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AI Supply Chain De-core Financing Platform

1) Transaction service fees of 1%-3% per order or 8%-15% annualized interest rate spread charged to funders; 2) Annual d

MODEL

Key Fields

FIELD STAMPS

IndustryFintech

RegionChina

ScaleMid-size

ChannelHybrid

📌 Background

In 2026, central banks encouraged supply chain de-coring and data-based credit lending, reducing reliance on core enterprise guarantees. AI and blockchain enable real-time risk control across the entire chain (orders, logistics, accounts receivable), increasing SME loan approval rates from 42% to 78%, with the market continuing to grow at over 15% annually. Traditional guarantee models struggle to cover secondary and tertiary suppliers, making platform-based and asset-light models mainstream.

👤 Target Customers

SME suppliers and distributors lacking collateral or core enterprise guarantees in the supply chain, with credit granted by platform funders or banks.

💰 Revenue Streams

1) Transaction service fees of 1%-3% per order or 8%-15% annualized interest rate spread charged to funders; 2) Annual data service fees, asset-backed securitization issuance management fees, and technology service fees exceeding 5% annualized charged to enterprises; 3) Scale-based revenue sharing and account value-added service fees based on transaction volume.

🧮 Cost Structure

AI risk control model R&D and computing power, blockchain rights confirmation technology operations, data interface integration, BD field sales teams, compliance legal fees, and funder profit-sharing.

🛡️ Moat

Industry transaction data and real-time logistics interfaces form a data flywheel, reducing bad debt rates to below 0.1%, multi-node rights confirmation technology barriers, and financial compliance licenses.

🔑 Keys to Success

Deeply bind core digital service scenarios in vertical industrial chains
AI and IoT physical credit capabilities replace core enterprise guarantees
Obtain API data from traffic platforms such as Amazon and WeChat

⚠️ Risks

Poor cooperation from core enterprises leads to data fragmentation
Platform advance payments lead to liquidity risk
Systemic industry downturn causes massive delinquencies

🏢 Cases

Shengye Capital AI Supply Chain Tech Platform: annualized service fee rate >5%, bad debt rate <0.1%
JD Supply Chain Finance Baobei: minute-level credit based on transaction and logistics with anytime borrowing and repayment
Zhongqi Yuncun Yunxin: multi-tier supplier accounts receivable circulation for 1000+ micro and small enterprises

📊 SWOT Analysis

Strengths

AI risk control provides second-level loan disbursement with extremely low bad debt rates
No collateral or guarantee required, reaching secondary and tertiary long-tail suppliers
Asset-light platform model enables rapid replication across industries

Weaknesses

Initial deep integration with core enterprise ERP and logistics scenarios requires a long customer acquisition cycle
Weak data for non-standardized industries, heavily reliant on industry experience
Capital costs are susceptible to macroeconomic interest rate fluctuations

Opportunities

Policies such as Document No. 77 encourage de-coring, bringing trillions in market compliance dividends
Full-chain industrial digitization and the trend of merging physical and credit data
Cross-border financing for outbound supply chains and expansion within the RCEP region

Threats

Competition from large enterprises or banks building closed-loop data platforms themselves
Asset quality risks caused by multi-link data fraud and trade disputes
Stricter regulatory compliance regarding platforms combining fundraising and information intermediation

Sources