JR/T 0202-2020 in English
VALIDBig data based intelligent payment risk control technical specification
- Issued on:2020-12-03
- Implemented on:2020-12-03
- File Format:PDF
- Delivery:Via email within 5 business days
$359.00
| Standard No: | JR/T 0202-2020 |
| Document status: | VALID |
| Title in English: | Big data based intelligent payment risk control technical specification |
| Title in Chinese: | 基于大数据的支付风险智能防控技术规范 |
| Language: | English |
| File Format: | Electronic (PDF) |
| Delivery: | Via email within 5 business days |
| Issued on: | 2020-12-03 |
| Implemented on: | 2020-12-03 |
| Professional Classification: | JR-Finance |
| Related Keywords: | risk types fraud risk prevention
intelligent payment risk control traditional risk control standards control technology machine learning/rule engine model interpretability requirements risk security protection requirements risk prevention |
| Related Topics: | Big Data
jr/t 0071 baseline risk data risk to pay JR/t |
Introduction
Analysis of the core framework of the standard
The specification builds a "trinity" technical framework system:
| Module | Core technology | Implementation requirements |
|---|---|---|
| Big data technology | Stream processing/batch processing/graph computing | Must meet JR/T 0071 security protection requirements |
| Risk prevention and control technology | Machine learning/rule engine | Model interpretability requirements |
| Risk type | Fraud/compliance/other risks | Dynamic classification mechanism |
Technical Analysis of Risk Types
Fraud Risk Prevention and Control Cases
For the counterfeit card fraud scenario, the specification requires the establishment of a three-level identification mechanism:
- Chip transaction abnormal feature detection
- Degraded transaction behavior pattern analysis
- Magnetic stripe transaction spatiotemporal feature verification
Typical prevention and control strategy: adopting the composite verification of device fingerprint + transaction location + behavior sequence
Machine Learning Implementation Specifications
| Learning Type | Sample Requirements | Applicable Scenarios |
|---|---|---|
| Supervised learning | Labeled samples ≥ 100,000 | Known risk pattern identification |
| Semi-supervised learning | Labeled samples ≥ 10,000 | New fraud detection |
| Unsupervised learning | No labeling requirement | Abnormal cluster detection |
System security implementation recommendations
Key measures for data protection
- Establish a four-layer permission isolation mechanism (development/testing/production/audit)
- Sensitive data storage must comply with the A3-level encryption requirements of JR/T 0171
- Model deployment approval process for implementing two-factor authentication
Standard evolution analysis
Compared with traditional risk control standards, this specification highlights three major innovations:
- For the first time, the full life cycle requirements for machine learning model management are clarified
- Relationship network analysis is introduced as a means of identifying gang fraud
- A closed-loop risk management feedback mechanism is established (assessment → verification → management → optimization)
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