{"id":801,"date":"2025-12-17T09:54:59","date_gmt":"2025-12-17T09:54:59","guid":{"rendered":"https:\/\/gtaroads.com\/blog\/?p=801"},"modified":"2026-04-01T09:55:49","modified_gmt":"2026-04-01T09:55:49","slug":"anti-fraud-through-the-eyes-of-an-advertiser","status":"publish","type":"post","link":"https:\/\/gtaroads.com\/blog\/anti-fraud-through-the-eyes-of-an-advertiser\/","title":{"rendered":"Anti-Fraud Through the Eyes of an Advertiser: What to Look for in Stats and How to Fight Fraud"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Advertising fraud is any artificial impressions, clicks, installs, or conversions that look like real performance but don\u2019t bring you real users or sales.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"445\" src=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Top-Illustration-1024x445.png\" alt=\"\" class=\"wp-image-292\" srcset=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Top-Illustration-1024x445.png 1024w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Top-Illustration-300x130.png 300w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Top-Illustration-768x334.png 768w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Top-Illustration-370x161.png 370w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Top-Illustration-760x330.png 760w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Top-Illustration.png 1155w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">For an advertiser, that means:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Wasted budget<\/li>\n\n\n\n<li>Inflated CPA \/ CPI \/ CPL<\/li>\n\n\n\n<li>Broken analytics \u2013 it gets hard to understand what really works<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Fraud rarely looks like something obviously evil. Much more often it\u2019s hidden in patterns: strange CTR, \u201ctoo perfect\u201d conversions, abnormal time-to-conversion, suspicious IPs and devices.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this article we\u2019ll look at anti-fraud from the advertiser\u2019s point of view:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What signals to look for in your stats<\/li>\n\n\n\n<li>Which fraud patterns are the most common (suspicious CTR, abnormal time-to-conversion, proxy \/ bots)<\/li>\n\n\n\n<li>How a typical ad network anti-fraud system works<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">1. Key Metrics and Basic Formulas<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">First, let\u2019s align on the main performance metrics and how to calculate them.<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">CTR (Click-Through Rate, %) = (Clicks \/ Impressions) \u00d7 100\n\nCR (Conversion Rate, %) = (Conversions \/ Clicks) \u00d7 100\n\neCPC (effective CPC) = Spend \/ Clicks\n\neCPA (effective CPA \/ CPI \/ CPL) = Spend \/ Conversions\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">For time-to-conversion we usually look at distributions:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Median TTC = median(Time from click to conversion)\n\nP90 TTC = 90th percentile of Time to conversion\n<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">In practice you don\u2019t need super complex math. The main idea is:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Compare metrics across <strong>placements \/ sources<\/strong> to find anomalies<\/li>\n\n\n\n<li>Compare them to your own <strong>historical baseline<\/strong> (your past campaigns)<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">2. Suspicious CTR: When a \u201cGood\u201d Metric Becomes a Warning Sign<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">CTR (Click-Through Rate) is one of the easiest places where fraud shows up.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2.1. Formula for CTR<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted\">CTR (%) = (Clicks \/ Impressions) \u00d7 100\n<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">2.2. What\u2019s \u201cnormal\u201d and what looks suspicious<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cNormal\u201d CTR depends on vertical, format, and traffic source, but you can use these rules of thumb:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Display \/ banner ads:<\/strong> 0.3\u20133%<\/li>\n\n\n\n<li><strong>Native \/ feed ads:<\/strong> 1\u20135%<\/li>\n\n\n\n<li><strong>Push \/ in-app rich formats:<\/strong> 2\u201310%<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of absolute values, look at anomalies relative to your averages.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Heuristic thresholds (per placement \/ site \/ source):<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>CTR &gt; 3\u00d7 campaign average<\/strong> and\n<ul class=\"wp-block-list\">\n<li>CR is low <strong>(CR &lt; 0.3\u00d7 campaign average)<\/strong>, or<\/li>\n\n\n\n<li>eCPA is much higher <strong>(eCPA &gt; 1.5\u20132\u00d7 campaign average)<\/strong><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Sudden CTR spikes by &gt;100\u2013200%<\/strong> in a short time (same creatives, same bids)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">If CTR is abnormally high but users do not convert or behave like real humans, this is a strong fraud signal (click-spam, auto-clicks, incentivized clicks, etc.).<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">3. Abnormal Time-to-Conversion (TTC)<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"617\" src=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001-1024x617.webp\" alt=\"\" class=\"wp-image-293\" srcset=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001-1024x617.webp 1024w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001-300x181.webp 300w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001-768x463.webp 768w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001-1536x926.webp 1536w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001-370x223.webp 370w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001-760x458.webp 760w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/jcp-01-00039-g001.webp 1966w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Time-to-conversion (TTC) is the lag between click and target action (install, signup, purchase, lead, etc.).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3.1. Key TTC metrics<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted\">TTC for each conversion = Conversion time \u2013 Click time\n\nMedian TTC = median(TTC across conversions)\n\nP90 TTC = value where 90% of conversions happen faster\n<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">3.2. What looks realistic<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Mobile app install:<\/strong> usually seconds to a few minutes<\/li>\n\n\n\n<li><strong>Simple signup \/ short form:<\/strong> 30 seconds \u2013 5 minutes<\/li>\n\n\n\n<li><strong>Complex form \/ loan \/ big order:<\/strong> 2\u201310 minutes or even longer<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The exact numbers depend on your funnel, but even without exact benchmarks you can see abnormal patterns.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3.3. Suspicious TTC patterns and thresholds<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pattern 1: Instant conversions<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Complex action (loan lead, long signup, multi-step funnel)<\/li>\n\n\n\n<li>Large share of conversions happens in <strong>0\u20133 seconds<\/strong> after the click<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Heuristic threshold:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>For actions that normally take &gt;30\u201360 seconds: <strong>if \u226510\u201320% of conversions have TTC \u22643 seconds, that\u2019s a red flag.<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pattern 2: \u201cPerfectly even\u201d TTC distribution<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Conversions come in a flat, mechanical pattern (e.g., exactly 5 conversions every 10 minutes)<\/li>\n\n\n\n<li>No daily cycles (morning vs night, weekdays vs weekends)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Real users convert in waves, not like a metronome.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pattern 3: Too many ultra-fast \u201cperfect\u201d conversions<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Most users complete a complex form in 5\u201315 seconds<\/li>\n\n\n\n<li>Many fields look random or identical<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is typical of automated form filling and script-based conversions.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">4. On-Site \/ In-App Behavior: How Real Users Differ from Bots<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Even if your network doesn\u2019t give behavior stats, you can track them via analytics tools (GA4, Appsflyer, Firebase, etc.).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Behavioral red flags per source \/ placement:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Average time on site &lt; 3\u20135 seconds<\/strong> with a lot of clicks<\/li>\n\n\n\n<li><strong>Pages per session \u2248 1<\/strong> while normal traffic shows 2\u20134 pages<\/li>\n\n\n\n<li><strong>Bounce rate &gt; 80\u201390%<\/strong> for that source<\/li>\n\n\n\n<li>Almost no interactions:\n<ul class=\"wp-block-list\">\n<li>Few scrolls<\/li>\n\n\n\n<li>Few clicks on buttons \/ links<\/li>\n\n\n\n<li>No meaningful events triggered<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Zero or extremely low <strong>returning users \/ retention<\/strong> from that source<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Fraud logic is simple: the bot comes, triggers whatever needs to be fired (pixel, script, event), then leaves with no real behavior.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">5. Technical Signals: IPs, Devices, Proxy \/ VPN<\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1020\" height=\"720\" src=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Ad-Fraud-Statistics.avif\" alt=\"\" class=\"wp-image-294\" srcset=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Ad-Fraud-Statistics.avif 1020w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Ad-Fraud-Statistics-300x212.avif 300w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Ad-Fraud-Statistics-768x542.avif 768w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Ad-Fraud-Statistics-370x261.avif 370w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/Ad-Fraud-Statistics-760x536.avif 760w\" sizes=\"auto, (max-width: 1020px) 100vw, 1020px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Technical parameters (IP, user-agent, device type, OS, geo) are extremely useful for detecting fraud.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5.1. What to monitor<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>IP and subnets<\/strong>\n<ul class=\"wp-block-list\">\n<li>Unusually high number of clicks \/ conversions from the same IP or narrow subnet (e.g. \/24)<\/li>\n\n\n\n<li>Large share of traffic from data-center \/ hosting IPs (not residential ISPs)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>User-Agent &amp; device<\/strong>\n<ul class=\"wp-block-list\">\n<li>Very large share of traffic with <strong>one identical user-agent string<\/strong><\/li>\n\n\n\n<li>Unrealistic or outdated browsers \/ OS versions that you almost never see in your good traffic<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Geo<\/strong>\n<ul class=\"wp-block-list\">\n<li>Geo that doesn\u2019t match your targeting but still \u201cconverts\u201d<\/li>\n\n\n\n<li>Geo that jumps around while IP ranges and devices remain similar<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Heuristic thresholds:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>&gt;20\u201330% of clicks from data-center \/ hosting IPs<\/strong> for a consumer offer = strong suspicion<\/li>\n\n\n\n<li><strong>&gt;50% of traffic with the same user-agent<\/strong> on a supposedly diverse campaign = red flag<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">6. Typical Fraud Patterns (With Numbers)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">6.1. Suspiciously high CTR with bad performance<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Scenario:<\/strong> Campaign runs for a week. Average CTR = 1.5%. One specific placement shows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CTR = 18\u201320% (that\u2019s <strong>12\u00d7<\/strong> higher than the average)<\/li>\n\n\n\n<li>CR = 0.2% (while campaign average is 1.0\u20131.5%)<\/li>\n\n\n\n<li>eCPA is 2\u20133\u00d7 higher than your campaign average<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Threshold example:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If for a placement:<ul><li><strong>CTR &gt; 3\u00d7 campaign median<\/strong>, and<\/li><li><strong>CR &lt; 0.5\u00d7 campaign median<\/strong>, or<\/li><li><strong>eCPA &gt; 1.5\u20132\u00d7 campaign median<\/strong><\/li><\/ul>then this placement is a strong candidate for a manual review or pause.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6.2. Abnormal time-to-conversion for a complex offer<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Scenario:<\/strong> Offer: loan application with a detailed form. Normal flow (based on your historical data):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Median TTC = 3 minutes<\/li>\n\n\n\n<li>P90 TTC = 8\u201310 minutes<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">New source appears with:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Median TTC = 15 seconds<\/li>\n\n\n\n<li>30% of conversions have TTC \u2264 5 seconds<\/li>\n\n\n\n<li>Many leads fail your internal scoring, or data looks random<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Heuristic rule:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If <strong>Median TTC &lt; 0.2\u00d7 your normal median<\/strong> for a complex action, and<\/li>\n\n\n\n<li><strong>\u226520\u201330% of conversions have TTC \u2264 5 seconds<\/strong>,<\/li>\n\n\n\n<li>this traffic should be marked as high-risk and potentially fraudulent.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6.3. Proxy \/ bot traffic with repeating patterns<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Scenario:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Most clicks come from a narrow set of data-center IP ranges<\/li>\n\n\n\n<li>80% of traffic has the same user-agent and screen resolution<\/li>\n\n\n\n<li>Clicks and conversions arrive in perfectly even batches, e.g., 10 clicks and 1 conversion every 5 minutes, 24\/7<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Real users don\u2019t behave that way. This is classic scripted or bot traffic.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">7. What Advertisers Can Do to Fight Fraud<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anti-fraud is not \u201cthe network\u2019s problem only\u201d. Advertisers have their own strong levers of control.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">7.1. Tracking design and feedback loop<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Implement proper tracking:<\/strong>\n<ul class=\"wp-block-list\">\n<li>UTM tags, click IDs, subIDs (sub1\u2013sub5) for placement IDs, source, creative, etc.<\/li>\n\n\n\n<li>This lets you quickly pinpoint fraudulent sources and placements.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Send conversion statuses back to the network:<\/strong>\n<ul class=\"wp-block-list\">\n<li><code>approved \/ rejected \/ hold<\/code><\/li>\n\n\n\n<li>Reasons: fake data, duplicate, invalid, fraud, etc.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Use S2S postbacks \/ API<\/strong> so the network sees not only the raw conversion, but also:\n<ul class=\"wp-block-list\">\n<li>Was the loan approved?<\/li>\n\n\n\n<li>Was the order paid?<\/li>\n\n\n\n<li>Did the user reach key in-app events?<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The more feedback the network gets, the better its anti-fraud model becomes for your specific business goals.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">7.2. Regular stats review with numeric rules<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Define a basic set of reports you check regularly:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>By <strong>placement \/ site ID \/ app ID<\/strong><\/li>\n\n\n\n<li>By <strong>geo, device, OS<\/strong><\/li>\n\n\n\n<li>By <strong>time (hour of day, day of week)<\/strong><\/li>\n\n\n\n<li>By <strong>source \/ subIDs<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Watch these metrics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CTR, CR<\/li>\n\n\n\n<li>eCPC, eCPA \/ eCPI<\/li>\n\n\n\n<li>On-site behavior (time on site, pages per session, bounce rate)<\/li>\n\n\n\n<li>Down-funnel metrics: approval rate, LTV, retention, ARPU<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example numeric rules you can use:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>CTR rule:<\/strong> placement CTR &gt; 3\u00d7 campaign median\n<ul class=\"wp-block-list\">\n<li>AND CR &lt; 0.5\u00d7 campaign median, OR<\/li>\n\n\n\n<li>eCPA &gt; 1.5\u00d7 campaign median \u2192 mark as suspicious and review manually.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>TTC rule (complex actions):<\/strong>\n<ul class=\"wp-block-list\">\n<li>Median TTC &lt; 0.2\u00d7 historical median, AND<\/li>\n\n\n\n<li>\u226520\u201330% conversions with TTC \u2264 5 seconds \u2192 high fraud risk.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Behavior rule:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Average time on site &lt; 3 seconds, AND<\/li>\n\n\n\n<li>Bounce rate &gt; 85\u201390%, AND<\/li>\n\n\n\n<li>CR not significantly higher than other traffic \u2192 strong candidate for blocking.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>IP \/ UA rule:<\/strong>\n<ul class=\"wp-block-list\">\n<li>&gt;20\u201330% of clicks from data-center IPs, OR<\/li>\n\n\n\n<li>&gt;50% of traffic from the same user-agent \u2192 source should be treated as high-risk.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7.3. Filters and rules on the advertiser side<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">You can apply extra protection on top of the network\u2019s anti-fraud layers:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Blacklists:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Block placements \/ sources that consistently show fraud patterns<\/li>\n\n\n\n<li>Block suspicious IPs \/ subnets (if you log them)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Caps and limits:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Limit daily volume from new sources (e.g. \u22645\u201310% of your total budget per new partner)<\/li>\n\n\n\n<li>Increase caps only for sources that show stable quality<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Targeting restrictions:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Exclude regions, device types, or OS versions with high fraud share<\/li>\n\n\n\n<li>Turn off formats that are known to be risky for your vertical (e.g., certain redirect traffic)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Protect your forms \/ apps:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Basic CAPTCHAs (where UX allows it)<\/li>\n\n\n\n<li>\u201cHoneypot\u201d hidden fields that only bots will fill<\/li>\n\n\n\n<li>Server-side validation of data<\/li>\n\n\n\n<li>Phone or email confirmation for high-risk flows<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7.4. Working together with the network<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Anti-fraud works best when the advertiser and the network act like partners, not enemies.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Start new sources and partners with <strong>test budgets and strict caps<\/strong><\/li>\n\n\n\n<li>Agree on <strong>quality KPIs<\/strong> upfront:\n<ul class=\"wp-block-list\">\n<li>Minimum approval rate<\/li>\n\n\n\n<li>Accepted fraud level and how to handle chargebacks<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Share analytics regularly:\n<ul class=\"wp-block-list\">\n<li>Which leads were rejected and why<\/li>\n\n\n\n<li>Which sources had high fraud and low LTV<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">8. How a Typical Ad Network Anti-Fraud System Works<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"419\" src=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/the-case-of-advertising-fraud-03-1024x419.jpg\" alt=\"\" class=\"wp-image-295\" srcset=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/the-case-of-advertising-fraud-03-1024x419.jpg 1024w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/the-case-of-advertising-fraud-03-300x123.jpg 300w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/the-case-of-advertising-fraud-03-768x314.jpg 768w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/the-case-of-advertising-fraud-03-370x151.jpg 370w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/the-case-of-advertising-fraud-03-760x311.jpg 760w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/12\/the-case-of-advertising-fraud-03.jpg 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Every network has its own implementation, but the architecture is usually similar.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">8.1. Data collection and enrichment<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The network collects:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Impression and click logs:\n<ul class=\"wp-block-list\">\n<li>Time, IP, user-agent, placement, site\/app ID<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Conversion logs:\n<ul class=\"wp-block-list\">\n<li>Click ID, time, event type<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Additional signals:\n<ul class=\"wp-block-list\">\n<li>Referrer, cookies \/ device ID, fingerprint, geo, connection type<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Then the data gets enriched with:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Geo detection by IP<\/li>\n\n\n\n<li>IP type (residential \/ data center \/ proxy \/ VPN)<\/li>\n\n\n\n<li>Normalized user-agent (device model, OS, browser version)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">8.2. Rule-based layer and risk scoring<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">On the first layer, there are hard rules such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Too many clicks \/ conversions from one IP or device in a short window<\/li>\n\n\n\n<li>Clicks \/ impressions from blacklisted or high-risk IP ranges<\/li>\n\n\n\n<li>Abrupt spikes in CTR or CR on a single placement<\/li>\n\n\n\n<li>Instant conversions with obviously impossible TTC<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Some traffic is rejected outright. For the rest, the system assigns <strong>a risk score<\/strong>: the more suspicious factors, the higher the risk.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">8.3. Machine learning layer<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Above the rules, networks often run ML models trained on labeled data:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Rejected \/ chargeback conversions<\/li>\n\n\n\n<li>Known fraud campaigns<\/li>\n\n\n\n<li>Advertiser feedback on lead quality<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The model looks at dozens or hundreds of features:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Time-based patterns<\/li>\n\n\n\n<li>IP \/ device behavior<\/li>\n\n\n\n<li>Combinations of geo, device, TTC, CTR \/ CR, and more<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Its goal is to estimate the probability that a specific click \/ lead \/ install is fraudulent.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">8.4. Actions the system can take<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Depending on settings and contracts with advertisers, the system may:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Block<\/strong> events (not show them in stats, not bill them)<\/li>\n\n\n\n<li><strong>Flag<\/strong> them as risky (e.g. separate them in reports)<\/li>\n\n\n\n<li><strong>Put on hold<\/strong> until the advertiser sends final statuses<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">No system is perfect. Networks must balance between:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Blocking as much fraud as possible<\/li>\n\n\n\n<li>Not killing legitimate but \u201cweird-looking\u201d traffic<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is why your feedback as an advertiser (approved \/ rejected conversions, reasons, LTV, retention) is crucial.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">9. Advertiser\u2019s Anti-Fraud Checklist (With Practical Thresholds)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Before launching a campaign<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Set up tracking with:\n<ul class=\"wp-block-list\">\n<li>UTMs, click IDs, subIDs for sources and placements<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Define which conversion statuses and quality signals you can pass back (approve, reject, reasons, down-funnel events)<\/li>\n\n\n\n<li>Agree with the network on:\n<ul class=\"wp-block-list\">\n<li>Quality KPIs (approval rate, acceptable fraud level)<\/li>\n\n\n\n<li>How chargebacks and fraud disputes are handled<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">During the campaign<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Monitor regularly:\n<ul class=\"wp-block-list\">\n<li>CTR, CR, eCPC, eCPA per source \/ placement<\/li>\n\n\n\n<li>Behavior: time on site, pages per session, bounce rate<\/li>\n\n\n\n<li>Time-to-conversion distribution per source<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Apply numeric rules:\n<ul class=\"wp-block-list\">\n<li>Stop or cap placements where:\n<ul class=\"wp-block-list\">\n<li><strong>CTR &gt; 3\u00d7 median<\/strong> and <strong>CR &lt; 0.5\u00d7 median<\/strong><\/li>\n\n\n\n<li>or <strong>eCPA &gt; 1.5\u20132\u00d7 median<\/strong><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Flag sources where:\n<ul class=\"wp-block-list\">\n<li>Median TTC &lt; 0.2\u00d7 normal median for a complex offer<\/li>\n\n\n\n<li>and \u226520\u201330% conversions have TTC \u2264 5 seconds<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Consider blocking sources where:\n<ul class=\"wp-block-list\">\n<li>Average time on site &lt; 3 seconds<\/li>\n\n\n\n<li>and bounce rate &gt; 85\u201390%<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Treat as high-risk:\n<ul class=\"wp-block-list\">\n<li>Sources with &gt;20\u201330% data-center IPs or &gt;50% identical user-agent traffic<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">After the campaign<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Review performance:\n<ul class=\"wp-block-list\">\n<li>Which sources brought the best LTV \/ approval rate<\/li>\n\n\n\n<li>Which showed stable fraud patterns<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Send detailed feedback to the network:\n<ul class=\"wp-block-list\">\n<li>Lists of rejected leads with reasons<\/li>\n\n\n\n<li>Notes on suspicious sources \/ placements and why<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anti-fraud is not a magic button inside the ad network. It\u2019s a <strong>joint process<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The network tracks technical and behavioral anomalies on its side<\/li>\n\n\n\n<li>The advertiser tracks business metrics and user quality on their side<\/li>\n\n\n\n<li>Both exchange data continuously<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The better you as an advertiser understand <strong>which signals in your stats may indicate fraud<\/strong>, and the more you formalize them with simple formulas and thresholds, the faster you can cut bad traffic, protect your budget, and help the network\u2019s anti-fraud system learn what \u201cquality\u201d means for your business.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Advertising fraud is any artificial impressions, clicks, installs, or conversions that look like real performance but don\u2019t bring you real users or&hellip;<\/p>\n","protected":false},"author":3,"featured_media":291,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[127],"tags":[86,139,49,138],"class_list":["post-801","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-affiliate","tag-advertiser","tag-antifraud","tag-publisher","tag-user"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - 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