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AI Credit Graph Breaking the Impossible Triangle of Microfinance

1) Proprietary lending net interest margin: earning interest spreads by lending with own capital; 2) Joint lending techn

MODEL

Key Fields

FIELD STAMPS

IndustryFintech

RegionChina

ScaleMid-size

ChannelOnline

📌 Background

Microfinance has long faced the impossible triangle of scale, cost, and risk: expanding scale makes risk control difficult, while controlling risk drives up costs. In 2026, New Net Bank wove a dense credit graph using an AI middle platform, incorporating multi-dimensional information such as social behavioral traits, operating cash flows, and scenario data into its risk control model. This enabled unsecured, second-level credit approvals for micro-merchants, and the bank even introduced AI lie detection to combat financial grey and black industries.

👤 Target Customers

Micro-merchants and individual industrial and commercial households as borrowers; banks and licensed financial institutions as funding providers.

💰 Revenue Streams

1) Proprietary lending net interest margin: earning interest spreads by lending with own capital; 2) Joint lending technology services: charging technology service fees based on scale in joint lending with banks; 3) Risk control middle platform subscription: charging licensed institutions annual subscription fees for model and middle platform capabilities; 4) Scenario data value-added: charging ecosystem partners credit data service fees per API call (this path is an opportunistic item, and the scale of potential revenue has not been disclosed).

🧮 Cost Structure

AI middle platform R&D and computing power costs Customer acquisition and scenario channel investment Bad debt provisioning and capital occupation

🛡️ Moat

Continuous iteration of the AI risk control model's practical data flywheel Purely online, second-level loan operational efficiency Licensed banking qualification barrier

🔑 Keys to Success

Continuous iteration of AI risk control model anti-fraud capabilities
Acquisition and compliant integration of multi-source scenario data
Expansion of the joint lending partner network

⚠️ Risks

Cyclical exposure of credit risk
Upgrading of grey and black industry tactics in AI anti-fraud confrontations

🏢 Cases

New Net Bank AI Middle Platform Credit Graph
AutoNavi (Amap) Cloud Map combined with MYbank for micro-business operational decision-making empowerment

📊 SWOT Analysis

Strengths

AI risk control model proven and matured through practical application
Extremely low marginal costs for purely online operations
Leading anti-fraud capabilities including AI lie detection

Weaknesses

High reliance on scenario partner data
Capital scale constraints limiting the lending ceiling

Opportunities

Continuous policy dividends encouraging technological empowerment in microfinance
New dimensions such as Amap operational data enriching the credit graph

Threats

Intensified competition as giants like Ant and ByteDance enter the credit track
Upgraded AI countermeasures from financial grey and black industries

Sources