White label DSP

How White label DSP Works

Incoming Ad Request → [White-Label DSP Core] → +--------------------------+
                                              │ 1. Pre-bid Analysis      │
                                              │    - IP Reputation Check │
                                              │    - User-Agent Parsing  │
                                              │    - Geo-location Match  │
                                              └───────────┬──────────────┘
                                                          ↓
                                              +--------------------------+
                                              │ 2. Behavioral Heuristics │
                                              │    - Click Frequency     │
                                              │    - Session Duration    │
                                              │    - Known Bot Patterns  │
                                              └───────────┬──────────────┘
                                                          ↓
                                              +--------------------------+
                                              │ 3. Decision Engine       │
                                              │    - Score Traffic       │
                                              │    - Block or Allow Bid  │
                                              └───────────┬──────────────┘
                                                          ↓
Filtered Traffic → [Bid on Ad Exchange] → Legitimate User

A white-label DSP integrates fraud detection directly into the media buying process, acting as a gatekeeper before ad budgets are spent. By leveraging a customizable platform, businesses can enforce their own traffic quality standards and block fraudulent activity in real time. This proactive approach prevents invalid clicks from draining campaign funds and ensures that analytics reflect genuine user engagement. The process is transparent, allowing for continuous optimization of anti-fraud rules based on performance data.

Data Ingestion and Initial Screening

When an ad opportunity arises, the DSP receives a bid request containing data about the user and the placement, such as IP address, device type, and location. The white-label DSP immediately subjects this data to an initial screening. This stage involves matching the information against foundational blocklists, checking IP reputation, and parsing the user-agent string to identify non-standard or suspicious client profiles. This first layer filters out the most obvious forms of non-human traffic before any deeper analysis is performed.

Real-time Behavioral Analysis

Next, the system analyzes behavioral patterns associated with the request. It examines the frequency of clicks or impression requests from the same user or IP, the time between actions, and other session-based heuristics. This stage is crucial for detecting sophisticated bots designed to mimic human behavior. By comparing current activity against historical data and known fraud patterns, the DSP can identify anomalies that suggest automated or malicious intent, such as impossibly fast click-throughs or interactions originating from a data center.

Scoring and Decision-Making

Based on the combined findings from the initial screening and behavioral analysis, the DSP’s decision engine assigns a fraud score to the ad request. This score represents the probability that the request is invalid. The platform then uses a pre-defined threshold to make a decision: if the score exceeds the acceptable level, the DSP blocks the bid, preventing the ad from being served. If the traffic is deemed legitimate, the bid proceeds to the ad exchange. This entire process occurs in milliseconds.

Diagram Element Breakdown

Incoming Ad Request → [White-Label DSP Core]

This represents the start of the process, where a publisher’s site has an ad slot available and sends a request to the DSP to fill it. The DSP core is the central engine that processes this request.

1. Pre-bid Analysis

This is the first line of defense. The system performs quick, fundamental checks like verifying the IP’s reputation against known bad actors and ensuring the user agent and location data appear legitimate.

2. Behavioral Heuristics

This component looks at the context of the request. It assesses if the frequency of clicks or page loads is humanly possible or if it matches patterns of known botnets, providing a deeper layer of validation.

3. Decision Engine

This is the brain of the operation. It aggregates all data points into a single risk score. Based on rules set by the platform owner, it makes the final call to either reject the request as fraudulent or pass it on.

Filtered Traffic → [Bid on Ad Exchange]

This shows the outcome. Only traffic that has passed all fraud checks is allowed to proceed. The DSP places a bid on this clean traffic, ensuring ad spend is directed toward real potential customers.

🧠 Core Detection Logic

Example 1: Click Velocity Capping

This logic prevents a single user (identified by IP address or device ID) from generating an unnaturally high volume of clicks in a short period. It is a fundamental rule in traffic protection to block basic bots and click-farm activities.

FUNCTION checkClickVelocity(user_id, timeframe_seconds, max_clicks):
  // Get all click timestamps for the user_id in the last X seconds
  clicks = getClicksForUser(user_id, since=NOW - timeframe_seconds)

  // Count the number of clicks
  click_count = length(clicks)

  // Compare against the threshold
  IF click_count > max_clicks:
    RETURN "BLOCK_TRAFFIC"
  ELSE:
    RETURN "ALLOW_TRAFFIC"
  ENDIF

Example 2: Data Center IP Filtering

This logic identifies if an ad request originates from a known data center or server hosting provider instead of a residential or mobile network. Server-based traffic is often non-human and used for large-scale bot operations.

FUNCTION isDataCenterIP(ip_address):
  // Query a database of known data center IP ranges
  is_server_ip = queryDataCenterDB(ip_address)

  IF is_server_ip == TRUE:
    // Flag as high-risk, likely non-human traffic
    RETURN TRUE
  ELSE:
    RETURN FALSE
  ENDIF

Example 3: Geo-Mismatch Detection

This logic compares the IP address’s geographic location with other location signals from the device (like GPS data or user profile settings) if available. Significant discrepancies often indicate the use of a proxy or VPN to mask the user’s true origin.

FUNCTION checkGeoMismatch(ip_geo, device_geo):
  // Check if both data points exist
  IF ip_geo is NOT NULL AND device_geo is NOT NULL:
    // Calculate distance or check for country/region mismatch
    distance = calculateDistance(ip_geo, device_geo)

    // If distance is beyond an acceptable radius (e.g., 100 km)
    IF distance > 100:
      RETURN "FLAG_AS_SUSPICIOUS"
    ENDIF
  ENDIF

  RETURN "SEEMS_OK"

📈 Practical Use Cases for Businesses

• Campaign Shielding: A business can use a white-label DSP to apply custom, pre-bid blocking rules across all campaigns, preventing ad spend from being wasted on fraudulent inventory before a bid is ever placed.
• Supply Path Optimization: By analyzing traffic quality from different supply-side platforms (SSPs), a company can use its DSP to automatically prioritize and select SSPs that consistently deliver clean, high-performing traffic.
• Data Transparency and Control: Owning the DSP technology allows a business to have full transparency into media buying, helping them identify and cut ties with publishers or channels that have high rates of invalid traffic.
• Reselling and Revenue Generation: An agency can rebrand the white-label DSP and offer managed, fraud-protected media buying services to its own clients, creating a new revenue stream.

Example 1: IP Blocklist Rule

A business can maintain a dynamic, proprietary blocklist of IP addresses known to be associated with fraud. This rule ensures any bid request from these IPs is immediately rejected.

FUNCTION preBidCheck(bid_request):
  // Get IP from incoming request
  user_ip = bid_request.ip

  // Check against internal blocklist
  IF user_ip IN proprietary_blocklist:
    REJECT_BID("IP found on internal blocklist")
  ELSE:
    PROCEED_TO_NEXT_CHECK()
  ENDIF

Example 2: Session Anomaly Scoring

This logic scores a user session based on multiple risk factors, such as time on site, number of actions, and mouse movement patterns. A session with a high anomaly score is flagged as likely bot activity.

FUNCTION scoreSession(session_data):
  score = 0
  
  // Rule 1: Very short session duration
  IF session_data.duration_seconds < 2:
    score += 40

  // Rule 2: No mouse movement detected
  IF session_data.mouse_events == 0:
    score += 30
    
  // Rule 3: Click happened too fast
  IF session_data.time_to_first_click < 1:
    score += 30

  RETURN score // High score indicates high fraud probability

🐍 Python Code Examples

This code demonstrates a simple way to filter out traffic coming from known fraudulent IP addresses by checking against a predefined set of blacklisted IPs.

BLACKLISTED_IPS = {"198.51.100.15", "203.0.113.88", "192.0.2.200"}

def filter_suspicious_ips(click_event):
    """
    Checks if a click event's IP is in the blacklist.
    Returns True if the IP is suspicious, False otherwise.
    """
    ip_address = click_event.get("ip")
    if ip_address in BLACKLISTED_IPS:
        print(f"Blocking suspicious IP: {ip_address}")
        return True
    return False

# Example usage:
click = {"ip": "203.0.113.88", "timestamp": "2025-07-17T20:00:00Z"}
is_fraud = filter_suspicious_ips(click)

This example function detects abnormal click frequency from a single user ID within a short time window, a common sign of bot activity or click spam.

from collections import defaultdict
import time

CLICK_LOG = defaultdict(list)
TIME_WINDOW_SECONDS = 10
MAX_CLICKS_IN_WINDOW = 5

def is_abnormal_click_frequency(user_id):
    """
    Analyzes click timestamps for a user to detect abnormal frequency.
    Returns True if click frequency is too high.
    """
    current_time = time.time()
    
    # Filter out old clicks
    CLICK_LOG[user_id] = [t for t in CLICK_LOG[user_id] if current_time - t < TIME_WINDOW_SECONDS]
    
    # Add current click
    CLICK_LOG[user_id].append(current_time)
    
    # Check if count exceeds the limit
    if len(CLICK_LOG[user_id]) > MAX_CLICKS_IN_WINDOW:
        print(f"Abnormal click frequency detected for user: {user_id}")
        return True
    return False

# Example usage:
user = "user-12345"
for _ in range(6):
    is_fraud = is_abnormal_click_frequency(user)

Types of White label DSP

• Pre-bid Filtering DSP: This type integrates fraud detection directly into the bidding process. It analyzes bid requests in real-time and decides whether to bid based on fraud risk scores, preventing money from being spent on invalid impressions before they are purchased.
• Post-bid Analytics DSP: This variation focuses on analyzing traffic after the ad has been served and clicked. It identifies fraudulent patterns by examining conversion data, user behavior on landing pages, and other post-click metrics to refine future blocklists and rules.
• Hybrid Model DSP: This combines both pre-bid blocking and post-bid analysis. It uses real-time filtering to stop most fraud upfront while leveraging post-click data to uncover more sophisticated schemes, creating a feedback loop that continuously improves detection accuracy.
• Contextual and Behavioral DSP: This type focuses heavily on the context of the ad placement and the user's behavior over time. It detects fraud by identifying mismatches between the site's content and the user's profile, or by flagging behavior that deviates from established human patterns.

🛡️ Common Detection Techniques

• IP Reputation Analysis: This technique involves checking an incoming IP address against global databases of known malicious actors, proxies, and data centers. It serves as a first-line defense to filter out traffic from sources with a history of fraudulent activity.
• Behavioral Analysis: This method monitors user interaction patterns, such as click frequency, mouse movements, and session duration. It identifies non-human behavior by flagging actions that are too fast, too uniform, or lack the randomness typical of legitimate users.
• Device and Browser Fingerprinting: The system collects detailed attributes of a user's device and browser (e.g., OS, plugins, screen resolution) to create a unique ID. This helps detect when a single entity is attempting to mimic multiple users by slightly altering its configuration.
• Click-Time Analysis: This technique analyzes the time distribution between an ad impression and a click. Fraudulent clicks often occur with unnatural timing, such as happening almost instantaneously or in synchronized bursts across multiple users, which this method can detect.
• Geographic Validation: This involves cross-referencing a user's IP-based location with other available geographic data points. Significant discrepancies can expose the use of VPNs or proxies intended to bypass geo-targeted campaign rules or conceal the true origin of traffic.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
TeqBlaze	A white-label DSP solution that offers AI-powered optimization and built-in ad fraud prevention through integrations like GeoEdge. It allows for extensive customization of the platform.	Highly customizable, strong focus on machine learning for optimization, provides a self-serve model for clients.	The advanced features may require significant technical expertise to fully leverage.
Epom	A white-label DSP that allows agencies and brands to create their own programmatic platform with custom branding. It emphasizes traffic quality control and offers features to adjust bids based on performance.	Flexible pricing models, enables setting custom bid markups, and supports a wide range of ad formats.	Some of the more advanced analytics and white-labeling features come at an additional cost.
BidsCube	Provides a white-label DSP with a focus on giving users full control over their advertising efforts. It offers robust targeting options and real-time analytics to boost campaign efficiency.	Strong IAB category targeting, allows for direct integration with various supply sources, emphasizes data ownership.	May be better suited for those with existing programmatic knowledge due to the level of control offered.
SmartyAds	Offers a customizable white-label ad exchange and DSP technology. It's designed to support various business models with a focus on creating a controlled, in-house advertising environment.	Fully customizable platform, provides optimization tools, and supports self-branded environments for monetization.	As a comprehensive solution, it might be more complex than needed for smaller-scale operations.

📊 KPI & Metrics

Tracking both technical accuracy and business impact is vital when using a white-label DSP for fraud protection. Technical metrics ensure the filtering logic is working correctly, while business KPIs confirm that these efforts are translating into improved campaign performance and a higher return on ad spend.

Metric Name	Description	Business Relevance
Invalid Traffic (IVT) Rate	The percentage of total traffic identified and blocked as fraudulent or non-human.	Directly measures the effectiveness of fraud filters in protecting the ad budget.
Fraud Detection Rate	The percentage of all fraudulent activity that the system successfully detects and blocks.	Indicates the accuracy and comprehensiveness of the detection algorithms.
False Positive Percentage	The percentage of legitimate traffic that is incorrectly flagged as fraudulent.	A high rate can lead to lost opportunities and reduced campaign reach; must be minimized.
Cost Per Acquisition (CPA) Change	The change in the cost to acquire a customer after implementing fraud protection.	Shows the direct financial impact of reallocating budget from fraudulent to clean traffic.
Clean Traffic Ratio	The proportion of traffic that passes all fraud filters and is deemed legitimate.	Helps in evaluating the quality of different traffic sources and optimizing supply paths.

These metrics are typically monitored through real-time dashboards integrated into the white-label DSP. Automated alerts can be configured to notify administrators of sudden spikes in fraudulent activity or unusual changes in key metrics. This continuous feedback loop allows for the ongoing optimization of fraud filters, blocklists, and scoring thresholds to adapt to new threats and improve overall accuracy.

🆚 Comparison with Other Detection Methods

Real-time vs. Post-Click Analysis

A white-label DSP's primary advantage is its pre-bid, real-time detection, which prevents ad spend on fraudulent traffic. This contrasts with post-click analysis methods, which identify fraud after the budget has been spent. While post-click analysis is useful for refunds and blacklisting, the DSP's preventative approach offers more immediate budget protection. Post-click tools may, however, catch sophisticated fraud that real-time systems miss.

Custom Rules vs. Third-Party Blacklists

While many fraud solutions rely solely on third-party blacklists, a white-label DSP allows for the creation of highly customized filtering rules tailored to specific campaign goals and observed traffic patterns. This provides a more dynamic and precise defense. However, it also requires more active management than a simple blacklist subscription. The most effective strategies often combine both for comprehensive coverage.

Integrated vs. Standalone Solutions

A white-label DSP integrates fraud detection directly into the media buying workflow, ensuring every impression is vetted. Standalone fraud detection services operate separately, which can create delays and data discrepancies between the fraud platform and the buying platform. The integrated nature of the DSP offers greater efficiency, but a specialized standalone tool might offer more in-depth, niche detection capabilities.

⚠️ Limitations & Drawbacks

While a powerful tool, a white-label DSP for fraud protection is not without its challenges. Its effectiveness can be limited by the complexity of its setup, the need for continuous management, and its ability to adapt to new types of fraud. In certain scenarios, its resource requirements may outweigh its benefits.

• High Initial Setup Complexity: Configuring custom rules, integrations, and hierarchies requires significant technical expertise and initial time investment.
• Potential for False Positives: Overly aggressive filtering rules can incorrectly block legitimate users, leading to lost conversions and reduced campaign reach.
• Adaptability to New Threats: While customizable, the platform's ability to fight novel fraud techniques depends on the vigilance of its human operators to update rules and analyze new patterns.
• Resource Overhead: Managing and continually optimizing a white-label DSP requires dedicated personnel, which can be a significant cost for smaller businesses.
• Data Limitations: The DSP's effectiveness is limited to the data available in the bid stream; it may not catch sophisticated bots that excel at mimicking human-like data signals.
• Scalability Costs: While the platform is scalable, increased traffic volume leads to higher infrastructure and data processing costs.

In cases of extremely sophisticated or low-volume fraud, a hybrid approach that combines the DSP with specialized third-party analytics tools might be more suitable.

❓ Frequently Asked Questions

How does a white-label DSP differ from a self-serve DSP?

A white-label DSP is a platform you purchase and rebrand as your own, giving you full control over features, integrations, and client accounts. A self-serve DSP is a platform you use by signing up for an account, but you don't own the technology and are limited to the provider's pre-defined features and integrations.

Is a white-label DSP cost-effective for small businesses?

Generally, a white-label DSP is more suitable for large agencies or businesses with high ad volumes due to the initial investment and management overhead. Small businesses might find a self-serve DSP more cost-effective, as it eliminates the need to pay for platform ownership and maintenance.

Can I choose my own SSPs and ad exchanges to connect with?

Yes, one of the key advantages of a white-label DSP is the ability to choose and integrate with the supply-side platforms (SSPs) and ad exchanges that are most relevant to your business niche, giving you full control over your inventory sources.

How quickly can fraud detection rules be updated?

Fraud detection rules can be updated in near real-time. Since you own and manage the platform, you can modify filtering logic, add IPs to blocklists, or adjust scoring thresholds instantly through the administrative dashboard, allowing for rapid response to new threats.

Does a white-label DSP guarantee 100% fraud prevention?

No solution can guarantee 100% fraud prevention, as fraudsters constantly evolve their techniques. However, a white-label DSP provides powerful, customizable tools to significantly reduce fraud by blocking most invalid traffic in real-time and allowing you to adapt your defenses as new threats emerge.

🧾 Summary

A white-label DSP provides businesses with a customizable and re-brandable platform for programmatic advertising, offering direct control over fraud protection. By enabling pre-bid analysis of traffic, it allows for the real-time application of custom filtering rules to block bots and invalid clicks before ad spend is committed. This approach is vital for protecting campaign budgets, ensuring data accuracy, and improving overall advertising ROI by focusing spend on legitimate, high-quality traffic sources.

🔗 Related Articles

• Fraud Detection of AD Clicks Using Machine Learning Techniques – https://journaljsrr.com/index.php/JSRR/article/view/1762
• Click-Fraud Detection for Online Advertising - ResearchGate – https://www.researchgate.net/publication/322471676_Click-Fraud_Detection_for_Online_Advertising
• A Survey on Online Advertising and Click fraud detection - ResearchGate – https://www.researchgate.net/publication/344212513_A_Survey_on_Online_Advertising_and_Click_fraud_detection
• What is a white-label DSP? | AppsFlyer – https://www.appsflyer.com/glossary/white-label-dsp/
• REAL-TIME AD CLICK FRAUD DETECTION - SJSU ScholarWorks – https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=6454&context=etd_projects