Mobile Ad Fraud Detection: 7 Patterns You Can Spot in 15 Minutes (Fraud Prevention Checklist Included)

Table of Contents

1. Pattern #1: Click-to-install time is "too perfect"

• Quickly spot potential automation red flags when installs align too closely in timing.

  2. Pattern #2: Spiky installs that don’t match spend

• Recognise unexpected install surges that aren't backed by corresponding ad budget increases.

  3. Pattern #3: High installs, low post-install actions

• Identify volume-driven fraud where installs don't translate to genuine user engagement.

  4. Pattern #4: Geo mismatch and location clustering

• Detect holistic discrepancies between targeted and actual install regions.

  5. Pattern #5: Conversion outliers from questionable placements

• Spot unusually high conversion rates from suspiciously low-quality sources.

  6. Pattern #6: Device/identity anomalies

• Look for non-standard device or identity patterns indicating fraud.

  7. Pattern #7: Deep link “success” without real engagement

• Uncover fake engagements where deep linking metrics don't lead to meaningful actions.

  8. How to Investigate in Linkzly (Step-by-step)

• 15-minute fraud prevention checklist

Pattern #1: Click-to-install time is “too perfect”

An unnaturally tight click-to-install (CTIT) distribution, such as a large share of installs occurring within a short time frame, is a common indicator of automation or click manipulation.

Fraud prevention action: Use CTIT clustering as an initial screening signal and validate findings with post-install engagement data.

Internal link to add: Link “time-to-install metrics” to Linkzly Analytics, which highlights time-to-install and click-to-install funnel reporting. Source

Pattern #2: Spiky installs that don’t match spend

Fraud may present as sudden surges in installs that do not correspond to campaign changes. Investigate whether installs increase while spend remains flat, or whether spikes occur at unusual times.

Fraud prevention action: Pause or cap the source until you confirm quality through downstream events and attribution validation.

Pattern #3: High install volume, weak post-install actions

A quick test for mobile ad fraud is when installs increase, but real user actions, such as registrations, purchases, or onboarding completions, do not.

Linkzly’s analytics positioning emphasises tracking installs, post-install engagement, and funnel metrics, helping you distinguish between volume and quality. Source

Fraud prevention action: Optimise campaigns for value events rather than focusing solely on install volume.

Pattern #4: Geo mismatch and unusual location clusters

If a campaign targets specific regions but deploys clusters in unexpected areas, or if a single small region suddenly dominates, consider this suspicious. Possible causes include device farms, proxies, or misrepresented inventory.

Fraud prevention action: Tighten geo-targeting, compare performance by region, and segment reporting by source and geography.

Pattern #5: Abnormally high conversion from low-quality placements

A partner with performance that appears unusually strong, especially if their click-to-install rate is significantly higher than other channels, may be a warning sign.

Fraud prevention action: Audit placement lists, exclude suspicious inventory, and verify that high-performing sources also deliver retention and revenue.

Linkzly describes click funnels and attribution breakdowns that help diagnose whether conversions align with typical user journeys. Source

Pattern #6: Device/identity anomalies

Possible red flags include:

• unusually concentrated device models/OS versions

• strange identity behaviour (too many near-duplicates, suspicious resets)

Fraud prevention action: Use device-level breakdowns to confirm that the distribution corresponds to your actual audience.

Linkzly Analytics describes device/platform mix and bot filtering/spam detection designed to keep reporting clean. Source

Pattern #7: Deep link “success” without substantive engagement

Deep linking is intended to improve UX and conversion rates by routing users to the right screen. Linkzly Deep Links emphasise attribution-ready clicks plus deferred deep linking so users land in the correct in-app destination even after install. Source

However, fraud can mimic superficial opens or shallow user sessions.

Fraud prevention action: Compare deep link opens with downstream events, such as add-to-cart, purchase, registration, and retention.

How to Investigate in Linkzly (Step-by-step)

The following workflow outlines how to validate suspicious traffic in Linkzly, using the signals described above.

Step 1) Confirm attribution-ready tracking is in place (before analysis).

It's crucial to ensure your data remains untainted from the outset. What if your data were already corrupted when the audit starts? Such a scenario could lead to wasted efforts and inaccurate conclusions. To prevent this, use Linkzly Deep Links so every click has a trackable context and can be matched to installs (including across install via deferred deep linking). Linkzly describes Deep Links as "attribution-ready out of the box," including click IDs and device fingerprint capture.

What to do:

• Ensure campaigns route through Deep Links (or Smart App flow), so clicks, installs, and conversions connect cleanly.

• If you’re unsure about your end-to-end setup, refer to the platform workflow overview. Source

Step 2) Use the click-to-install funnel to spot CTIT anomalies.

In Linkzly Analytics, look for funnel views and time-to-install metrics to identify overly tight CTIT distributions and sharp changes. Linkzly explicitly lists “Click-to-Install Funnel” and “Time-to-install metrics” among tracked analytics. Source

What to look for:

• CTIT clustering

• sudden funnel shape changes by source/partner

• sudden increases in install rate with no change in click volume

Step 3) Segment performance by source, geo, and device

Linkzly Analytics highlights geographic breakdowns (country/region/city) and device/platform insights. By segmenting performance, you can effectively identify trends and anomalies. For instance, after segmentation, one client reduced wasted spend by 27%, demonstrating the tangible benefits of this approach.

What to look for:

• geo mismatch (target vs observed)

• Suspicious concentration of installs in one city/region

• abnormal device/OS distribution

Step 4) Validate quality using post-install engagement + custom events.

Linkzly tracks post-install engagement and custom in-app events, tying them back to the install source. Source

What to do:

• Compare installs vs activation events (signup, tutorial_complete, add_to_cart, purchase) by source.

• Flag sources with high installs but weak event rates

Step 5) Check bot/spam filtering indicators.

Linkzly Analytics states it includes bot filtering, spam detection, and suspicious activity filtering to support trustworthy reporting. Source

What to do:

• Review bot-filtered counts and suspicious activity indicators.

• Cross-check whether “top” sources are also the biggest contributors to filtered traffic.

Step 6) Decide: fix, quarantine, or cut.

Once you identify a suspicious source:

• Fix: tighten targeting, adjust attribution windows, and require stronger conversion events.

• Quarantine: cap budgets until event quality improves

• Cut: exclude placements/sources that repeatedly fail quality checks.

If your app is commerce-focused, you can also connect fraud prevention to revenue attribution and SKU-level deep linking using Linkzly’s e-commerce attribution approach. Source

15-minute Fraud Prevention Checklist

Use this weekly:

• CTIT distribution by partner/source (look for “too perfect” timing)

• Installs vs spend by hour/day (look for unexplained spikes)

• Installs vs activation events (look for hollow volume)

• Geo targeting vs observed geo (look for mismatches/clusters)

• Click → install CVR outliers (too high vs baseline)

• Device/OS anomalies (unexpected concentration)

• Deep link opens vs meaningful actions (shallow engagement)

• Bot/spam filtering review (confirm clean reporting) Source

If you want to turn mobile ad fraud detection into an always-on fraud prevention workflow, start by using Deep Links to capture attribution-ready clicks and connect them to installs and events. Then monitor anomalies in Linkzly Analytics in real time.

• Deep Links: https://linkzly.com/smart-link

• Analytics: https://linkzly.com/analytics

• How it works: https://linkzly.com/how-it-works