Affiliate programs lose money to traffic they should never have paid for, and most of them only find out after the payout has gone through. That gap is the business. Companies already pay vendors, agencies and in-house specialists to catch bad conversions, so a person who can read tracker data, spot what looks wrong and explain it clearly has something to sell. This guide covers how to make money with affiliate fraud audits: what the work involves, what clients pay for, how to package it, how AI helps, and where it gets hard.
One thing up front. Public pricing for stand-alone fraud audits is thin, and I found a lot of vendor marketing dressed up as statistics. Where a number comes from a vendor, I say so. Where it is my own arithmetic, it is labeled illustrative. Nothing here is a promise that you will earn a particular amount.
Why Companies Pay to Find Affiliate Fraud
Every fraudulent conversion is paid twice: once in the commission, and again in the decisions made on bad data. If you scale a source because it looks profitable, and the profit was fake, the mistake compounds.
Illustrative calculation: a program pays $150 CPA and gets 500 FTDs a month. That is $75,000 in monthly affiliate spend. If 5% of those conversions turn out to be invalid, that is 25 FTDs, or $3,750 a month. At 10% it is $7,500. These are made-up percentages, not benchmarks. The point is that a single-digit share of bad traffic can cover the price of an audit within a couple of months, provided the money can actually be recovered or the payouts stopped.
That condition matters. Fraud detected is not money saved. A flagged conversion that the advertiser has already paid, with no clawback clause in the partner terms, is a finding but not a refund. Money is saved when payouts are reversed, a source is cut before the next invoice, or budget moves to something that works. A good audit report says which of the three is realistic.
There are real numbers on how large the problem can get. In the lawsuit Uber filed over mobile install attribution, CNBC reported that Uber paid the agency Fetch Media more than $82.5 million between 2016 and the first quarter of 2017. Spend on the account grew from under $1 million a month in late 2015 to over $6 million a month in late 2016. Kevin Frisch, who ran Uber’s performance marketing at the time, later said the company turned off $100 million of a $150 million budget and installs did not change. He told reporters that if you only look at the higher-level reporting, “you just don’t catch it.” This is an app-install case, not a classic affiliate program, and the figures come from litigation and press coverage. But it shows what attribution manipulation looks like at scale, and how it surfaces through testing rather than dashboards.
Smaller, program-level cases exist too. impact.com describes a case where PUMA found fraud tied to a cash-on-delivery loophole. According to impact.com, closing it cut fraud by 90% and sales rose 2.5 times. That is a vendor-published case study, so treat it as one data point, not a typical result.
And audits can come back clean. Ben Edelman and Wesley Brandi at Harvard Business School reported years ago that in nearly half the programs they tracked, they found no fraud at all, while the most targeted merchants saw dozens of incidents. The study is old, but it is a useful reminder: your service sells the answer, not the alarm.
What Is an Affiliate Fraud Audit?
An affiliate fraud audit is a structured review of a program’s traffic and conversion data to find patterns that do not fit normal behavior, trace them to a source, and report what is likely wrong, how sure you are, and what to do next. It does not end with a verdict on a person. It ends with evidence, a confidence level and a recommendation.
Here is what gets looked at.
| Area | What You Check | Example Warning Signal |
|---|---|---|
| Clicks and traffic | Volume by affiliate, source, subID, device | Click volume jumps with no matching change in campaign setup |
| Conversions | Count, payout, status, reversals | Conversions land in tight bursts, then stop |
| Timing | Click-to-conversion gap, time of day | Large share of conversions within seconds of the click |
| IP and device | Repeats, ranges, hosting providers, user agents | Many conversions from one device fingerprint or subnet |
| GEO | Click country vs. conversion or payment country | Traffic from a GEO the offer does not accept, converting anyway |
| Postbacks and attribution | Click IDs, event order, duplicates | Conversion event recorded before the click, or missing click ID |
| Partner history | Past CR, EPC, approval and reversal rates | A long-stable partner changes pattern overnight |
| Downstream quality | Deposits, retention, refunds, lead validity | Good front-end metrics, near-zero activity after signup |
4 Ways to Make Money From Affiliate Fraud Detection
1. One-Off Affiliate Fraud Audits
A fixed scope, a fixed data window, one report. The client gives you a month or a quarter of affiliate data; you return a ranked list of findings with evidence and a suggested action for each. Typical turnaround is a couple of weeks, depending on how fast the data arrives, which is usually the slowest part.
The closest public example I could verify is from AffiliateManager.Expert, which lists a flat-fee tracking review at $1,000, with written findings within 14 business days of data access and a refund if it delivers fewer than three actionable recommendations. Careful here: that $1,000 is for the tracking review, not a fraud audit. The site has separate fraud and compliance audit pages, and I could not find a published price for them. They are described as having free and paid options.
Agency price guides run higher. Jolly Consulting quotes affiliate audits at $2,500 to $7,500, and Hamster Garage puts audit projects at $5,000 to $20,000. Both are agency marketing pages rather than market surveys, and neither is specific to fraud. Read them as a ceiling for broad program audits, not a benchmark for what a new freelancer can charge.
2. Monthly Fraud Monitoring
The recurring version. Instead of one report, the client gets a regular review of new data: anomaly summaries, a watchlist of partners and subIDs, alerts when something crosses an agreed threshold, and investigation time when a flag needs digging into. Revenue is steadier, but so is the obligation. If you miss something in month three that you would have caught in month one, the client will notice. I found no reliable public price list for this model, so any figure you see in an article, including mine later, is a planning assumption.
3. Affiliate Fraud Consulting
Consulting is for clients who want to stop the next problem, not just find the last one. That can mean writing fraud rules into partner terms, setting up partner vetting, fixing tracking configuration, defining reversal and clawback policies, or training an in-house manager to run reviews. impact.com’s own guidance leans this way: it argues that the managers who prevent the most fraud are the ones who know their partners well. Process and relationships, not just software.
4. Fraud Detection SaaS
Software with dashboards, integrations and automated alerts, sold by subscription. Public pricing exists here, which makes it the best-documented model. 24metrics, for example, lists three tiers on its pricing page, updated in August 2026: Basic at €120 a month billed annually (up to 50,000 clicks and 5,000 conversions), Advanced at €500 a month (500,000 clicks, 50,000 conversions, one hour of expert advice monthly), and Enterprise from €2,500 a month with consulting on fraud cases. Other 24metrics pages still show different entry prices, so confirm before quoting anyone.
SaaS also means you carry uptime, integrations with every tracker and network, support, security reviews and a sales cycle against well-funded vendors. impact.com markets fraud protection as part of its platform, and Awin runs its own publisher screening. That is real competition for any stand-alone tool.
How Much Can You Charge for an Affiliate Fraud Audit?
Short answer: nobody publishes a clean market rate. What exists is a mix of flat fees, SaaS tiers and free audits that vendors use to win customers. Here is what I could verify.
| Provider | Service | Public Price | What’s Included |
|---|---|---|---|
| AffiliateManager.Expert | Tracking review | $1,000 flat | Written findings within 14 business days of data access; refund if fewer than 3 actionable recommendations |
| AffiliateManager.Expert | Fraud and compliance audits | Not published | Free and paid options described; no price found |
| 24metrics | SaaS Basic | €120/mo (annual billing) | Up to 50k clicks and 5k conversions |
| 24metrics | SaaS Advanced | €500/mo | Up to 500k clicks and 50k conversions, 1 hour of expert advice monthly |
| 24metrics | Enterprise | From €2,500/mo | Custom volume, consulting on fraud cases |
| TrafficGuard | Affiliate audit | Free (two-week test) | Traffic analysis through an impact.com integration; sales entry point to paid protection |
| Lunio | Affiliate audit | Free | Vendor-run traffic audit as a lead generator |
The free audits are the part many guides skip. TrafficGuard publishes an example from one mobile app: of roughly 2.06 million clicks, about 412,000 were flagged invalid, and of 21,529 conversions, 2,113 were invalid, which it puts at about 22% of clicks and 10% of conversions and $45,000 a month in waste. That is a single vendor-reported example, not a benchmark. But it tells you how you will be compared: against someone who looks at the same data for nothing, because they hope to sell software afterward.
Your edge is independence. A vendor profits when it finds fraud. An independent analyst can say “this looks fine,” or “this is unusual but probably explainable,” and be believed.
Illustrative service packaging, not a market-standard price list. Basic audit (one data window, limited scope): $1,000 to $1,500. Full audit (several partners, deeper investigation): $2,500 to $4,000. Advanced investigation (high volume, complex attribution): $5,000 and up. Monthly monitoring: $750 to $2,000 a month. These are planning numbers anchored loosely to the $1,000 flat fee and the agency ranges above.
How to Perform an Affiliate Fraud Audit
Step 1: Get the Data
Ask for raw exports, not dashboard screenshots. At minimum: click ID, timestamp, affiliate ID, subID, campaign or source, IP, device and user agent, GEO, conversion event and timestamp, payout, conversion status, and the postback log if one exists. You also want reversal history and any notes on partners already under review.
Check three things before you accept the project. Can the client give you click-level data, not only totals? Does the tracker data reconcile with the network or payment records? If not, start there, because mismatched numbers will look like fraud when they are only plumbing. AffStudio’s piece on why tracker and CPA network numbers don’t match covers the usual causes.
Step 2: Establish the Baseline
A number means nothing alone. Conversion rate, EPC, time to convert and device mix all need a reference: the same partner last month, the same offer across other partners, the same GEO and device class. Without it you are guessing. Build the baseline first, and keep seasonality and campaign changes in view. A new creative or a promo can double CR with no fraud involved.
Step 3: Find Anomalies
Look for deviations from the baseline: a burst of conversions in a narrow window, a timing distribution that collapses to a few seconds, a partner whose profile changes overnight. impact.com notes that a click-to-conversion time under five seconds is not always fraudulent on its own, but it warrants a direct conversation. That is the right framing for everything in this step. An anomaly opens an investigation. It does not close one.
Step 4: Investigate the Source
Drill down from affiliate to campaign to subID to placement, then compare device, timing and GEO inside each slice. Often the finding sits one level lower than the headline: the partner is fine, but one subID is not. Check the traffic yourself if you can. Click the links, look at the landing flow, see what the placement actually is. Edelman’s analysis of the Honey dispute pointed to code that behaved differently when it detected a tester, which is a reminder that some problems hide from the people looking for them.
Step 5: Classify the Finding
Use three buckets. Normal: explained by the baseline. Unusual, needs review: deviates, plausible innocent cause not yet ruled out. High risk, requires investigation: multiple independent signals point the same way. Do not call a partner fraudulent unless the evidence establishes it. Your report can say “consistent with incentivized traffic” and still be honest.
The reason is practical. Tricia Meyer, a long-time affiliate manager, wrote about a program whose reversal rates looked alarming, in the high 70s percent for two months. The reversals were cancellations, not fraud, so the pattern had an ordinary explanation. Another practitioner source, Indoleads, makes the same broader point: there is no universal acceptable reversal rate, and 5% might be a problem for a digital product and normal for retail.
Step 6: Produce the Report
For each finding: what you saw, the evidence, which traffic is affected, estimated financial impact (with the assumptions spelled out), a confidence level, and the next action. Keep the executive summary to one page. The client should be able to forward it to their finance team without translating it.
9 Affiliate Fraud Signals Worth Investigating
This is the working list, a level below the audit steps above. None of these is proof by itself.
1. Abnormal conversion spikes
What it looks like: Conversions jump by a multiple in a day or two with no matching traffic or campaign change.
Why it matters: Fake leads and scripted conversions often arrive in bursts.
What to check next: Campaign changes, promos, tracking updates, and whether the spike sits in one subID or across the partner.
Can it prove fraud? No. Launches and promos produce spikes too.
2. Unusual click-to-conversion timing
What it looks like: Many conversions within seconds of the click, or a suspiciously uniform gap.
Why it matters: Real users take time to read, register and pay.
What to check next: Offer type (some convert fast), device, and whether timing clusters on specific subIDs.
Can it prove fraud? No. impact.com says a very short gap warrants a conversation, not a conclusion.
3. Repeated IP and device patterns
What it looks like: Many conversions from the same fingerprint, subnet or hosting provider.
Why it matters: Fraud farms reuse infrastructure. Cheap proxies and emulators leave traces.
What to check next: Carrier NAT, offices, shared Wi-Fi, VPN use that is normal for the vertical.
Can it prove fraud? No. Shared IPs are common, so IP alone is weak evidence.
4. Impossible or suspicious GEO combinations
What it looks like: Click from one country, conversion or payment from another the offer does not serve.
Why it matters: Geo mismatch can signal masking or spoofing.
What to check next: Travel, VPN adoption, mobile roaming, and how the GEO is detected at each step.
Can it prove fraud? Rarely on its own. Several mismatches in one slice is stronger.
5. Extreme conversion rates
What it looks like: CR far above the same offer across other partners or the partner’s own history.
Why it matters: Inflated CR can mean misattributed or manufactured conversions.
What to check next: Traffic type, audience warmth, tracking implementation. A brand-term or coupon source converts high legitimately.
Can it prove fraud? No. High CR can mean excellent traffic.
6. SubID-level anomalies
What it looks like: One subID with a pattern the others lack: odd timing, one device cluster, abnormal CR.
Why it matters: Bad traffic is often concentrated. A clean partner can have one dirty placement.
What to check next: What the subID maps to, when it started, and whether the partner knows.
Can it prove fraud? No, but it often narrows the search fast.
7. Postback inconsistencies
What it looks like: Conversion events before the click, missing or duplicated click IDs, repeated transaction IDs.
Why it matters: These point at tracking manipulation or at broken setup. Both cost money.
What to check next: Postback logs, click-ID passing, server-to-server configuration. AffStudio has a separate piece on postback problems.
Can it prove fraud? Sometimes. Impossible event order is strong, but misconfiguration is the first suspect.
8. Sudden change in partner quality
What it looks like: A stable partner shifts: approvals drop, reversals climb, or performance improves sharply.
Why it matters: Sudden improvement can be as suspicious as decline.
What to check next: Traffic source changes, new sub-affiliates, offer updates, partner staff changes.
Can it prove fraud? No. It is a reason to ask questions.
9. Strong front-end metrics, weak downstream quality
What it looks like: Healthy clicks and signups, but deposits, retention or lead validity are near zero.
Why it matters: Incentivized or fake-account traffic looks good at the top of the funnel.
What to check next: Match leads against later events; check duplicates and disposable emails.
Can it prove fraud? Not alone. It is among the stronger signals when combined with timing or device patterns.
How AI Can Make Affiliate Fraud Audits Faster
Two different things get called “AI” here. One is machine learning that models normal behavior and flags outliers. The other is a language model that helps an analyst read, summarize and question the flagged data. Mixing them up is how people end up claiming that a chatbot “detected fraud” from a CSV. I found no evidence supporting that as a reliable workflow.
What the evidence does show: vendors such as impact.com describe machine learning plus dedicated analysts, and 24metrics describes ML models on Google Cloud, with a vendor-reported accuracy improvement over traditional methods. Academic work on LLMs for anomaly detection, including a 2025 survey, highlights hallucination and computational cost as open problems, while noting the potential for explanations. So: detection from statistics and ML, explanation and drafting from LLMs, verdicts from people.
AI for Anomaly Detection
Input: cleaned click and conversion data. Processing: statistical or ML models compare each partner and subID against its baseline. Output: a ranked list of outliers. Human role: decide which outliers deserve time.
AI for Pattern Discovery
Input: the flagged slices. Processing: clustering across IP, device, timing and subID. Output: candidate groups, such as “forty conversions sharing a fingerprint.” Human role: judge whether the group means anything.
AI-Assisted Investigation
Input: one flagged partner with its metrics and history. Processing: an LLM drafts hypotheses and the checks that would confirm or reject each. Output: an investigation checklist. Human role: run the checks, because the model cannot see the live traffic. AffStudio’s walkthrough of an AI assistant for campaign analysis shows the general pattern.
AI for Report Generation
Input: verified findings and numbers. Processing: an LLM turns them into client-ready prose. Output: a draft report. Human role: check every figure against source data and remove anything the evidence does not support.
AI for Continuous Monitoring
Input: daily or weekly data pulls. Processing: scheduled checks against thresholds. Output: alerts and a short digest. Human role: triage. A noisy alert system gets ignored, so tuning thresholds is real work.
What AI Should NOT Decide
A model should not accuse a partner, terminate an affiliate, withhold commissions or label an anomaly as fraud. Each of those has consequences for someone’s income and for your client’s reputation, and models are wrong in ways that sound confident.
False positives are the core risk. Dr. Augustine Fou, a long-time ad fraud researcher, has argued that detection tools can miss traffic from real devices, which means automated tools fail in both directions. Meanwhile an honest publisher caught in a blanket rule loses revenue and trust. If your report triggers an unjustified ban, the client may face a dispute and you may face the blame.
Models also work with incomplete data. A missing postback, a partial export, a changed tracker setup: any of these shifts the picture. The honest sentence is “based on the data provided, this pattern is unusual and worth reviewing.”
The AI-Powered Affiliate Fraud Audit Workflow
The practical chain runs like this: affiliate and tracker data, cleaning, baseline calculation, anomaly detection, AI-assisted investigation, human review, fraud report, client action. Tools matter less than the order. Trackers such as Voluum, RedTrack and Keitaro export the click-level data. Spreadsheets handle small audits; SQL or Python handle larger ones. n8n can schedule pulls and move data around, and an LLM sits near the end for explanation and drafting. If you are choosing a tracker, AffStudio’s tracker comparison is a decent starting point.
How to Turn a One-Off Audit Into Recurring Revenue
The audit is the entry point. Once you have shown a client what is wrong, the natural next question is “how do we keep watching for it?” That leads to monitoring, then to a managed arrangement where you own ongoing reviews, then to consulting on rules and processes, and eventually to software if you have repeated the same work enough times to productize it.
Illustrative monthly package: a weekly anomaly digest, a partner watchlist, monthly review call, two hours of investigation time included, and a quarterly summary report. Price it from the hours you expect to spend plus a margin, not from a competitor’s SaaS tier.
Where to Find Your First Clients
Affiliate networks. They carry fraud risk across many advertisers and often hire specialists directly. A job posting for a fraud handling specialist at Awin, for instance, describes investigating publishers across its networks. That is evidence of demand, and also of in-house competition. Consulting or overflow work is a more realistic fit than a full engagement.
Affiliate programs. Mid-size advertisers with a few hundred active partners and no dedicated analyst. They have the data, the budget and the pain. Check where they already see partner quality problems; impact.com reports that about a quarter of brands cite partner quality as a major concern and 21% describe being in a constant battle with fraud.
Affiliate managers. Often the buyer and the user. They want evidence to bring to a partner or to leadership. Pitch them the report as something that makes their conversations easier.
Agencies. Agencies running programs for several clients can resell audits or use you as a white-label analyst. One agency relationship can bring repeat work.
High-volume advertisers. Finance, gambling, e-commerce, anywhere CPA payouts are large. The audit pays back fastest here, but they also have the most sophisticated in-house teams and vendor relationships.
How to Sell Your First Affiliate Fraud Audit
Do not lead with “I use AI to detect fraud.” Every vendor says that, and it tells the client nothing. Lead with the outcome: “I’ll analyze your affiliate traffic and identify anomalies that may be costing you money.”
A concrete first offer: Affiliate Traffic Audit. A defined date range, a defined set of affiliates or campaigns, traffic-quality and conversion anomaly analysis, a written report, and a call to walk through it. Set the scope in writing.
Before accepting, confirm you will get click-level data, conversion and status data, and ideally payout and reversal history. Without those you can deliver only a surface review, and you should say so. Agree what happens with the findings: you report, the client decides.
Because vendors give free audits away, a good approach is to offer a small paid pilot with a narrow scope, then expand. A fixed price on a small scope is easier to approve than an open-ended engagement.
How Much Work Does an Audit Actually Take?
Everything in this section is an illustrative calculation. I could not find real benchmarks for audit effort, so treat the hours as assumptions to replace with your own.
| Task | Manual workflow | Reusable workflow |
|---|---|---|
| Data preparation | 4 h | 1.5 h |
| Initial analysis | 6 h | 2 h |
| Investigation | 6 h | 5 h |
| Report | 4 h | 1.5 h |
| Client call | 1.5 h | 1.5 h |
| Total | 21.5 h | 11.5 h |
At an illustrative $2,000 price, the manual workflow yields about $93 of gross revenue per working hour; the reusable one about $174. Investigation barely shrinks, because that is the part that needs judgment.
Gross revenue is not profit. Add unpaid time: sales calls, scoping, revisions, back-and-forth on data access. If that is six hours, the figures drop to roughly $73 and $114 an hour. Then subtract software, taxes and the cost of support after delivery. A reusable workflow raises the ceiling, but you have to build and maintain it first.
How to Automate the Business
A practical pipeline: tracker or network API, then n8n, then a database or spreadsheet, then anomaly detection, then LLM analysis, then report generation, then email or Telegram delivery. AffStudio has guides on automated affiliate reporting with n8n and on AI agents for affiliate marketing that cover the mechanics.
What can be automated: data pulls, cleaning, baseline calculation, threshold checks, alerts, first-draft summaries. What cannot: deciding which flags matter, checking traffic by hand, classifying findings, talking to the client, and signing off on anything that names a partner. If you are thinking about selling this as an automation service, AffStudio also covers how to start an n8n agency and how to price one.
Audit Service vs. Monitoring vs. SaaS
| Model | Startup Cost | Technical Complexity | Revenue Model | Scalability | Time to Launch |
|---|---|---|---|---|---|
| Audit service | Low | Low to moderate | Project fees | Limited by your hours | Weeks |
| Monitoring | Low to moderate | Moderate | Monthly retainer | Moderate, needs process | 1 to 3 months |
| SaaS | High | High | Subscriptions | High, if it works | Many months |
There is no winner. Audits get you cash and client knowledge quickly but cap out at your capacity. Monitoring smooths revenue and adds obligations. SaaS scales best and demands the most: integrations, support, security, and a sales fight with vendors who already have the customers.
7 Mistakes That Can Kill an Affiliate Fraud Audit Business
- Treating every anomaly as fraud. The reversal case above is the pattern: alarming numbers, ordinary cause.
- Relying only on IP addresses. Shared networks and VPNs make IP weak evidence. Combine signals.
- Ignoring historical baselines. Without them you are comparing nothing to nothing.
- Not understanding attribution. The Honey dispute turned on how last-click credit and stand-down rules are applied. If you cannot read attribution logic, you will misread the data.
- Producing generic reports. A template full of boilerplate tells the client nothing. Name the subIDs, the dates, the numbers.
- Making accusations without evidence. Honest publishers do get caught by blanket rules, and a wrong accusation can cost a client a good partner.
- Building SaaS before validating demand. Sell the audit first. If clients keep asking for the same thing monthly, you have a signal.
Is Affiliate Fraud Auditing Worth Starting in 2026?
It can make sense if you can get access to affiliate or marketing data, understand tracking well enough to separate fraud from broken setup, investigate patiently, explain findings to non-analysts, and run the work as a repeatable process with a path to recurring revenue. If one of those is missing, the business gets hard quickly.
The risks are real. Clients are hard to win when vendors offer free audits. Data access can stall a project for weeks. False positives damage trust. A wrong accusation brings legal and reputational risk. Specialized SaaS and bundled platform tools keep getting better. And at scale, the technical load grows fast. I could not verify any reliable market size for affiliate fraud services, so I would not build a plan on one.
A reasonable test: sell two or three small paid audits to people you can reach, deliver them well, and see whether any client asks for more. That answers the question better than a forecast.
FAQ
Can you make money doing affiliate fraud audits?
Possibly, if you have access to buyers and real analytical skill. Public evidence on earnings is thin, so treat any income figure as a plan, not a promise.
How much does an affiliate fraud audit cost?
There is no standard price. The verified examples include a $1,000 flat tracking review from AffiliateManager.Expert, agency price guides from roughly $2,500 to $20,000 for broader audits, and free vendor audits from TrafficGuard and Lunio.
What data do you need for an affiliate fraud audit?
Click-level and conversion-level exports with timestamps, affiliate ID, subID, IP, device, GEO, payout and status, plus postback logs and reversal history where available.
What tools are used for affiliate fraud detection?
Trackers like Voluum, RedTrack and Keitaro for data, spreadsheets, SQL or Python for analysis, and vendors such as 24metrics, TrafficGuard or Lunio for automated detection.
Can AI detect affiliate fraud?
ML models can flag outliers and LLMs can help explain them, but suspicious behavior still needs human verification before anyone is accused or penalized.
How long does an affiliate fraud audit take?
The listed tracking review promises findings within 14 business days of data access. Effort depends on volume and data quality; my hour estimates above are illustrative.
Can affiliate fraud monitoring be sold as a monthly service?
Yes, and it is the natural next step after an audit. I found no reliable public price list, so set prices from your own hours.
Do you need to build software to start?
No. A spreadsheet, SQL or Python, and a clear report template are enough for your first audits.
Sources
- AffiliateManager.Expert, tracking review and fraud audit pages
- 24metrics pricing page (updated August 2026)
- TrafficGuard, affiliate audit overview
- impact.com, affiliate marketing scams guide (PUMA case, fraud concern statistics, click-to-conversion guidance)
- CNBC reporting on Uber v. Fetch Media and Kevin Frisch
- Hello Partner / PPC Land coverage of the Honey dispute and network responses, 2026 (alleged; PayPal disputes the claims)
- Harvard Business School, Working Knowledge: Edelman and Brandi on affiliate fraud
- Tricia Meyer, reversal-rate case; Indoleads on reversal rates
- Jolly Consulting and Hamster Garage agency pricing pages (agency marketing, not market surveys)
- Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey, NAACL Findings 2025
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