In app purchase

How In app purchase Works

+-----------------+      +--------------------+      +----------------+      +---------------+
|   User Action   |----->|   App/SDK Event    |----->|  Validation    |----->| Fraud System  |
| (Makes Purchase)|      | (Incl. Receipt)    |      |  Server (MMP)  |      | (Analyzes Data) |
+-----------------+      +--------------------+      +----------------+      +---------------+
        │                                                    │                      │
        │                                                    │                      │
        └────────────────────────────────────────────────────┼──────────────────────┘
                                                             │
                                                    +----------------+
                                                    |  Store API     |
                                                    | (Apple/Google) |
                                                    +----------------+

In the context of click fraud protection, analyzing in-app purchases (IAP) is a powerful method to verify the quality and legitimacy of users acquired through advertising campaigns. Instead of just tracking clicks or installs, this process examines whether the users who came from an ad are real, engaged customers who generate revenue. Fraudsters can fake installs and clicks, but faking a verified purchase is significantly harder. By focusing on post-install events like IAP, advertisers can differentiate between low-quality, fraudulent traffic and high-value, genuine users, ensuring their ad spend is directed toward sources that deliver real return on investment.

Data Collection and Receipt Generation

When a user makes an in-app purchase, the app store (Apple’s App Store or Google Play) generates a unique, cryptographically signed digital receipt. This receipt contains critical details about the transaction, such as the product ID, purchase time, and a unique transaction identifier. The mobile app’s Software Development Kit (SDK) is responsible for capturing this receipt along with the purchase event and forwarding it to a validation server, often managed by a Mobile Measurement Partner (MMP) or a dedicated fraud prevention service.

Server-to-Server Validation

Once the validation server receives the event and the receipt, it initiates a server-to-server check with the corresponding app store’s API. The server sends the receipt data to Apple or Google for verification. The app store’s API confirms whether the receipt is authentic, has not been used before (to prevent replay attacks), and matches the transaction details provided. This external verification is the core of the process, as it relies on the app store as a trusted, neutral third party to confirm the purchase’s legitimacy.

Fraud Scoring and Attribution

If the receipt is validated successfully, the user is marked as a legitimate, paying customer. If the validation fails—or if no receipt is provided for a purchase event—the transaction is flagged as suspicious or fraudulent. This information is then used to score the quality of the traffic source (the ad network or publisher) that brought the user to the app. Traffic sources that deliver users who make verified purchases are considered high-quality, while those sending users who generate fake or invalid purchase events are identified as fraudulent.

Diagram Breakdown

User Action and App/SDK Event

The process begins when a real user initiates an in-app purchase. The app’s SDK records this event and, crucially, captures the digital receipt generated by the app store. This package of data is the first input into the detection pipeline.

Validation Server (MMP)

The event data is sent to a third-party server, typically from a Mobile Measurement Partner (MMP) like AppsFlyer or Adjust. This server acts as the central hub for validating the transaction’s authenticity.

Store API (Apple/Google)

The validation server communicates directly with Apple’s or Google’s servers, sending the receipt for verification. This is the most critical step, as it uses the store’s authority to confirm if a real financial transaction took place.

Fraud System

The result of the validation (success or failure) is fed into the fraud system. This system aggregates the data, marks invalid events as fraud, and updates the quality score for the ad network responsible for the user acquisition, allowing advertisers to block low-quality sources.

🧠 Core Detection Logic

Example 1: Receipt Validation

This is the most fundamental logic. It checks if a revenue event reported by an ad network is accompanied by a valid purchase receipt from the app store. It prevents fraud where publishers send fake purchase events to make their traffic appear more valuable.

FUNCTION handle_purchase_event(event_data):
    IF event_data.has("receipt"):
        receipt = event_data.get("receipt")
        is_valid = validate_with_app_store(receipt)

        IF is_valid == TRUE:
            // Legitimate purchase
            ATTRIBUTE_REVENUE(event_data.source, event_data.amount)
        ELSE:
            // Invalid receipt
            FLAG_FRAUD(event_data.source, "Invalid Receipt")
    ELSE:
        // Missing receipt, indicates fraud
        FLAG_FRAUD(event_data.source, "Missing Purchase Receipt")
END FUNCTION

Example 2: Purchase Behavior Anomaly

This logic analyzes the timing and frequency of purchases to spot non-human behavior. A user making multiple high-value purchases immediately after install, or multiple users from the same source doing so, is highly suspicious and likely indicates automated bot activity.

FUNCTION check_purchase_behavior(user_id, install_timestamp, purchase_event):
    time_since_install = purchase_event.timestamp - install_timestamp
    purchase_amount = purchase_event.amount

    // Flag if a large purchase happens too quickly after install
    IF time_since_install < 60 seconds AND purchase_amount > 50.00:
        FLAG_FRAUD(user_id, "Anomalous First Purchase")
        RETURN

    // Flag if purchase frequency is too high
    recent_purchases = GET_PURCHASES(user_id, last_hour)
    IF count(recent_purchases) > 10:
        FLAG_FRAUD(user_id, "High Purchase Frequency")
END FUNCTION

Example 3: Geo-Mismatch Detection

This logic compares the geographic location of the click, the install, and the purchase. A significant mismatch—for instance, a click from Vietnam, an install in the US, and a purchase with a Brazilian currency—suggests the use of proxies or other masking techniques common in ad fraud.

FUNCTION check_geo_mismatch(click_data, install_data, purchase_data):
    click_country = click_data.country
    install_country = install_data.country
    purchase_currency = purchase_data.currency

    // Mapping currency to expected country for simplicity
    expected_country = get_country_for_currency(purchase_currency)

    IF click_country != install_country OR install_country != expected_country:
        FLAG_FRAUD(click_data.source, "Geo-Location Mismatch")
    ELSE:
        // Countries align, likely legitimate
        PROCESS_AS_VALID(purchase_data)
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Optimization – Businesses can automatically pause or defund campaigns from ad networks that deliver traffic with low rates of verified in-app purchases, reallocating budget to high-performing, legitimate sources.
• Protecting ROAS Metrics – By filtering out fraudulent revenue events, companies ensure their Return on Ad Spend (ROAS) calculations are based on real income, leading to more accurate and reliable business decisions.
• User Quality Verification – IAP validation serves as definitive proof that an acquired user is genuine and engaged, helping app marketers build a clean, high-value user base and avoid rewarding publishers for fake users.
• Refund Fraud Detection – By tracking which purchases are later refunded through the app stores, businesses can identify advertising partners who drive users that exploit refund policies, a common form of IAP fraud.

Example 1: New User Purchase Velocity Rule

This pseudocode checks if a new user, attributed to a specific ad partner, makes an unusually high number of purchases within the first 24 hours. This helps catch bots programmed to simulate high engagement immediately after an install.

FUNCTION analyze_new_user_purchases(user):
  IF user.install_age < 24 hours:
    purchases = get_validated_purchases(user.id)
    
    IF count(purchases) > 5:
      // Flag the ad partner for review
      FLAG_PARTNER(user.source_partner, "High New User Purchase Velocity")
      // Invalidate revenue from this user for ROAS calculation
      MARK_USER_AS_SUSPICIOUS(user.id)

Example 2: Cross-Device Purchase Validation

This logic flags traffic as suspicious if multiple distinct user accounts, all attributed to the same advertising click ID, make separate purchases. This pattern can indicate a single fraudulent actor using a device farm or emulators to generate fake conversions.

FUNCTION check_click_to_purchase_ratio(click_id):
  // Get all users and purchases attributed to a single ad click
  associated_users = get_users_for_click(click_id)
  total_purchases = 0
  
  FOR user IN associated_users:
    total_purchases += count_validated_purchases(user.id)
  
  // A single click should ideally lead to one user's purchases
  IF count(associated_users) > 1 AND total_purchases > 1:
    FLAG_CLICK(click_id, "Click ID associated with multiple purchasers")

🐍 Python Code Examples

This code demonstrates a simple function to validate an in-app purchase. It checks for the presence of a receipt and simulates calling an external service to verify its authenticity, which is a core step in preventing revenue fraud.

def validate_purchase(purchase_event):
    """
    Validates an in-app purchase by checking for a receipt and its authenticity.
    Returns True for valid purchases, False for fraudulent ones.
    """
    receipt = purchase_event.get("receipt_data")
    source_publisher = purchase_event.get("publisher_id")

    if not receipt:
        print(f"FRAUD: No receipt found for purchase from {source_publisher}.")
        return False

    # In a real system, this would be an API call to Apple/Google
    is_authentic = external_receipt_validator(receipt)

    if not is_authentic:
        print(f"FRAUD: Invalid or reused receipt from {source_publisher}.")
        return False

    print(f"SUCCESS: Validated purchase from {source_publisher}.")
    return True

# --- Helper for simulation ---
def external_receipt_validator(receipt):
    # Simulate validation: reject receipts that are empty or contain "fake"
    return receipt is not None and "fake" not in receipt

# --- Example Usage ---
valid_event = {"publisher_id": "network_A", "revenue": 9.99, "receipt_data": "valid_receipt_string"}
fraud_event_1 = {"publisher_id": "network_B", "revenue": 29.99, "receipt_data": None}
fraud_event_2 = {"publisher_id": "network_C", "revenue": 49.99, "receipt_data": "fake_receipt"}

validate_purchase(valid_event)
validate_purchase(fraud_event_1)
validate_purchase(fraud_event_2)

This example identifies suspicious purchase frequency from a single user or IP address. It helps detect non-human or bot-like behavior where a user makes an unrealistic number of purchases in a short time, a common indicator of automated fraud.

from collections import defaultdict

# Store purchase timestamps per user_id
purchase_logs = defaultdict(list)
PURCHASE_TIME_THRESHOLD_SECONDS = 60 # 1 minute
MAX_PURCHASES_IN_THRESHOLD = 3

def record_and_check_purchase(user_id, timestamp):
    """
    Records a purchase and checks if the frequency is suspiciously high.
    """
    purchase_logs[user_id].append(timestamp)
    
    # Get recent purchases within the time window
    recent_timestamps = [t for t in purchase_logs[user_id] if timestamp - t < PURCHASE_TIME_THRESHOLD_SECONDS]
    
    if len(recent_timestamps) > MAX_PURCHASES_IN_THRESHOLD:
        print(f"FRAUD ALERT: User {user_id} made {len(recent_timestamps)} purchases in under a minute.")
        return False
        
    print(f"User {user_id} purchase recorded.")
    return True

# --- Example Simulation ---
user_A = "user_123"
record_and_check_purchase(user_A, 1672531200) # Purchase 1
record_and_check_purchase(user_A, 1672531210) # Purchase 2
record_and_check_purchase(user_A, 1672531220) # Purchase 3
record_and_check_purchase(user_A, 1672531230) # Purchase 4 -> triggers alert

Types of In app purchase

• Receipt Validation – This is the most direct form of IAP analysis. It involves sending the purchase receipt generated by the app store to a server for verification. This confirms the transaction was real and not a fake event sent by a fraudulent publisher to claim credit for a high-value user.
• Purchase Velocity Analysis – This method analyzes the time between an app install and the first purchase, as well as the frequency of subsequent purchases. A burst of rapid purchases immediately after install is a strong indicator of automated bot activity, not genuine user behavior.
• Refund and Chargeback Monitoring – This type tracks which users, and by extension which acquisition sources, have a high rate of refunded purchases. Fraudsters often make real purchases to trigger a CPA event and then request a refund, making this a critical metric for identifying IAP abuse.
• SKU and Price Point Analysis – This involves monitoring the types of items and price points being purchased. Fraudulent activity may focus on specific, low-friction SKUs or show unusual patterns, like purchasing items in a nonsensical order, which deviates from legitimate user behavior.
• Geographic Mismatch Detection – This technique cross-references the location of the user’s device, the currency used for the purchase, and the region of the app store. A mismatch, such as a purchase in Russian rubles from a device located in Vietnam, is a red flag for fraud.

🛡️ Common Detection Techniques

• Purchase Receipt Validation – This technique involves cryptographically verifying the transaction receipt with the app store (Apple or Google). It is the most definitive way to confirm a legitimate purchase occurred and wasn’t fabricated by fraudsters.
• Behavioral Anomaly Detection – This method flags non-human patterns, such as an impossibly short time between app install and first purchase or an unusually high frequency of purchases. It helps identify bots programmed to simulate valuable user actions.
• Transaction Geo-Mismatch – This technique compares the geographic location associated with the user’s device, the IP address, and the currency of the in-app purchase. A significant mismatch often indicates the use of proxies or other methods to conceal the user’s true origin.
• Device and User ID Profiling – This involves analyzing if the same device ID or user account is associated with an abnormally high number of transactions across different apps or has a history of suspicious refunds. This helps uncover coordinated fraud rings and device farms.
• Post-Install Event Correlation – This technique analyzes the user’s behavior leading up to a purchase. A legitimate user typically shows a pattern of engagement (e.g., completing levels, browsing items), while a fraudulent purchase often occurs with no preceding in-app activity.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
AppsFlyer Protect360	A multi-layered solution that provides real-time mobile ad fraud detection, including post-attribution protection against in-app event fraud and bots. It validates installs and in-app events to ensure budget is spent on real users.	Comprehensive protection (before, during, and after install), integrates with their attribution platform, uses cluster analysis to identify new threats.	Can be complex to configure custom rules. Cost may be prohibitive for smaller developers.
Adjust Fraud Prevention Suite	A proactive tool that filters out fraudulent traffic before attribution. It uses an encrypted SDK signature to prevent spoofing and distribution modeling to identify click spam, protecting against fake installs and events.	Real-time prevention, strong defense against SDK spoofing and click injection, anonymous IP filtering.	Primarily focused on pre-attribution prevention; may require other tools for deep post-install analysis.
Kochava Fraud Console	Offers real-time fraud detection and customizable rules to block fraudulent sources. It identifies and prevents tactics like click injection, install hijacking, and in-app purchase fraud across mobile and CTV campaigns.	Customizable thresholds and blocklists, global fraud blocklist, validates iOS install receipts.	The level of customization can be overwhelming for new users. Reporting interface can be dense.
TrafficGuard	Provides multi-layered ad fraud prevention for mobile and PPC campaigns. It verifies traffic from the impression level through to post-install events, ensuring advertisers only pay for genuine engagement and installs.	Full-funnel protection, provides detailed fraud reports for claiming refunds from ad networks, easy to get started.	May not have the same depth of mobile-specific attribution features as a dedicated MMP. Can be resource-intensive on high-volume campaigns.

📊 KPI & Metrics

Tracking both technical accuracy and business outcomes is essential when deploying in-app purchase validation. Technical metrics ensure the system correctly identifies fraud, while business metrics confirm that these actions are protecting revenue and improving advertising efficiency. This dual focus helps optimize fraud filters and demonstrate tangible value.

Metric Name	Description	Business Relevance
Invalid Purchase Rate	The percentage of total in-app purchase events that fail receipt validation and are flagged as fraudulent.	Measures the overall level of revenue fraud, helping to quantify the financial risk from malicious publishers.
False Positive Rate	The percentage of legitimate purchases that are incorrectly flagged as fraudulent by the system.	A high rate indicates overly aggressive rules that could harm relationships with honest ad partners and skew data.
ROAS from Verified Traffic	Return on Ad Spend calculated using only revenue from purchases that have passed validation.	Provides a true measure of campaign profitability by excluding fake revenue from performance metrics.
Partner Quality Score	A score assigned to each ad partner based on the percentage of their traffic that results in verified, non-refunded purchases.	Allows for data-driven decisions on which ad networks to invest in and which to block, optimizing ad spend.
Mean Time to Detect (MTTD)	The average time it takes for the system to identify and flag a fraudulent in-app purchase after it occurs.	A lower MTTD means faster action can be taken to block fraudulent sources and minimize financial damage.

These metrics are typically monitored through real-time dashboards provided by anti-fraud services or Mobile Measurement Partners. Alerts are often configured to notify teams of sudden spikes in invalid purchases or abnormal behavior from a specific ad partner. This feedback loop allows fraud analysts to quickly investigate, refine detection rules, and update blocklists to continuously adapt to new threats.

🆚 Comparison with Other Detection Methods

Detection Accuracy

In-app purchase validation offers very high accuracy for identifying revenue fraud because it relies on cryptographic verification from a trusted source (Apple/Google). In contrast, methods like IP blacklisting are less precise; a blacklisted IP might be a shared proxy used by both legitimate users and fraudsters, leading to false positives. Behavioral analytics can be highly effective but may struggle to interpret novel bot behaviors, whereas a failed receipt validation is a definitive signal.

Real-Time vs. Batch Processing

IAP validation can operate in near real-time, allowing for rapid flagging of fraudulent events and sources. This is faster than methods that rely on batch analysis of historical data, such as analyzing conversion rates over a 24-hour period. While some methods like real-time IP filtering are also instant, they lack the contextual proof of fraud that IAP validation provides. IAP validation gives confirmation that a specific, high-value conversion was fake.

Effectiveness Against Sophisticated Fraud

This method is highly effective against marketing fraud where publishers send fake revenue events to inflate their perceived quality. However, it does not stop install fraud or click spamming that happens before the purchase. A multi-layered approach is superior, combining IAP validation to verify revenue with other methods like click injection detection and distribution modeling to catch fraud at earlier stages of the user journey.

⚠️ Limitations & Drawbacks

While powerful, relying solely on in-app purchase validation for fraud detection is not a complete solution. Its effectiveness is confined to post-install revenue events, meaning it cannot prevent earlier forms of ad fraud like fake clicks or install hijacking. This can lead to a delayed detection of fraudulent sources.

• Scope Limitation – This method is only effective for apps that use an in-app purchase revenue model. It offers no protection for apps monetized through ads, subscriptions outside the app store, or other models.
• Delayed Detection – Fraud is only identified after a purchase event is attempted, which can be hours or days after the fraudulent install occurred. This means ad spend has already been wasted on acquiring the fake user.
• Doesn’t Stop Install Fraud – Sophisticated bots may never attempt a purchase. In these cases, IAP validation will not flag the fraudulent install, and advertisers may still pay for the fake acquisition if that is the campaign goal.
• Data Privacy and Implementation Complexity – Correctly and securely handling purchase receipts requires careful SDK implementation and can introduce privacy considerations. Mishandling this data can lead to errors or security vulnerabilities.
• Vulnerability to Refund Fraud – A fraudster can make a legitimate, validated purchase to appear as a quality user and later request a refund from the app store. While this can be tracked, it’s a separate process and not prevented by initial validation.

For comprehensive protection, hybrid strategies that combine IAP validation with real-time click and install analysis are more suitable.

❓ Frequently Asked Questions

How does in-app purchase validation help with ad campaign budgeting?

It ensures that Return on Ad Spend (ROAS) calculations are based on real, verified revenue. By filtering out fake purchase events, marketers can confidently allocate their budget to ad networks that deliver genuinely paying users and cut spending on fraudulent sources.

Can this method detect fraud if a purchase receipt looks real?

Yes. The core of the process is server-to-server validation with Apple or Google. Fraud prevention systems check if the receipt is cryptographically signed by the store and if the transaction ID has been used before. This prevents fraudsters from replaying old receipts or fabricating new ones.

What happens if an app doesn’t have in-app purchases?

In-app purchase validation is not applicable for apps that do not use this monetization model (e.g., hyper-casual games that rely on ad revenue). These apps must use other fraud detection methods like analyzing retention rates, session length, and identifying bot-like engagement patterns.

Is in-app purchase validation effective against bot traffic?

It is highly effective against bots programmed to fake purchase events. However, it does not stop bots that only generate installs or clicks without attempting a purchase. Therefore, it is best used as part of a multi-layered security approach that also detects anomalies earlier in the user journey.

Does this process violate user privacy?

When implemented correctly, it does not. The validation process uses anonymized transaction receipts and does not require personally identifiable information (PII). The communication is between the app’s server, the measurement partner, and the app store, focusing only on the transaction’s legitimacy rather than the user’s personal details.

🧾 Summary

In digital advertising fraud prevention, in-app purchase validation is a critical technique for verifying the quality of user acquisition campaigns. By checking transaction receipts with the app stores, it confirms that revenue-generating events are legitimate and not fabricated by bots or fraudulent publishers. This method helps protect ad budgets, ensures accurate ROAS calculations, and helps advertisers distinguish between fake traffic and real, high-value users.

🔗 Related Articles

• Mobile Ad Fraud Detection: Types, Techniques & Best Practices – https://focal.inc/blog/mobile-fraud-detection-types-techniques-best-practices
• In-app Payment Fraud Definition – https://fraudnet.ai/glossary/in-app-payment-fraud/
• In-App Purchase Validation FAQ – https://support.singular.net/hc/en-us/articles/360038314191-In-App-Purchase-Validation-FAQ
• How to Prevent Mobile Ad Fraud: Best Practices & Strategies – https://branch.io/resources/blog/how-to-prevent-mobile-ad-fraud/
• Mobile ad fraud prevention and detection solution – https://www.appsflyer.com/products/fraud-protection/