# Design an ad click fraud detection system: system design interview question

> Fast decisions and later fraud analysis must handle coordinated abuse, uncertain evidence, charge corrections, and errors against legitimate users.

Source: https://sysround.com/questions/design-ad-click-fraud-detection-system
Updated: 2026-11-05


Difficulty: hard. Levels: Senior, Staff+.

## How the interviewer opens it

"Design a service that detects invalid advertising clicks from bots and coordinated abuse. It must make fast decisions and assess past clicks while limiting errors against legitimate users."

## What to clarify first

- Decision meaning and charges
- Correction window and closure
- Scale and read to write mix
- Latency
- Availability, consistency, and durability
- Available signals and fraud types
- False positive definition
- Late events and labels
- Retention
- Scope limits

## Scope of a strong answer

Core requirements:

- Accept click events and return initial decisions
- Detect bots and coordinated abuse across related clicks
- Revise past decisions when new evidence arrives
- Apply charge rules and corrections within a fixed window

Usually out of scope:

- Ad auctions and ad selection
- Payment processing and invoice delivery
- Detailed model development
- A complete conversion attribution service

## What interviewers probe

### Decision speed and missing data

Each click needs a fast decision, but recent behavior arrives later. Missing evidence must not become proof of fraud.

### Related clicks across publishers and devices

Coordinated abuse can remain below each local threshold. Shared networks and popular publishers can produce similar patterns without fraud.

### Changes to past decisions

Late evidence can reverse decisions. Charge corrections must remain correct despite duplicate messages, delayed messages, and a fixed financial deadline.

### Errors against legitimate users

Labels cover a biased sample. Temporary holds and later revisions can harm legitimate traffic even when final decisions look correct.

## Common mistakes

- Treating a shared IP address as proof that clicks come from one actor
- Using only individual click rules and missing coordinated abuse
- Treating missing data as fraud evidence
- Handling decision versions without an atomic financial deadline check
- Leaving suspicious charge rules or closure behavior undefined
- Measuring only final errors and ignoring holds or earlier incorrect decisions

## The bar by level

- **Senior:** Designs feature windows, versioned corrections, and atomic closure. Evaluates cumulative false positives through independent audits. Explains failures and safe recovery.
- **Staff+:** Defines ownership across regions and reconciles decisions with frozen charges. Sets release limits despite biased labels. Plans clock authority, recovery capacity, and privacy changes.

Practice this question with an AI interviewer: https://sysround.com/practice/new?problem=advertising-click-fraud


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SysRound (https://sysround.com) is an AI practice tool for system design interviews. It has a voice or typed AI interviewer and a shared whiteboard. At the end, you get feedback on each skill and a model answer. The AI feedback can be wrong.