Anti-Fraud Checklist: High-Risk Black Industry Indicators - Abnormal Device Fingerprints, Dense IP Proxies, Bulk Account Registration and Cross-Platform Repeat Borrowing, Need for Multi-Source Verification and Dynamic Thresholds

With the rapid development of financial technology, black industry attack methods are also constantly upgrading. Financial institutions need to establish a comprehensive anti-fraud system to timely identify and prevent black industry attacks. This article summarizes the key indicators of high black industry incidence and proposes an anti-fraud strategy of establishing multi-source verification and dynamic thresholds.

1. Overview of High-Risk Black Industry Indicators

Black industry attacks usually manifest as the following high-incidence indicators, which financial institutions need to focus on monitoring:

2. Abnormal Device Fingerprints

Device fingerprint is an important identifier for identifying device uniqueness, and black industry usually evades risk control by tampering with device fingerprints.

2.1 Common Abnormal Device Fingerprints

The following are common abnormal device fingerprint situations:

• Frequent changes of device ID (changing multiple device IDs in a short period of time)
• Device parameter mismatch (e.g., operating system version does not match device model)
• Device fingerprint tampered or forged
• Same device fingerprint associated with multiple accounts

3. Dense IP Proxies

Black industry usually uses IP proxies to hide real IP addresses, evade geographical restrictions and IP blacklists.

3.1 Methods to Identify IP Proxies

Financial institutions can identify IP proxies through the following methods:

1. IP address does not match geographical location
2. IP address associated with multiple accounts in a short period of time
3. Using known proxy IP pools
4. Abnormal ASN (Autonomous System Number) of IP address
5. Low reputation score of IP address

4. Bulk Account Registration

Black industry usually registers accounts in bulk for fraudulent activities.

4.1 Characteristics of Bulk Account Registration

The following are common characteristics of bulk account registration:

• Registering a large number of accounts in a short period of time (e.g., within 1 hour)
• Registered accounts use similar usernames or email formats
• Registered accounts use the same device or IP address
• Incomplete registration information or unified format

5. Cross-Platform Repeat Borrowing

Black industry usually uses multiple accounts to borrow repeatedly on different platforms or the same platform, forming cross-borrowing.

5.1 Identification of Cross-Platform Repeat Borrowing

Financial institutions can identify cross-platform repeat borrowing through the following methods:

1. Multiple accounts associated with the same ID card or phone number borrowing
2. Same device or IP address associated with multiple borrowing accounts
3. Borrowing time and amount showing regularity
4. Borrower information (e.g., work unit, address) highly similar

6. Establishing Multi-Source Verification and Dynamic Thresholds

In response to high-risk black industry indicators, financial institutions need to establish an anti-fraud system with multi-source verification and dynamic thresholds.

7. Implementation Suggestions for Anti-Fraud Checklist

Financial institutions can implement the anti-fraud checklist according to the following steps:

7.1 Data Collection and Integration

First, it is necessary to collect and integrate multi-source data, including:

1. Device data (device fingerprint, operating system, browser, etc.)
2. Network data (IP address, geographical location, ASN, etc.)
3. User behavior data (registration time, login frequency, operation path, etc.)
4. Business data (borrowing amount, repayment records, overdue situations, etc.)
5. Third-party data (credit reports, blacklists, etc.)

7.2 Rule Engine and Model Construction

Establish rule engines and machine learning models, including:

1. Rule-based anti-fraud engine (for identifying known fraud patterns)
2. Machine learning models (for identifying unknown fraud patterns)
3. Deep learning models (for processing complex nonlinear relationships)

7.3 Real-time Monitoring and Early Warning

Establish a real-time monitoring and early warning system, including:

1. Real-time data stream processing (such as using Kafka, Flink, etc.)
2. Abnormal behavior detection and early warning
3. Fraud case management and analysis

7.4 Continuous Optimization and Iteration

The anti-fraud system needs continuous optimization and iteration, including:

1. Regularly analyze fraud cases and update rules and models
2. Monitor the false positive rate and false negative rate of the anti-fraud system
3. Pay attention to the latest fraud methods and prevention measures in the industry
4. Conduct pressure tests and simulated attacks to verify the effectiveness of the anti-fraud system

8. Conclusion

Black industry attacks are an important challenge facing financial institutions, and a comprehensive anti-fraud system needs to be established. By identifying high-risk black industry indicators such as abnormal device fingerprints, dense IP proxies, bulk account registration and cross-platform repeat borrowing, and establishing an anti-fraud strategy of multi-source verification and dynamic thresholds, financial institutions can effectively prevent black industry attacks and protect user fund security and their own interests.

Anti-fraud is a continuous process, and financial institutions need to constantly update anti-fraud rules and models to adapt to changes in black industry attack methods. At the same time, strengthening cooperation with industry partners, sharing fraud information and prevention experience is also an important way to improve anti-fraud effects.