppliers in the computing power industry chain

💰 Revenue Streams

1) Banks earn interest spread income based on loan scale; 2) Platforms charge technical service fees based on the number of API calls; 3) Risk control data model licensing fees.

🧮 Cost Structure

Costs of computing power transaction data collection and risk control model R&D; bank funding costs; compliance regulation and data security system operation and maintenance

🛡️ Moat

First-mover positioning advantage and bank-side computing power risk control model barriers; closed-loop Token transaction data creates network effects

🔑 Keys to Success

• Open up the Token transaction data channel between computing power platforms and banks
• Establish bank-recognized computing power asset valuation and risk control standards

⚠️ Risks

• Sharp fluctuations in computing power prices expand credit risk exposure
• Insufficient standardization of Token transaction data affects cross-bank promotion

🏢 Cases

• Bank of China launched the country's first Computing Power Token Loan
• Agricultural Bank of China and China CITIC Bank followed by launching Token transaction loan products

📊 SWOT Analysis