Reduce Fraud Rate by 37%: How to Optimize Fraudscore with IP Proxy (IPFLY Case Study)

A leading consumer finance organization in Indonesia once faced a painful reality: 4.97% of its loan applicants turned out to be fraudulent, leading to massive bad debt losses. That was until they introduced a fraudscore-based risk control system, which cut the fraud rate to 3.11% while only reducing the approval rate by 5%—a 37% fraud reduction that saved millions in losses. This case reveals the core value of fraudscore: it’s not just a number, but a “risk compass” for businesses in finance, e-commerce, and online services.

However, 62% of businesses still struggle with fraudscore management, according to a 2026 anti-fraud industry report. Common pain points include: unclear calculation logic leading to misjudgments, high fraudscores caused by low-quality IPs, and ineffective optimization strategies. This guide will solve these problems comprehensively: from deciphering fraudscore’s calculation mechanism to practical optimization tactics, and from proxy service selection (with IPFLY as a key solution) to API integration tutorials. By the end, you’ll master how to use fraudscore to build a solid anti-fraud defense line.

Core Cognition: What Is Fraudscore & How Is It Calculated?

Fraudscore (fraud risk score) is a quantitative indicator that evaluates the probability of a user/transaction being fraudulent, typically ranging from 0 to 100 (the higher the score, the higher the fraud risk). It integrates multi-dimensional data to help businesses make quick risk decisions—such as approving a loan, processing a payment, or blocking a suspicious login.

Key Calculation Dimensions of Fraudscore

Mainstream fraudscore systems (e.g., JPMorgan Chase’s SafeTech) rely on 4 core dimension groups, with IP-related features accounting for 20-30% of the total weight:

• User Identity Dimensions: Name, ID number, date of birth, and whether the information matches official databases.
• Behavioral Dimensions: Device fingerprint, login frequency, operation path, and whether the behavior is consistent with normal user habits (e.g., abnormal click speed).
• Transaction/Application Dimensions: Transaction amount, product type, shopping cart data, and whether the application information is filled in abnormally quickly.
• Network & IP Dimensions: IP type (residential/data center), geographical consistency (whether the IP location matches the user’s declared address), IP reputation, and historical fraud records of the IP segment.

Industry Benchmarks for Fraudscore

Fraudscore thresholds vary by industry—here are 2026 industry benchmarks to reference:

The Hidden Link: How Proxy IP Affects Fraudscore

In fraudscore’s network dimension evaluation, proxy IPs are double-edged swords: low-quality proxies (e.g., public data center IPs) will significantly increase fraudscore, while high-quality residential proxies can help legitimate businesses (e.g., cross-border e-commerce) reduce unnecessary risk warnings.

How Low-Quality Proxies Push Up Fraudscore

Fraudsters often use cheap public proxy pools, but these IPs have three fatal flaws that trigger high fraudscore:

• Poor Reputation: These IPs are frequently listed in threat intelligence databases (e.g., 30% of Bright Data’s US IP pool are marked as “frequent abusers”), directly increasing risk scores.
• High Rotation Frequency: Switching 10+ IPs per second is identified as abnormal behavior by fraudscore systems.
• Geographical Inconsistency: Jumping between multiple countries/cities in an hour (e.g., from Beijing to New York) violates normal user behavior patterns.

Why Legitimate Businesses Need High-Quality Proxies for Fraudscore Optimization

For cross-border businesses or global data collection teams, legitimate operations may be misjudged due to IP issues: for example, a Chinese cross-border seller monitoring Amazon prices with a fixed IP may have a high fraudscore due to frequent access. High-quality proxies solve this by:

• Providing real residential IPs that mimic genuine user access, avoiding being marked as “suspicious proxies”.
• Supporting precise geo-targeting to ensure IP location matches the business’s target market, improving geographical consistency scores.
• Maintaining stable IP usage cycles to avoid triggering “abnormal rotation” warnings.

IPFLY: The Optimal Proxy Solution for Fraudscore Optimization

Among numerous proxy providers, IPFLY stands out for fraudscore optimization scenarios, especially for small and medium-sized businesses. Its core advantages lie in “low fraud risk IP pool + no-client integration + high availability”, perfectly matching the needs of fraudscore management.

Core Advantages of IPFLY for Fraudscore Optimization

1.No-Client Design: Seamless Integration with Risk Control Systems

Unlike Bright Data and Oxylabs, which require installing client software or dedicated tools, IPFLY has no client application. Businesses can directly integrate it into existing fraudscore query systems, risk control platforms, or API workflows by configuring proxy parameters. This not only reduces deployment time (completion in 5 minutes) but also avoids compatibility issues with internal systems—critical for risk control teams that value efficiency and stability.

2.Low-Fraud-Risk IP Pool: Fundamental Guarantee for Fraudscore Reduction

IPFLY’s 90 million+ dynamic residential IP pool has a fraud risk score of less than 0.1%, far lower than Bright Data’s 47.52% global average fraud rate for proxies. These IPs are sourced from real ISPs, with complete geographical information and clean usage records, making them indistinguishable from legitimate user IPs. For cross-border e-commerce businesses, using IPFLY’s IPs can reduce “false high fraudscore” warnings by 80%.

3.99.9% Uptime: Stable Support for Real-Time Fraudscore Monitoring

Fraudscore calculation requires real-time data support (e.g., real-time IP reputation query). IPFLY’s 99.9% uptime ensures that risk control systems do not experience service interruptions, while competitors like Bright Data and Oxylabs have uptimes of 99.7% and 99.8% respectively. For financial institutions that process 10,000+ transactions daily, 0.2% more uptime means avoiding hundreds of potential fraud judgment delays.

4.Cost-Effectiveness: Friendly to Small and Medium-Sized Businesses

IPFLY’s pay-as-you-go model starts at $0.8/GB, significantly lower than Bright Data’s $3/GB and Oxylabs’ $7.5/GB (enterprise package). For a cross-border e-commerce business with 50 daily price monitoring tasks, using IPFLY can save $1,440 in annual proxy costs compared to Bright Data—critical for businesses with limited anti-fraud budgets.

IPFLY vs. Competitors: Comprehensive Comparison for Fraudscore Scenarios

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Practical Tutorial: Query Fraudscore with IPFLY Proxy (Python + Amount API)

We’ll use Amount API (a professional fraudscore query service) to demonstrate how to integrate IPFLY proxy for stable fraudscore queries. This tutorial is applicable to payment, finance, and e-commerce risk control scenarios.

Preparation

1.Sign up for Amount API and get an access token: https://api.amount.com/.

2.Get IPFLY proxy credentials: Log in to IPFLY’s official platform, go to “Residential Dynamic IP” → “Account Password Extraction” to get host, port, username, and password.

Step-by-Step Code Implementation

import requests

# Configure Amount API parameters
AMOUNT_TOKEN = "your_amount_api_bearer_token"
API_URL = "https://api.amount.com/v1/verify/fraud-score"

# Configure IPFLY proxy parameters (no client required)
IPFLY_PROXY = {
    "http": "http://your_ipfly_username:your_ipfly_password@gw.ipfly.com:8080",
    "https": "https://your_ipfly_username:your_ipfly_password@gw.ipfly.com:8080"
}

# Request body (user/transaction data for fraudscore calculation)
request_body = {
    "identity": {
        "first_name": "Robert",
        "last_name": "Smith",
        "ssn": "123456789",
        "date_of_birth": "1990-01-15"
    },
    "address": {
        "address_1": "123 Michigan Ave",
        "city": "Chicago",
        "state": "IL",
        "zip_code": "60601"
    },
    "contact": {
        "email": "robert.smith@example.com",
        "phone": "3125551234"
    },
    "ip": {
        "ip_address": "your_target_ip"  # IP to evaluate (can be IPFLY's proxy IP)
    },
    "event": {
        "id": "trans_123456",
        "type": "payment",
        "product_type": "online_retail"
    }
}

try:
    # Send fraudscore query request with IPFLY proxy
    response = requests.post(
        API_URL,
        json=request_body,
        headers={"Authorization": f"Bearer {AMOUNT_TOKEN}"},
        proxies=IPFLY_PROXY,
        timeout=15
    )
    if response.status_code == 200:
        fraud_data = response.json()
        print(f"Fraudscore: {fraud_data.get('fraud_score', 'N/A')}")
        print(f"Risk Level: {fraud_data.get('risk_level', 'N/A')}")
        print(f"Risk Factors: {fraud_data.get('risk_factors', [])}")
    else:
        print(f"Request failed: Status code {response.status_code}, Message: {response.text}")
except Exception as e:
    print(f"Error occurred: {str(e)}")

Key Notes

• Replace “your_target_ip” with the IP you want to evaluate (e.g., the IP of a user initiating a transaction). For cross-border businesses, use IPFLY’s region-specific ports (e.g., 8081 for US IPs) to match the target market.
• Combine this code with your risk control system: automatically trigger fraudscore queries for high-value transactions, and use IPFLY’s proxy to ensure stable API access.
• Regularly update IPFLY’s IP pool: IPFLY’s second-level IP update ensures that the IPs used for evaluation are always of high quality.

Advanced Strategies: Optimize Fraudscore from Multiple Dimensions

Proxy optimization is just one part of fraudscore management. Combine these strategies to build a comprehensive anti-fraud system:

Integrate Device Fingerprint with IP Data

Track the association between device fingerprints and IPs: if the same device is linked to 10+ IPs within an hour, trigger a high-risk warning. Use the following code snippet to record device-IP associations (Redis implementation):

// Record device fingerprint and IP association (valid for 1 hour)
public void recordDeviceIp(String deviceFingerprint, String ip) {
    String key = "device:ip:" + deviceFingerprint;
    jedis.sadd(key, ip); // Store IPs in a set to avoid duplicates
    jedis.expire(key, 3600); // Set expiration time
}

Build a Dynamic IP Reputation System

Construct an internal IP reputation score model to complement fraudscore evaluation:

IP Reputation Score = 50 (base score) + (Recent 7-day abnormal times * -2) + (Abnormal devices in the same IP segment * -1) + (IP-geography matching degree * 0.5)

Reference: Alibaba Cloud’s practice shows that this model can improve high-risk IP identification accuracy to 92.3% with a false positive rate of less than 0.07%.

Link with Threat Intelligence Databases

Integrate external threat intelligence platforms (e.g., Microstep Online, 360 Threat Intelligence Center) to real-time intercept IPs marked as “malicious”. IPFLY’s IP pool is regularly synchronized with global threat intelligence databases, ensuring that its IPs are not in any blacklists.

Fraudscore Optimization—A Balance Between Risk Control and User Experience

In 2026, as fraud tactics become more sophisticated, fraudscore has become a core tool for businesses to defend against risks. However, excessive reliance on a single indicator or neglect of IP quality can lead to either missed frauds or unnecessary user experience damage.

IPFLY’s proxy solution provides a key breakthrough for fraudscore optimization: its low-fraud-risk IP pool reduces false high-risk warnings, no-client integration simplifies deployment, and high availability ensures uninterrupted risk control operations. Compared to high-cost competitors like Bright Data and Oxylabs, IPFLY offers a more cost-effective choice for small and medium-sized businesses.

Remember: The ultimate goal of fraudscore optimization is not to pursue the lowest score, but to balance risk control and user experience. Combine proxy optimization with device fingerprinting, threat intelligence, and other strategies to build a flexible, accurate anti-fraud system that protects your business while retaining legitimate users.