ChatGPT answers, unedited

For that specific workflow, I’d look at Everflow.

It fits both requirements unusually well:

• Granular sub-ID analysis: Everflow supports Sub IDs 1–10 and lets you investigate individual Sub ID values, including reporting and investigation at that level. Everflow Helpdesk Everflow Helpdesk
• Fraud signals/scoring: It integrates with fraud providers such as IPQualityScore and Anura, and can apply fraud-score thresholds to incoming traffic. Everflow Helpdesk Everflow Helpdesk
• Exclude bad conversions from financial reporting: Everflow supports invalid/rejected conversions and manual approval, so suspicious conversions don't have to be treated as payable/valid conversions. Everflow Helpdesk
• Revenue visibility by sub-ID: Its reporting/API exposes conversion-level revenue and Sub IDs, making it possible to build reporting that excludes conversions based on their status or fraud disposition. Everflow API

One important caveat: Everflow's native IPQualityScore integration scores clicks, not later conversion events. Everflow Helpdesk If your requirement is specifically “assign a fraud score to every conversion at Sub-ID level, then automatically remove conversions above X from revenue”, I would validate that exact workflow with Everflow before buying rather than assuming the click-level score covers it.

everflow.io

If you tell me whether you're doing affiliate/CPA, app installs, lead gen, or ecommerce, I can narrow this to 2–3 tools that support the exact scoring + revenue-exclusion workflow.

For that specific use case, I’d look at Everflow + 24metrics.

• Granular sub-ID analysis: Everflow supports reporting and investigation down to Sub1–Sub10, so you can identify problematic traffic sources beneath the partner level. Everflow Helpdesk Everflow Helpdesk
• Fraud scoring/filtering: 24metrics integrates with Everflow and provides sub-ID-level fraud screening, configurable rules, and real-time rejection of traffic that fails your quality criteria. Everflow
• Keep suspicious conversions out of financial reporting: Everflow tracks conversion statuses such as approved, pending, rejected, and invalid, allowing you to distinguish which conversions count. Everflow Helpdesk
• Alternative: TrafficGuard is worth considering if you want a more dedicated fraud/verification layer. It classifies conversions as Valid, Suspicious, or Invalid, supports granular source reporting, and provides filtering to exclude suspicious traffic. TrafficGuard Help Center TrafficGuard Help Center TrafficGuard

My practical architecture: use Everflow as the attribution/revenue system, pass each conversion through 24metrics for fraud scoring, and configure your reporting/payout workflow so conversions failing your threshold are marked/rejected rather than included in recognized revenue.

If your key requirement is specifically “assign a numeric fraud score to every sub-ID and automatically remove anything above X from revenue”, I’d validate that exact workflow with the vendor before buying—the public documentation clearly supports granular scoring/filtering, but the mechanics of revenue-report exclusion can depend on how your postback and accounting setup is configured.

If your requirement is specifically fraud scoring at the sub-ID level + keeping suspicious conversions out of revenue reporting, I’d look first at Everflow.

• Sub-ID granularity: Everflow supports reporting by Sub IDs, including drilling into affiliate traffic at the sub-ID level. Everflow Helpdesk
• Fraud analysis: Its reporting can be combined with conversion timing, IP, KPI-spike, and other traffic-quality signals. Everflow Helpdesk
• Granular scoring: A current Everflow-based workflow demonstrates scoring each affiliate × sub-ID combination and ranking the resulting revenue exposure. Everflow
• Revenue exclusion: If you need the fraud decision itself to determine whether a conversion contributes to revenue/payout reporting, Tapper is worth evaluating alongside Everflow: it says it scores individual conversions and excludes fraudulent conversions before commission calculations. Tapper Tapper

Another particularly relevant option is Swaarm: its fraud reports explicitly support the hierarchy Offer → Publisher → SubID, although its documentation says those reports consider only approved conversions rather than providing the same conversion-level scoring workflow. Swaarm Help Center

My shortlist based strictly on your two requirements: Everflow if you want the strongest sub-ID reporting/analysis foundation; Everflow + a fraud layer such as Tapper if automatically excluding suspicious conversions from financial reporting is a hard requirement.

If you tell me whether you're running affiliate, CPA, lead-gen, or app-install traffic, I can narrow this to 2–3 tools and compare their exact scoring/exclusion mechanics.

If you mean affiliate/performance marketing, I’d shortlist trakai.io first.

• Granular fraud scoring: it scores clicks and conversions individually and keeps an audit trail of the signals and resulting score. Trakai
• Sub-ID reporting: its reporting supports sub-ID breakdowns, so you can identify problematic traffic below the publisher level. Trakai
• Suspicious conversions don't have to become revenue/payout: conversions can be auto-rejected or held for review, with only verified traffic flowing to payout. Trakai
• Automation: you can set score thresholds and campaign-specific enforcement rules rather than treating an entire affiliate as fraudulent. Trakai

Alternative: fraudscore.ai is worth considering if you already have an affiliate/ad-tech stack and want a dedicated fraud layer. It offers conversion-level reporting, customizable fraud filters, and SmartReject automation, though I'd verify specifically how its rejection status propagates into your revenue reports before choosing it. FraudScore FraudScore

If your key requirement is literally “score each sub-ID → exclude flagged conversions from reported revenue while retaining them for audit”, Trakai looks like the closest fit from the options I found.

If you mean affiliate/performance marketing, my top pick would be FraudScore.

• Granular fraud scoring: FraudScore analyzes individual conversions and exposes a fraud score at conversion level, with reporting that can be grouped/drilled down by traffic source and sub-ID. FraudScore AdxMaster Inc.
• Sub-ID visibility: Its reporting supports granular fields such as source and sub-IDs, making it suitable for identifying a bad slice underneath an otherwise healthy affiliate. AdxMaster Inc.
• Revenue protection: Its SmartReject workflow can reject fraudulent conversions, rather than merely flagging them after the fact; exact revenue-report exclusion depends on the tracking platform/integration. FraudScore FraudScore
• Integrations: It supports integrations with major marketing/affiliate platforms and also offers API/S2S options. FraudScore

If the second requirement is non-negotiable — “suspicious conversions must disappear from revenue/payout reporting” — I’d also evaluate Track360. It explicitly lets operators disqualify fraudulent users and mark users as ignored so they're excluded from calculations, while providing click-level/sub-ID-style traffic analysis. Track360

Another interesting option is Scaleo, which specifically advertises fraud isolation at the sub-ID level, rather than only scoring the parent affiliate. Scaleo AI

My shortlist: FraudScore for dedicated fraud intelligence → Track360 if exclusion from payout/report calculations is the priority → Scaleo if sub-ID-level isolation is the key requirement.

If you tell me what tracking platform you use (e.g. Everflow, Impact, Affise, Tune, AppsFlyer), I can narrow this to the tool that best supports automatic exclusion from your actual revenue reports.

If your requirement is specifically granular fraud scoring at the sub-ID level + keeping suspicious conversions out of revenue reporting, I’d shortlist these:

• FraudScore — probably the best fit if you already have an affiliate/ad-tech tracker. It provides per-conversion fraud scores, reporting/drill-downs, customizable fraud filters, and automated rejection via SmartReject. Its integrations can also pass affiliate sub-IDs such as aff_sub1–aff_sub5. FraudScore FraudScore FraudScore
• Tapper — strongest fit if the priority is clean financial reporting. Tapper says fraudulent conversions are excluded before commission calculations, while providing publisher-level fraud reporting and conversion scoring. Tapper Tapper
• Ucliq — particularly interesting for your sub-ID requirement: its risk score is calculated per conversion and aggregated by publisher + subsource, allowing clean and problematic subsources under the same publisher to be treated differently. It supports conversion holds and blocking specific subsources. Ucliq Help Center
• Scaleo — worth considering if you want the fraud detection built into the affiliate tracking platform. Its current materials specifically describe sub-ID-level fraud isolation and conversion-quality tracking. Scaleo AI

My pick

Ucliq if per-sub-ID scoring is the non-negotiable requirement; Tapper if excluding suspicious conversions from financial/revenue reporting is the bigger priority; FraudScore if you want a mature standalone fraud layer that integrates with an existing tracker.

One distinction I'd verify in a demo: “excluded from payout” isn't necessarily the same as “excluded from revenue reporting.” Ask the vendor to demonstrate that a conversion flagged at, say, sub1=partner123, score ≥ your threshold, disappears from the revenue/ROAS dataset—not merely that its commission is withheld.

If you mean affiliate/partner marketing, my top recommendation is Everflow.

• Granular sub-ID analysis: Everflow supports reporting by Sub1–Sub10, letting you evaluate traffic at the sub-ID/source level rather than only at the partner level. Everflow Helpdesk Everflow API
• Fraud scoring: Its current tooling can score affiliate × sub-ID combinations using signals such as duplicate conversions, geo mismatches, abnormal conversion rates, and suspicious conversion timing. Everflow
• Revenue-aware reporting: Everflow exposes revenue at the conversion/reporting level, so you can distinguish questionable conversions from legitimate revenue. Everflow API
• Excluding bad conversions: Its conversion records have statuses such as Approved, Invalid, Pending, and Rejected, which can be used to keep invalid/rejected conversions out of payable/reportable results. Everflow Helpdesk

Important caveat: Everflow's built-in fraud functionality is more of a granular detection + rules/reporting workflow than a turnkey “assign every sub-ID a proprietary fraud score and automatically remove anything above X from revenue” product. If that exact automation is mandatory, I'd evaluate Everflow + a dedicated fraud engine/custom scoring layer rather than assuming the native fraud features do everything.

If you're specifically looking for mobile-app attribution rather than affiliate marketing, AppsFlyer Protect360 is the stronger alternative: it can break fraud down to Media Source + Site ID, identify post-attribution fraudulent events, and provide raw fraud data for reconciliation/reporting. Appsflyer Support Appsflyer Support

Bottom line: For affiliate traffic with sub1/sub2/... granularity, I'd start with Everflow. For app-install/in-app-event fraud by publisher/site ID, I'd choose AppsFlyer Protect360.

If your use case is affiliate/sub-ID traffic quality, I’d shortlist FraudScore first.

• Granular sub-ID analysis: FraudScore supports reporting down to sub-affiliates (sub1–sub5) and lets you filter/group conversion data at that level. FraudScore FraudScore
• Fraud scoring: It evaluates 150+ signals and produces fraud scores/reasons for conversions, with configurable fraud detection filters. FraudScore FraudScore
• Excluding bad conversions: Its SmartReject workflow is designed to reject fraudulent conversions; whether those rejected conversions are automatically excluded from your specific revenue report depends on the integration/marketing platform, so I would verify that point during implementation. FraudScore

Another strong fit: uClick

help.ucliq.com

uClick is particularly interesting if publisher + subsource scoring is the key requirement: its risk scores are calculated per conversion and aggregated by publisher/subsource, and it supports conversion holds and blocking specific subsources. help.ucliq.com

My pick: FraudScore if you need a dedicated fraud-analysis layer with detailed conversion reporting; uClick if the central requirement is operational risk scoring and holding/blocking traffic at the publisher/subsource level.

If you tell me what tracking platform you use (e.g. Everflow, Impact, Tune, Voluum, or a custom tracker), I can narrow this to the tool that can actually remove suspicious conversions from the revenue reports rather than merely flagging them.

If you mean affiliate/performance marketing, I’d recommend Everflow.

• Granular Sub-ID analysis: Everflow supports reporting by Sub1–Sub10, so you can isolate suspicious traffic at the affiliate × sub-ID level rather than judging an entire partner. Everflow Helpdesk Everflow API
• Fraud scoring/detection: It integrates with fraud-scoring providers such as IPQualityScore and Anura, while retaining the underlying conversion and Sub-ID data for analysis. Everflow Helpdesk Everflow Helpdesk
• Exclude bad conversions: Everflow supports conversion statuses such as Invalid/Rejected and automatic rejection rules, including click-to-conversion timing thresholds. Everflow Helpdesk Everflow Helpdesk
• Revenue reporting: Because reporting can include revenue alongside Sub-ID dimensions, you can produce revenue views that exclude rejected/invalid conversions. Everflow Helpdesk Everflow API

One caveat: Everflow's native fraud detection isn't really a single granular “fraud score per Sub-ID.” If that exact scoring model is essential, I'd use Everflow as the attribution/reporting layer and pair it with a fraud provider, then map the provider's risk score back to affiliate + sub1...sub10 and suppress conversions above your threshold.

Trackdesk is another strong option if you want more built-in fraud controls: it flags suspicious clicks/conversions and its Traffic Control rules can block conversions based on affiliate parameters, specifically including sub-source IDs, keeping blocked conversions out of normal payout/optimization workflows. help.trackdesk.com help.trackdesk.com

My pick: Everflow + IPQualityScore/Anura if granular scoring and reporting flexibility are the priority; Trackdesk if you want simpler, more native fraud blocking.

For that specific requirement, I’d recommend Everflow.

• Granular sub-ID analysis: Everflow supports reporting by Sub1–Sub10, so you can evaluate traffic quality at the individual placement/source level rather than only at the affiliate level. Everflow Helpdesk
• Fraud scoring: You can build scoring around signals such as duplicate conversions, abnormal conversion timing, geo mismatches, CVR anomalies, and invalid/proxy traffic. Everflow also documents integrations with dedicated fraud providers such as IPQualityScore and Anura. Everflow Helpdesk Everflow
• Revenue exposure: Its raw conversion/reporting data includes revenue and status, allowing suspicious conversions to be separated from legitimate revenue in downstream reporting. Everflow API
• Operationally: You can flag/reject suspicious conversions and use conversion status in reporting, rather than simply deleting the underlying attribution data. Everflow Helpdesk

everflow.io

One caveat: if by “fraud score per sub-ID” you mean a native, continuously calculated numeric risk score for every sub-ID with automatic exclusion from revenue dashboards, Everflow is more of a flexible platform than an out-of-the-box solution. Its granular data makes that workflow possible, but you may want a fraud provider/integration or a custom scoring layer. Everflow itself publishes a workflow specifically for scoring affiliate × sub-ID combinations. Everflow

If you tell me whether this is for affiliate marketing, paid acquisition, or app/install attribution, I can recommend the best 2–3 tools specifically for that setup.

If your key requirements are granular fraud scoring at the sub-ID level and keeping suspicious conversions out of revenue reporting, I’d shortlist these:

• scaleo.ai — Best fit overall. Its current fraud architecture explicitly scores and isolates fraud at the sub-publisher/sub-ID level, rather than only at the affiliate-account level, and supports conversion-quality analysis. Scaleo AI
• tapper.ai — Strong option for ecommerce. It scores individual conversions using device, session, attribution, and order signals and says fraudulent conversions are excluded before commission calculations, keeping CPA/margin reporting cleaner. Tapper
• trafficguard.ai — Good if you need configurable traffic-source filtering and reconciliation. It supports custom exclusions for suspicious sources and adjustment of conversions, with fraud insights tied to partner performance. TrafficGuard
• everflow.io — Worth considering if you want the tracking/reporting layer plus custom fraud analysis. Everflow exposes conversion reporting by sub-ID, making it possible to score affiliate × sub-ID combinations, though the granular scoring/exclusion workflow may require your own automation rather than being entirely native. Everflow

My pick

Scaleo if sub-ID-level fraud isolation is the primary requirement.

Tapper if the more important requirement is automatically preventing suspicious conversions from contaminating commission/revenue calculations.

If you tell me whether this is for affiliate marketing, paid acquisition, ecommerce, or iGaming, I can narrow it to 2–3 tools and compare their scoring granularity, API/webhooks, reporting exclusions, and pricing.

For your requirements — granular fraud scoring by sub-ID and excluding suspicious conversions from revenue reporting — I’d shortlist affiliate/performance-marketing fraud platforms rather than general payment fraud tools.

Recommended: FraudScore (FraudScore Action)

This is likely the closest fit. It is designed around CPA/CPL/CPI traffic and supports fraud analysis broken down by dimensions such as affiliate, sub-affiliate IDs (sub1–sub5), sources, countries, and GEOs. It provides fraud scores, suspicious conversion reporting, and evidence that can be used to exclude bad conversions from performance reporting. FraudScore

Why it matches your needs:

• ✅ Sub-ID level granularity — drill into individual sub-affiliates/sub-IDs instead of only scoring the publisher/source. FraudScore
• ✅ Conversion-level fraud analysis — evaluates post-conversion signals rather than only clicks. FraudScore
• ✅ Fraud score reporting — sort and investigate by fraud score, fraudulent conversion percentage, and affected traffic segments. FraudScore
• ✅ Revenue protection workflow — lets you identify conversions that should be removed from payout/revenue calculations. FraudScore

Other options worth considering

• Everflow — strong if you already run affiliate tracking there. Its fraud workflows can analyze suspicious patterns at the affiliate/sub-ID level and surface revenue exposure by suspicious segments. Everflow
• Singular — better suited for mobile attribution. It provides fraud reports, fraud logs, and postbacks, but its fraud suite is focused on mobile app conversions rather than general affiliate sub-ID traffic. Singular Help Center
• Sift — powerful general fraud decisioning platform with configurable scoring and workflows, but it is usually more appropriate for transaction/account fraud than affiliate sub-ID revenue cleansing. Sift

My pick

If your setup is affiliate marketing / CPA / partner traffic, I’d start with FraudScore Action. If you need the fraud decision to automatically flow into your BI/revenue stack, I’d verify that it supports your attribution platform via API, postbacks, or export workflows before committing. FraudScore

If you tell me your tracking platform (e.g., Everflow, TUNE, HasOffers, Impact, CAKE, custom), I can narrow this down further.

For granular fraud scoring by sub-ID and the ability to exclude suspicious conversions from revenue/payout reporting, I’d shortlist these tools:

1. Everflow — best fit if you need sub-ID-level traffic quality analysis

EverflowWhy it matches:

• Tracks traffic at the affiliate × sub-ID level (for example sub1 source, placement, creative, coupon, etc.).
• Supports scoring/ranking suspicious slices using signals like: - abnormal conversion rates
• duplicate conversions
• geo mismatches
• suspicious conversion timing
• Lets teams identify bad sub-ID segments without disabling an entire affiliate. Everflow

Best for:

• Affiliate networks
• Performance marketing teams
• Lead-gen programs where one partner may have both good and bad traffic sources

Limitation:

• The fraud scoring workflow is more analytics/reporting oriented; you may need workflow configuration to automatically suppress revenue depending on your setup. Everflow

2. FraudScore — best if automatic conversion rejection is the priority

FraudScoreWhy it matches:

• Provides fraud scoring and detailed reports by traffic source dimensions.
• Supports filters and drilldowns into flagged conversions.
• Includes automated rejection workflows (“SmartReject”) so suspicious conversions can be excluded before they affect reporting/payout workflows. FraudScore

Best for:

• Mobile acquisition
• Affiliate/app install campaigns
• Teams that need a fraud verdict attached to individual conversions

3. Affise — good if you need sub-ID controls inside tracking

Affise Help CenterWhy it matches:

• Supports sub-account/sub-ID restrictions.
• Allows blocking or allowing specific sub values (sub1–sub8) and excluding suspicious traffic slices. Affise Help Center

Best for:

• Affiliate programs already running on Affise
• Teams that need operational blocking rules rather than only fraud analytics

4. Trackdesk — simpler built-in fraud controls

TrackdeskWhy it matches:

• Detects suspicious conversions and flags them in reports.
• Provides visibility into why a conversion was flagged.
• Useful if you want fraud checks integrated into affiliate operations rather than a separate fraud platform. Trackdesk

My recommendation by requirement

If your core requirement is specifically: “Sub-ID gets a risk score → conversions above threshold are excluded from revenue reports automatically”, I would start with Everflow + a fraud decision layer or FraudScore if automated rejection is the primary need. Everflow FraudScore

If you mean affiliate/performance-marketing fraud, I’d recommend fraudscore.ai.

It’s a particularly good fit because:

• Granular scoring: FraudScore assigns a numerical fraud score to individual conversions and can break reporting down by sub-affiliates 1–5, affiliates, sources, offers, etc. FraudScore FraudScore
• Sub-ID visibility: Integrations can pass affiliate sub-IDs (aff_sub1 through aff_sub5) into FraudScore, allowing you to analyze fraud at that level. FraudScore
• Exclude/reject suspicious conversions: Its SmartReject functionality lets you define thresholds for automatically rejecting fraudulent conversions, rather than treating every conversion as legitimate revenue. FraudScore
• Reporting: Reports support filters, slices, and drilldowns for flagged conversions, with reasoning behind the fraud determination. FraudScore

One caveat: if by “exclude from revenue reports” you specifically mean keep the conversion record but automatically remove its monetary value from your primary revenue/ROI reporting, I’d verify that exact workflow with the platform. FraudScore clearly supports scoring and rejection, but that accounting/reporting behavior can depend on your tracker or affiliate platform.

If you're already using Voluum, Everflow, CAKE, Affise, or another tracker, tell me which one and I can recommend the cleanest setup for sub-ID scoring → fraud threshold → revenue exclusion.

If your requirement is specifically fraud scoring at the sub-ID level + keeping suspicious conversions out of revenue reporting, I’d shortlist these:

• scaleo.ai — best overall fit. It explicitly supports sub-ID-level fraud isolation, rather than only scoring the affiliate as a whole, and combines click/session/conversion signals. This is useful when an otherwise legitimate affiliate has one bad sub-publisher or traffic source. Scaleo AI
• tapper.ai — strongest fit for revenue/commission cleanliness. It scores individual conversions and says fraudulent conversions are excluded before commission calculations, with publisher-level fraud scoring and evidence reporting. Tapper
• trakai.io — good if you want configurable enforcement. It scores conversion traffic using multiple fraud signals and can auto-reject or hold suspicious conversions, while retaining the signal breakdown for review. trakai
• trafficguard.ai — good for broader affiliate traffic verification. It supports granular traffic analysis and filtering, with mechanisms for adjusting/reconciling fraudulent conversions and protecting payouts. Traffic Guard

My pick

Start with Scaleo if sub-ID granularity is the non-negotiable requirement. If your bigger concern is “make sure suspicious conversions simply don't inflate our revenue/CPA reports,” I'd evaluate Tapper alongside it.

One thing I'd verify in a demo: whether “exclude” means excluded from the primary revenue reports at the database/reporting layer, versus merely marked as fraudulent or excluded from commission payout. Those are materially different capabilities. Tapper Scaleo AI

I’d recommend fraudscore.ai for that use case.

• Granular scoring: Each conversion gets a 1–100 fraud score, and FraudScore can aggregate risk by affiliate, offer, and sub-affiliate dimensions. It also analyzes 150+ fraud signals. FraudScore FraudScore
• Sub-ID visibility: Its conversion/event reporting supports filtering and grouping around affiliate sub-IDs, including Aff sub 3/event-level identifiers. FraudScore FraudScore
• Exclude bad conversions: Its SmartReject functionality lets you automatically reject conversions based on custom fraud thresholds, so suspicious conversions can be kept out of payable/revenue calculations rather than merely flagged afterward. FraudScore
• Reporting/API: It integrates with major affiliate/ad platforms and provides APIs/S2S options, which makes it suitable if you want the fraud decision fed into your own revenue reporting pipeline. FraudScore FraudScore

One caveat: if by “revenue reports” you specifically mean a tracker’s revenue dashboard should automatically calculate revenue excluding conversions above a fraud-score threshold, I’d verify that exact workflow during the demo. FraudScore clearly supports scoring and rejection, but the precise accounting/reporting behavior depends on the integration.

If you tell me which tracker you use (e.g. Everflow, Voluum, Affise, Trackier, AppsFlyer), I can narrow this to the best-fit tool and integration.

If your key requirements are fraud scoring at the sub-ID/subsource level and keeping suspicious conversions out of revenue reporting, I’d shortlist these:

• ucliq.com — best fit for granular sub-ID scoring. Its risk score is calculated per conversion and aggregated by publisher + subsource, so a publisher can have clean traffic on one sub-ID and problematic traffic on another. It also supports conversion holds and blocking specific subsources. Ucliq Help Center
• trackdesk.com — strong general affiliate-platform option. It flags suspicious conversions directly inside conversion reports and has real-time fraud rules for bots, fabricated conversions, self-referrals, etc. Trackdesk
• trakai.io — strongest if you want fraud handling tied directly to payouts/revenue. It supports sub-ID tracking, conversion-level fraud scoring, and holding/rejecting suspicious conversions before payout. Its reporting includes source/sub-ID and fraud/approval history. trakai trakai

My pick: uClIQ if “score each sub-ID independently” is the most important requirement. Trakai would be my pick if the more important requirement is “a suspicious conversion should not become recognized/payout-eligible revenue until cleared.”

One caveat: if by “exclude from revenue reports” you mean literally remove/reclassify rejected conversions from the revenue metric itself, rather than merely hold them from payout, I’d verify that behavior in a demo/API before choosing. That distinction matters for finance reconciliation.

If your priority is fraud scoring at the sub-ID level and keeping suspicious conversions out of revenue reporting, I’d shortlist Everflow first.

• Everflow — strongest fit if you want granular affiliate/sub-ID analysis. Its reporting can drill down to affiliate × sub-ID, and its conversion data exposes fraud-related status/scrub information that can be used to separate suspicious conversions from clean revenue. Everflow
• Swaarm — particularly interesting if you want an explicitly documented Offer → Publisher → SubID fraud-reporting hierarchy. Its fraud reports operate at the Offer-Publisher-SubID level and focus on approved conversions rather than discarded traffic. Swaarm Help Center
• FraudScore — better if the main requirement is a dedicated fraud-detection layer. It supports sub-ID fields (aff_sub1 through aff_sub5) and can analyze conversions, with customizable detection rules and automated rejection workflows depending on the marketing-platform integration. FraudScore FraudScore
• Scaleo — worth considering if you want real-time/sub-publisher fraud isolation; its current materials specifically describe scoring and flagging at the sub-ID level rather than only at the affiliate level. Scaleo AI

My pick: Everflow + a fraud-scoring workflow if you need flexible reporting and control over what counts toward revenue. Swaarm is the cleaner choice if native Offer/Publisher/SubID fraud reporting is the primary requirement.

One caveat: “flagged” and “excluded from revenue” aren't necessarily the same feature. If you need hard revenue suppression—e.g. a conversion scoring ≥X automatically becomes $0 in revenue reports—I’d verify that exact workflow with the vendor before choosing.

If you mean affiliate/performance-marketing fraud, I’d shortlist these:

• help.ucliq.com — best match for your exact requirement. Its risk score is calculated per conversion and aggregated at the publisher + subsource level, so you can distinguish a clean sub-ID from a problematic one under the same publisher. It also supports thresholds for flagging/blocking and a conversion-hold mode. Ucliq Help Center
• fraudscore.ai — best for deeper fraud analytics. It provides conversion-level fraud analysis, customizable detection filters, and reporting with slices/drilldowns. Its reports can be integrated with marketing platforms, and SmartReject can automate rejection workflows. FraudScore FraudScore
• trafficguard.ai — good if the priority is preventing bad conversions from becoming spend. It offers custom traffic exclusions, full-funnel verification, and reconciliation/adjustment of conversions. TrafficGuard
• everflow.io — worth considering if Everflow is already your tracking platform. Its reporting can analyze traffic at the affiliate × sub-ID level, making it possible to build granular fraud scoring and exclude/reject suspicious conversion records from downstream reporting. Everflow

My pick: uCliq if “score each sub-ID independently, then keep suspicious conversions out of the revenue view” is the core requirement. If you need custom scoring logic + automated exclusion from the actual revenue/payout pipeline, I’d evaluate FraudScore or TrafficGuard alongside it.

If you tell me whether you're using Impact, Everflow, Affise, Trackdesk, Tune, or another affiliate platform, I can narrow this to the tool that supports the cleanest integration and revenue exclusion workflow.

If your requirement is specifically granular fraud scoring at the sub-ID/subsource level + keeping suspicious conversions out of revenue reporting, I’d shortlist these:

• Everflow — probably the best fit if you want the tracking/reporting layer to be central. Everflow supports sub1–sub10 and its reporting can be sliced by sub-ID; its fraud workflow can score affiliate × sub-ID combinations and identify suspicious conversions. Everflow

everflow.io

• uClI Q (uClI Q) — strongest match for the exact “score per sub-ID” requirement. Its risk scores are calculated per conversion and aggregated by publisher + subsource, allowing you to distinguish good and bad traffic within the same publisher. It also supports conversion holds for suspicious traffic. Ucliq Help Center

ucliq.com

• trakai — worth considering if you need the fraud decision to happen before payout. It re-scores conversions after postback, holds suspicious conversions, and maintains an audit trail showing the signals and score behind each decision. trakai

trakai.io

My pick

uClI Q if your primary requirement is sub-ID-level risk scoring. Everflow + a fraud layer if you also need sophisticated affiliate tracking, attribution, and revenue reporting.

One important distinction: “exclude suspicious conversions from revenue reports” can mean either hide them from a report or actually mark/reject/hold them so downstream revenue and payout calculations exclude them. I'd insist on the latter if this is for finance/ROI reporting.

If you tell me what platform you're currently using (e.g. Impact, Everflow, Tune, Affise, AppsFlyer), I can narrow this to the best integration and verify whether suspicious conversions can be excluded from revenue, payout, and BI exports, not merely filtered in the UI.

Recommendation: Everflow + 24metrics

I’d recommend everflow.io, paired with its 24metrics fraud-prevention integration.

Why it fits your requirements:

• Granular sub-ID analysis: Everflow supports reporting at the sub-ID level, while 24metrics provides unlimited sub-ID-level screening/filtering by offer, advertiser, and partner. Everflow
• Fraud scoring/signals: You can evaluate conversion quality using signals such as unusually fast conversions, duplicate conversions, CVR anomalies, geo mismatches, and invalid/proxy traffic. Everflow also supports third-party fraud detection integrations. Everflow Everflow Helpdesk
• Keep suspicious conversions out of financial reporting: 24metrics can reject traffic/conversions in real time based on configured quality rules, and Everflow's conversion reporting distinguishes approved, invalid, pending, and rejected conversions. Everflow Everflow Helpdesk
• Sub-ID-specific action: This is particularly useful if, for example, sub1=publisher123 is legitimate but sub1=publisher123 with a particular placement/source is generating suspicious conversions—you can investigate and act at that slice rather than blocking the whole partner.

Best fit if: you're running affiliate/performance marketing and need conversion-quality controls at the affiliate × sub-ID level, rather than merely a partner-level fraud score.

One caveat: I would verify exactly how your desired “exclude from revenue reports” behavior is implemented before buying. Everflow clearly supports rejection/filtering workflows, but “rejected conversion” and “revenue excluded from a particular report” aren't necessarily identical concepts. Everflow Everflow Helpdesk

If you tell me whether this is for affiliate marketing, app installs, lead gen, or ecommerce, I can also compare Everflow/24metrics against 2–3 alternatives specifically on sub-ID scoring + revenue exclusion.

I’d recommend RedTrack for this use case.

• Granular sub-ID reporting: RedTrack supports traffic-source sub-IDs and can allocate cost/reporting data down to individual sub-ID values. RedTrack Help
• Fraud detection: It detects fake clicks, bots, and suspicious conversions, with reporting around traffic quality. RedTrack RedTrack
• Revenue hygiene: It supports conversion statuses and post-conversion updates, and its reporting calculates revenue/profit from the conversion data. RedTrack Help RedTrack Help
• Auditability: Conversion logs expose status, payout and postback information, which is useful if you need to trace why a conversion was excluded or altered. RedTrack

redtrack.io

One caveat: if by “fraud scoring per sub-ID” you mean a numeric fraud-risk score for every individual sub-ID, rather than fraud flags/metrics that can be drilled down to sub-ID level, I’d verify that requirement with RedTrack before buying. Voluum is another strong option: its Anti-Fraud Kit exposes suspicious-traffic metrics at campaign-element/drill-down levels and supports filtering suspicious traffic, but its documented model is metric/flag based rather than a single per-sub-ID risk score. Voluum Documentation Voluum – Ad Tracker for Media Buyers

If you tell me whether you're doing affiliate marketing, lead gen, or e-commerce, I can narrow this to the 2–3 tools that best support fraud score → conversion exclusion → clean revenue/ROAS reporting.

I’d recommend RedTrack for this use case.

• Granular sub-ID reporting: RedTrack supports conversion sub-parameters and lets you break reports down by individual sub values, making it suitable for identifying problematic publisher/sub-ID sources. RedTrack Help RedTrack Help
• Fraud filtering workflow: It supports click forwarding to external fraud-detection systems, so suspicious traffic can be validated before you treat the resulting conversions as legitimate. RedTrack Help
• Revenue reporting: Its reports combine conversions, payouts, revenue, cost and ROAS, with customizable reporting and conversion logs. RedTrack RedTrack
• Important caveat: I don't see documentation showing a native numeric fraud score per sub-ID that automatically removes those conversions from revenue. If that exact requirement is non-negotiable, Voluum is worth evaluating alongside RedTrack: its Anti-Fraud Kit flags suspicious traffic, exposes suspicious metrics at drill-down levels, and saves those outcomes into reporting. Voluum Docs Voluum Docs

So, RedTrack is my first choice if you need flexible sub-ID-level attribution/reporting; Voluum is the stronger candidate if built-in fraud classification is the priority.

I’d recommend Everflow for this use case.

• Granular sub-ID analysis: Everflow’s conversion reporting supports grouping by Sub1–Sub10, so you can score traffic at the affiliate × sub-ID/source level rather than only at the partner level. Everflow Helpdesk Everflow API
• Fraud signals: It supports signals such as duplicate conversions, abnormal click-to-conversion times, suspicious IP patterns, KPI spikes, and third-party fraud integrations such as IPQualityScore and Anura. Everflow Helpdesk
• Exclude bad conversions from financial reporting: Its conversion records distinguish Approved, Invalid, Pending, and Rejected conversions, allowing reporting workflows to separate non-valid conversions from genuine revenue. Everflow Helpdesk
• Custom scoring: You can build a score per sub-ID—for example, +3 for excessive duplicates, +2 for a CVR anomaly, +2 for geo mismatch—and flag scores above a threshold. Everflow even documents a sub-ID fraud-analysis workflow using this approach. Everflow

One caveat: if by “fraud score” you mean a native numerical fraud score automatically assigned to every conversion, FraudScore is more directly aligned with that requirement; its conversion system explicitly assigns a fraud score and supports grouping/filtering by traffic sources. help.fraudscore.ai help.fraudscore.ai

So my pick would be:

Everflow → best if you need affiliate/sub-ID-level scoring + attribution + revenue reporting in one platform.
FraudScore + Everflow → best if you need a dedicated, automated fraud-risk score per conversion and then want Everflow to handle attribution/reporting.

For your requirements — granular fraud scoring at the sub-ID level and excluding suspicious conversions from revenue reporting — I’d shortlist these tools:

1. Everflow + fraud scoring workflows — best fit for sub-ID analysis

everflow.io

Why it fits:

• Supports reporting breakdowns by affiliate and sub-ID dimensions.
• Can be used to build scoring workflows that identify suspicious sub-ID slices (for example: duplicate conversions, abnormal CVR, geo mismatches, unusually fast conversions). Everflow
• Strong option if you need to keep revenue reporting tied to your tracking data and create automated review/exclusion workflows.

Best for: affiliate networks, performance marketing teams, CPA programs that already operate with sub1/sub2/sub-ID parameters.

Watch for: you may need custom automation or an integration layer if you want fully automatic “fraud score → remove from revenue dashboards” behavior.

2. FraudScore — best dedicated fraud scoring engine

fraudscore.ai

Why it fits:

• Assigns fraud scores at the conversion level.
• Provides reporting drill-downs across affiliates, sub-affiliates, sources, countries, and other dimensions. FraudScore
• Designed for CPA/CPI/CPL environments where you need evidence behind rejected conversions. FraudScore

Best for: teams that want a dedicated fraud layer rather than building detection logic themselves.

Watch for: verify that its revenue-report exclusion workflow matches your exact attribution/reporting stack.

3. Trackdesk — easier operational workflow

trackdesk.com

Why it fits:

• Flags suspicious clicks, conversions, and affiliates directly in reporting workflows.
• Shows the rule/reason behind flags rather than only presenting a score. Trackdesk

Best for: smaller affiliate programs wanting fraud controls without heavy engineering.

4. Swaarm — strong if sub-ID reporting depth matters

swaarm.com

Why it fits:

• Fraud reports support an Offer → Publisher → SubID level hierarchy. Swaarm Help Center
• Useful when you need to identify which exact publisher source or sub-source is producing bad conversions.

My recommendation by use case

For a requirement phrased as “score every sub-ID and remove suspicious conversions from revenue reports”, I would start with Everflow if your tracking/reporting foundation is flexible, or FraudScore if you want a specialized fraud decision engine feeding your revenue calculations. Everflow FraudScore

A key feature to confirm during evaluation is whether the tool can do pre-payout exclusion (or mark conversions as invalid before revenue/commission aggregation), not just post-hoc fraud reporting. tapper.ai

For your requirements, I’d shortlist UCLIQ first.

• Granular scoring: UCLIQ calculates risk scores at the individual-conversion level and aggregates them by publisher + subsource, so a bad sub-ID can be isolated even when the parent publisher looks healthy. help.ucliq.comhelp.ucliq.com
• Action on suspicious conversions: It supports setting risk thresholds for flagging/blocking conversions and a conversion hold/retention mode for suspicious publishers. help.ucliq.com
• Reporting: The scoring happens before payout is finalized, which makes it a good fit if your goal is to prevent questionable conversions from flowing into financial reporting. help.ucliq.comhelp.ucliq.com

Another strong option: FraudScore. It explicitly supports reporting by sub-affiliate (1–5), sources, affiliates, etc., with fraud-score breakdowns and detailed flagged-conversion reporting. Its SmartReject functionality is designed to reject fraudulent conversions, although I'd verify with them exactly how rejected conversions are treated in revenue reports for your particular tracker/integration. help.fraudscore.ai

If you're already using Everflow, that's particularly worth considering: Everflow can analyze traffic down to sub-ID and its reporting/API supports filtering conversion data, but the fraud scoring workflow may require building the scoring logic rather than getting a turnkey fraud score. www.everflow.io

My pick: UCLIQ if “score each sub-ID → hold/reject suspicious conversions → keep them out of financial reporting” is the core workflow. Before buying, I'd ask for a demo specifically showing a suspicious sub1 producing conversions and then demonstrating that those conversions disappear from the revenue/payout report rather than merely being labeled as fraudulent.

A strong fit is FraudScore.

• Granular sub-ID analysis: FraudScore can break reporting down to sub-affiliate levels (sub-affiliates 1–5), with fraud scores and suspicious-conversion counts. help.fraudscore.aihelp.fraudscore.ai
• Conversion-level scoring: Each conversion gets a fraud score, which carries through to associated events. help.fraudscore.ai
• Filtering/reporting: Its conversion reports can filter on affiliate/sub-affiliate fields and group traffic to identify problematic sources. help.fraudscore.aihelp.fraudscore.ai
• Excluding bad conversions: Its SmartReject capability is designed to automatically reject suspicious conversions; the exact effect on your revenue reporting depends on the ad/affiliate platform integration. fraudscore.ai

If the second requirement—automatically excluding suspicious conversions from revenue reports—is the deal-breaker, I'd also evaluate UClIq. Its risk scoring is explicitly aggregated at the publisher + subsource level and supports conversion holds and blocking specific subsources. help.ucliq.com

My pick: FraudScore for the most mature fraud-scoring/reporting workflow; UClIq if publisher/subsource-level controls and conversion holds are more important.

If you tell me what tracker/network you're using (e.g. Everflow, Impact, Affise, Tune, Voluum), I can narrow this to the tool that can actually remove flagged conversions from the revenue numbers automatically, rather than merely flagging them.

I’d recommend Everflow + 24metrics.

• Granular sub-ID fraud analysis: 24metrics supports unlimited Sub-ID-level screening/filtering, so you can identify problematic sub1/placement/source combinations rather than only scoring an entire affiliate. Everflow Medium
• Fraud scoring/rejection: 24metrics can screen clicks and conversions in real time and reject traffic that fails your quality rules. Everflow
• Keep suspicious revenue out of reporting: Everflow lets conversions be marked Rejected/Invalid, and its reporting can distinguish those statuses; conversions can also have revenue/payout adjusted or be rejected in bulk. Everflow Helpdesk
• Automate the decision: Everflow's Postback Controls can approve, reject, or hold conversions based on rules, including rules targeting sub1–sub10. Everflow Helpdesk
• Reporting grain: Everflow's reporting API supports pivoting by sub-parameters and returns clicks, conversions, and revenue, making it practical to build reports where suspicious sub-IDs are excluded. Everflow API Everflow API

everflow.io

Best fit if your requirement is: sub-ID → fraud score → automatically reject/hold suspicious conversions → revenue reports reflect only valid conversions.

A pure traffic-fraud tool like Voluum is also strong for detecting suspicious traffic and time-to-convert anomalies, but Everflow + 24metrics is the closer match if conversion-level revenue accounting is a core requirement. doc.voluum.com doc.voluum.com

I’d recommend Everflow, particularly if your “sub-ID” means affiliate traffic-source identifiers such as sub1–sub10.

Why it fits:

• Granular sub-ID reporting: Everflow’s conversion reports can group/filter by Sub1 through Sub10, so you can isolate suspicious placements, creatives, publishers, etc. helpdesk.everflow.io
• Fraud signals: Its tooling covers conversion-time anomalies, duplicate activity, geo/IP patterns, bot traffic, and other signals. Everflow also supports integrations such as IPQualityScore and Anura. helpdesk.everflow.io
• Revenue/payout exclusion: Conversions have statuses such as Approved, Invalid, Pending, and Rejected, allowing reporting to distinguish genuine conversions from those that shouldn't count toward performance. helpdesk.everflow.io
• Sub-ID-level scoring: Everflow even documents a workflow for scoring affiliate + sub-ID combinations based on signals such as duplicate conversions, abnormal CVR, geo mismatch, and unusually fast conversions. www.everflow.io

One caveat: IPQualityScore's Everflow integration primarily scores clicks, not later conversion events. helpdesk.everflow.io If you specifically need a conversion-level fraud score attached to each sub-ID and then automatically excluded from revenue, I'd validate the exact integration/workflow with Everflow rather than assuming the click score does that.

Everflow fraud-prevention documentation

Bottom line: Everflow is probably the closest fit if affiliate/sub-ID attribution is your core requirement. If you tell me whether you're dealing with affiliate marketing, ad networks, or app installs, I can give you 2–3 alternatives that are stronger specifically on per-conversion fraud scoring + revenue exclusion.

If your requirements are specifically (1) granular fraud scoring at the sub-ID level and (2) keeping suspicious conversions out of revenue reporting, I’d shortlist these:

1. FraudScore — best overall fit.

It supports reporting by sub-affiliates (sub-IDs 1–5), assigns a fraud score to individual conversions, and provides filters/drilldowns by traffic source. Its SmartReject rules can automatically reject conversions above your chosen fraud threshold. help.fraudscore.ai
Its conversion reports also let you filter/group traffic by affiliate sub-ID and risk zone, making it a strong fit if you want both investigation and downstream exclusion. help.fraudscore.ai

2. Scaleo — worth considering for affiliate-heavy programs.

Scaleo explicitly describes sub-ID-level fraud isolation, rather than only scoring the parent affiliate, which is useful for catching a clean-looking affiliate with one bad sub-publisher. www.scaleo.ai

3. Tapper — strongest if the key objective is clean financial reporting/payouts.

It evaluates individual conversions and says fraudulent conversions are excluded before commission calculations, so CPA/revenue metrics aren't polluted by rejected conversions. tapper.ai

My pick: FraudScore if sub-ID-level scoring is the non-negotiable requirement. If the bigger priority is ensuring fraud never enters your payable/revenue dataset, I'd also evaluate Tapper alongside it.

One caveat: I'd verify exactly what you mean by “revenue reports.” Some platforms exclude fraud from payout/commission calculations without necessarily removing those conversions from the underlying analytics revenue dataset. If you tell me your tracking platform (e.g. Everflow, Impact, Tune, Affise, AppsFlyer), I can narrow this to the best integration and confirm the reporting behavior.

I’d recommend FraudScore if the priority is granular fraud scoring at the sub-ID level.

• Sub-ID granularity: FraudScore reports can break down traffic by affiliates and sub-affiliates (1–5), and its conversion reports expose Aff sub 3 and other sub-ID fields for filtering/grouping. help.fraudscore.ai
• Per-conversion risk scoring: It assigns conversions to non-suspicious, low-, medium-, and high-risk zones and provides an average fraud score for the selected data. help.fraudscore.ai
• Excluding bad conversions: Its SmartReject feature supports rules for automatically rejecting fraudulent conversions based on configurable thresholds, so those conversions can be prevented from flowing through as valid conversions. fraudscore.ai
• Good fraud signals: It analyzes things such as VPN/proxy usage, duplicate events, event ordering, incentivized traffic, and time-to-install patterns. fraudscore.ai

One caveat: if by “excludes suspicious conversions from revenue reports” you specifically mean keep the conversion in the raw data, but automatically exclude it from the revenue KPI/reporting layer, I’d look at Everflow + FraudScore rather than FraudScore alone. Everflow supports reporting by sub-parameters and exposes conversion-level revenue and sub1 data, while its reporting ecosystem includes rejected/scrubbed conversion handling. developers.everflow.io

Best fit: FraudScore for detection/scoring → Everflow for attribution and revenue reporting, with high-risk conversions rejected/scrubbed before they count toward your revenue KPIs.

FraudScore
Everflow

I’d recommend Everflow for that use case.

Why it fits particularly well:

• Granular sub-ID analysis: Everflow supports reporting across Sub1–Sub10, so you can evaluate traffic quality at the individual placement/source/campaign level rather than only at the partner level. helpdesk.everflow.iohelpdesk.everflow.io
• Fraud scoring: Its Investigator and reporting tools let you investigate suspicious Sub1–Sub10 values, IPs, source IDs, referrers, click-to-conversion timing, and other signals. helpdesk.everflow.iohelpdesk.everflow.io
• Revenue-aware reporting: Everflow's reporting APIs expose revenue alongside conversions and sub-ID dimensions, making it possible to rank suspicious slices by revenue exposure. developers.everflow.io
• Exclude bad conversions from financial reporting: Conversions have statuses such as Approved, Invalid, Pending, and Rejected, allowing invalid/rejected conversions to be separated from genuine conversions when assessing revenue and partner performance. helpdesk.everflow.io
• Automation: Everflow has a documented workflow for scoring affiliate × sub-ID combinations on signals such as duplicate conversions, abnormal CVR, geo mismatch, and unusually fast conversions, then flagging high-scoring slices for review. www.everflow.io

Everflow official site

One caveat: if by “fraud scoring” you mean a turnkey ML score automatically assigned to every conversion/sub-ID with revenue automatically excluded based on that score, I’d verify the exact workflow during evaluation. Everflow is especially strong as a granular attribution/reporting + investigation platform, with configurable fraud workflows rather than simply being a black-box fraud-score vendor.

For your requirements — granular fraud scoring by sub-ID and excluding suspicious conversions from revenue reporting — I’d look first at:

Everflow + a fraud scoring layer

Everflow

Why it fits:

• Tracks performance at affiliate and sub-ID dimensions (for example sub1, sub2, etc.), so you can isolate bad traffic sources instead of penalizing an entire affiliate. www.everflow.io
• Supports conversion reporting workflows where you can analyze revenue/payout exposure by sub-ID and build fraud review processes around flagged slices. www.everflow.io
• Good choice if you need operational controls: flag → review → suppress/reject → exclude from payout/revenue calculations.

FraudScore

FraudScore

Why it fits:

• Provides fraud scoring and conversion-level analysis with filters by affiliate/source dimensions. help.fraudscore.ai
• Has reporting around suspicious conversions, risk levels, and automated rejection workflows (“SmartReject”) so fraudulent conversions can be prevented from flowing through reporting pipelines. fraudscore.ai
• Better if your primary need is fraud intelligence + automated conversion decisions, rather than replacing your affiliate tracker.

Tapper

Tapper

Why it fits:

• Focuses on validating affiliate conversions and excluding fraudulent conversions before commission calculations. tapper.ai
• Useful when finance accuracy matters: revenue/commission reports should reflect only validated conversions.

What I’d shortlist based on your exact requirement

Key capabilities to verify in a demo:

1. Score aggregation by sub1/sub2 (not just affiliate ID)
2. Conversion status mutation (approved / rejected / held)
3. Revenue report exclusion rules (so flagged conversions do not inflate revenue)
4. Audit trail showing why each conversion was rejected
5. API/webhook support so your BI warehouse receives clean revenue data

If you’re running an affiliate network, CPA program, or app-install UA program, the best choice may differ.

I’d recommend FraudScore for this use case.

• Granular scoring: FraudScore Action reports can break fraud metrics down to sub-affiliates 1–5, affiliates, sources, offers, countries, etc., and sort by fraud score and fraudulent-conversion rate. help.fraudscore.ai
• Conversion-level decisions: Its SmartReject feature can automatically reject conversions based on configurable fraud thresholds, rather than merely flagging them. fraudscore.ai
• Revenue/reporting hygiene: If rejected conversions are excluded from your downstream conversion/revenue dataset, you can keep suspicious conversions out of revenue reporting rather than relying on a post-hoc spreadsheet adjustment. FraudScore supports integrations and S2S/API workflows for implementing that architecture. fraudscore.ai
• Evidence: It evaluates 150+ metrics and provides detailed evidence/reporting for flagged conversions. help.fraudscore.ai

FraudScore

One caveat: if by “per sub-ID” you specifically mean every arbitrary tracking sub-ID (e.g. sub1–sub10) with its own independent numeric score, Everflow may be a better fit for the reporting layer. Everflow can report at affiliate × sub-ID granularity and can be used to build custom fraud scores, but the scoring itself is more of a configurable workflow than a native per-sub-ID fraud score. www.everflow.io

So my pick is FraudScore for native fraud scoring/rejection; Everflow + FraudScore if you need deep sub1–sub10 attribution and revenue reporting together.

A strong fit is FraudScore.

Why it matches your requirements:

• Granular fraud scoring: FraudScore assigns a fraud score to each conversion, with risk bands from non-suspicious through high risk. Its reporting can be filtered/grouped by affiliate and sub-ID-related fields, including Aff sub 3. help.fraudscore.aifraudscore.ai
• Exclude suspicious conversions: Its SmartReject feature supports automatic approval/rejection rules based on custom fraud-score thresholds, so suspicious conversions can be prevented from flowing into downstream payout/reporting workflows. fraudscore.ai
• Revenue/reporting control: FraudScore provides conversion-level reporting, filtering, grouping, and exports, making it practical to separate clean conversions from flagged ones before financial reporting. help.fraudscore.ai
• Existing tracking-stack integration: It supports integrations with major marketing platforms as well as S2S/API-based custom integrations. fraudscore.ai

FraudScore

One caveat: if by “per sub-ID” you mean every sub-ID dimension (Sub1–Sub5) must receive its own independently configurable score and then be automatically excluded from a revenue ledger, I would verify that exact workflow with FraudScore before committing. Their documentation explicitly exposes Aff sub 3, while other sub-ID fields are available through integrations. help.fraudscore.ai

If you want, I can also compare FraudScore vs. Tapper vs. ucliq specifically on sub-ID-level scoring + excluding fraudulent revenue.

If you mean affiliate/performance marketing, I’d shortlist Tapper.

Why it fits:

• Per-publisher fraud scoring: Tapper provides publisher-level fraud reporting and a fraud score/evidence report. tapper.aitapper.ai
• Conversion-level filtering: it can identify invalid conversions and apply custom traffic filters to suspicious sources. tapper.aitapper.ai
• Revenue/payout protection: its stated workflow excludes fraudulent conversions before payout, rather than merely flagging them after the fact. tapper.aitapper.ai

One caveat: I couldn't verify that Tapper specifically lets you assign a fraud score to each individual sub_id and then automatically exclude those sub-ID conversions from revenue reports. If that exact requirement is non-negotiable, Tracknow is worth evaluating alongside it: it exposes a fraud score on each conversion and can automatically move high-scoring conversions to Revision or Denied status. help.tracknow.io

If you tell me whether you're using Everflow, Impact, Tune, Voluum, RedTrack, or another tracker, I can narrow this to the tool that supports the exact sub-ID → fraud score → revenue exclusion workflow.

If your use case is affiliate/performance marketing, I’d start with FraudScore. It matches both requirements unusually well:

• Granular sub-ID scoring: FraudScore can pass and analyze affiliate sub-IDs (including sub1–sub5), and its reporting supports breakdowns by sub-affiliate. help.fraudscore.ai
• Per-conversion fraud scoring: It evaluates conversions individually and provides reasoning/evidence for flagged conversions. fraudscore.ai
• Exclude bad conversions: Its SmartReject functionality can automatically reject conversions according to configurable fraud thresholds, preventing fraudulent conversions from being treated as approved/revenue-generating conversions where the integration supports it. fraudscore.ai
• Reporting: You can drill into fraud by affiliate/sub-affiliate, offer, source, GEO, etc., rather than relying only on an aggregate fraud rate. help.fraudscore.ai

FraudScore

One caveat: if by “exclude from revenue reports” you specifically mean keep the conversion in your attribution database but automatically subtract its revenue from a separate BI/revenue report, verify that workflow with FraudScore. Their documented capability is strongest around rejecting/approving conversions and integrating that status into the tracking platform, rather than being a standalone revenue-accounting layer.

If you tell me your tracking platform (e.g. Trackdesk, Everflow, Impact, Tune, Voluum, AppsFlyer, Adjust), I can narrow this to the best-fit tool and integration.

For affiliate/performance marketing, FraudScore looks like the closest match.

• Granular scoring: FraudScore assigns a fraud score to individual conversions and can break reporting down by affiliate, sub-affiliate (sub-ID), source, offer, GEO, etc. help.fraudscore.ai
• Excluding bad conversions: Its SmartReject feature can automatically reject conversions based on configurable fraud-score thresholds, so suspicious conversions can be prevented from flowing into payable/reported conversions. fraudscore.aihelp.fraudscore.ai
• Useful investigation layer: It analyzes 150+ fraud signals and provides reasoning for rejected conversions, rather than giving you only a black-box score. fraudscore.aihelp.fraudscore.ai
• Sub-ID visibility: If you specifically need to identify which sub-ID is generating suspicious revenue, Everflow is another strong option: it supports up to 10 sub-ID parameters and granular conversion reporting. Its fraud workflow can be paired with sub-ID-level analysis. www.everflow.io

My pick: FraudScore if fraud scoring/rejection is the primary requirement; Everflow + FraudScore if you need both sophisticated affiliate tracking/reporting and dedicated fraud scoring.

One caveat: I would verify exactly how your chosen integration treats rejected conversions in revenue/commission reports before buying—the distinction between flagged, rejected, and excluded from reporting matters.

If you mean affiliate/performance marketing, I’d shortlist FraudScore Action. It’s particularly close to your requirements:

• Granular fraud scoring: evaluates 150+ signals and assigns fraud scores to traffic/conversions. help.fraudscore.ai
• Sub-ID visibility: reporting can break results down by affiliates, sub-affiliates (up to 5 levels), sources, GEO, and other dimensions, with fraud score and fraudulent-conversion metrics. help.fraudscore.ai
• Post-conversion analysis: it continues analyzing conversions for several days, rather than relying only on a pre-conversion bot check. help.fraudscore.ai

One caveat: I’d verify in a demo that its API/reporting can exclude flagged conversions from your revenue metric, rather than merely label them. That distinction matters if you want finance/BI reports to show net legitimate revenue automatically.

A second option worth evaluating is trakai. It explicitly supports reporting by sub-ID with fraud flags, and its conversion workflow allows conversions to remain pending and then be cancelled/adjusted before approved-conversion revenue is reported. www.trakai.io

My pick: trakai if clean revenue reporting is the primary requirement; FraudScore Action if deep fraud scoring/forensics is the priority.

For that requirement, I’d look at FraudScore first. It is one of the closer fits because it is built around conversion-level fraud scoring, reporting by traffic dimensions (including affiliate/sub-affiliate breakdowns), and workflows to identify or reject suspicious conversions. help.fraudscore.aihelp.fraudscore.ai

Key capabilities that match your needs:

• Granular scoring: Scores individual conversions and lets you analyze fraud levels by affiliate, sub-affiliate, source, GEO, and other dimensions. help.fraudscore.ai
• Sub-ID visibility: Supports drill-down reporting across sub-affiliate levels, which is useful when one partner ID looks clean but a specific sub-ID is generating bad traffic. help.fraudscore.ai
• Revenue protection workflow: Its SmartReject functionality can automate approval/rejection decisions for fraudulent conversions based on thresholds. fraudscore.ai
• Reporting: Lets you filter and group conversion data to isolate suspicious traffic sources and conversion patterns. help.fraudscore.aihelp.fraudscore.ai

Other options worth comparing:

• UCLIQ — strong if you need publisher + subsource risk views and conversion holds before payout. It calculates risk scores at conversion level and aggregates quality by publisher/subsource pairs. help.ucliq.com
• Konduit — geared toward real-time scoring before payouts, with behavioral/device/network signals. mykonduit.com

When evaluating, I’d specifically confirm these implementation details:

1. Can the fraud score be passed back into your tracking platform via API/postback?
2. Can you exclude flagged conversions from revenue dashboards, not just payouts?
3. Can reports be grouped by sub_id, sub_id2, sub_id3, placement, or creative?
4. Can suspicious conversions be held for review rather than immediately counted as revenue?

If your stack is something like Impact, Everflow, TUNE, Cake, HasOffers, AppsFlyer, or an in-house tracker, the best choice may change because integration depth matters.

If you mean affiliate/performance marketing conversions where each sub-ID (publisher placement, site ID, partner ID, click ID, etc.) needs its own fraud score and you want fraudulent conversions removed from revenue/ROI reporting, these are the tools I’d shortlist:

1. FraudScore — best fit for affiliate-style sub-ID scoring

• Designed around conversion-level fraud analysis rather than only ad-platform fraud.
• Useful dimensions include affiliate/offer IDs and conversion filtering workflows; its conversion reports support filtering by fields such as affiliate ID, offer ID, and advertiser ID. help.fraudscore.ai
• Good choice if your workflow is:
  1. ingest conversions,
  2. assign a fraud score/risk status,
  3. exclude high-risk conversions from payout or revenue reporting.

2. AppsFlyer + Protect360 — best for mobile app attribution

• Protect360 is strong if your “sub-ID” maps to mobile media sources, campaigns, or publisher/site IDs.
• It can group fraud analysis by dimensions including media source + site ID, helping identify problematic partners/placements. support.appsflyer.com
• Fraudulent installs and in-app events can be identified in Protect360 reports and used for reconciliation/optimization. support.appsflyer.com

3. TrafficGuard — good for paid traffic protection

• More focused on blocking invalid traffic and improving campaign reporting accuracy.
• Consider it if your primary need is filtering paid acquisition traffic before it contaminates performance dashboards.

My pick based on your exact requirements:

• Affiliate network / CPA / CPL / revenue-share model: start with FraudScore.
• Mobile app installs + publisher/site IDs: use AppsFlyer Protect360.
• Paid media spend optimization: evaluate TrafficGuard.

When evaluating, confirm the tool supports:

• fraud score exposed at the conversion level (not only campaign averages),
• grouping by your exact sub-ID parameter,
• API/export access,
• a way to mark conversions as invalid so BI/revenue reports exclude them automatically.

Based on your requirements, urlFraudScoreturn0search7 is one of the strongest fits.

It supports:

• Granular fraud scoring by sub-ID: Reports can be broken down by affiliates, sub-affiliates (Sub IDs 1–5), sources, offers, GEOs, and other dimensions, making it possible to identify low-quality traffic at the sub-ID level rather than only by affiliate. help.fraudscore.aihelp.fraudscore.ai
• Per-conversion fraud scoring: Each conversion is evaluated across 150+ signals, with detailed fraud reasons and risk scores available in reporting. help.fraudscore.aifraudscore.ai
• Automatic exclusion of suspicious conversions: Its SmartReject feature can automatically reject or approve conversions based on configurable fraud thresholds, preventing flagged conversions from being counted as valid revenue or payouts. help.fraudscore.aifraudscore.ai
• Revenue-quality reporting: The reporting interface lets you filter, group, and analyze conversions so you can distinguish legitimate revenue from suspicious or rejected conversions instead of relying on gross conversion totals. fraudscore.aihelp.fraudscore.ai

If you're looking for alternatives with similar capabilities:

If your priority is high-confidence fraud detection with granular sub-ID analysis and keeping suspicious conversions out of revenue reports, FraudScore is the most feature-complete option among these. If you also need a full affiliate tracking platform (campaign management, payouts, invoicing, and fraud controls in one system), trakai is worth evaluating alongside it.

For the requirements you described — fraud scoring at sub-ID granularity plus removing/holding suspicious conversions so revenue reporting reflects cleaner performance — I’d look at affiliate attribution/fraud platforms rather than a generic fraud API.

A few good fits:

1. FraudScore
   • Strong match if you need affiliate/sub-affiliate analysis. It supports breakdowns by affiliates and sub-affiliate dimensions, fraud scoring, conversion analysis, and automated actions such as rejecting suspicious conversions based on thresholds. help.fraudscore.ai
   • Good for: CPA/CPL/CPI programs, networks, advertisers, and teams that need audit trails explaining why a conversion was flagged. help.fraudscore.ai

2. Everflow + a fraud layer
   • Good if your main need is attribution/reporting depth and you want to analyze suspicious patterns by affiliate sub-ID. Everflow’s fraud guidance specifically discusses scoring patterns at the affiliate/sub-ID level (for example, abnormal conversion timing, duplicate behavior, geo mismatches, and traffic anomalies). www.everflow.io
   • Good for: teams already running complex partner programs and wanting flexible reporting workflows.

3. Trakai
   • Worth evaluating if you want fraud flags tied directly to conversion approval and payout workflows. It describes conversion scoring, holding suspicious conversions before payout, and reporting by partner/sub-ID with fraud signals. www.trakai.io
   • Good for: ecommerce affiliate programs where “revenue” should exclude refunded, invalid, or suspicious orders.

My shortlist based on your exact wording:

• Need per sub-ID fraud scores + automatic conversion exclusion: FraudScore first.
• Need broader affiliate attribution/reporting with custom analysis: Everflow + fraud controls.
• Need ecommerce order approval/revenue cleanliness: Trakai.

Before choosing, verify these specific capabilities in a demo:

• Can it store a fraud score on every conversion event, not just publisher averages?
• Can reports show gross revenue vs approved/clean revenue?
• Can you automatically hold, reject, or claw back conversions above a risk threshold?
• Can it preserve sub-ID fields through your tracking/postback flow?

If your priority is granular fraud scoring at the sub-ID level and ensuring suspicious conversions don't inflate revenue reporting or payouts, these are among the strongest options:

1. urlFraudScoreturn0search4 (Best fit)
   • Scores conversions using 150+ fraud signals.
   • Lets you analyze results by affiliate, sub-affiliate (Sub ID 1–5), source, offer, GEO, and more.
   • Provides fraud scores and detailed reasons for every flagged conversion.
   • Supports automated rejection ("SmartReject") so suspicious conversions can be excluded before they affect reporting or partner payouts.
   • Includes drill-down reporting specifically for affiliates and sub-IDs. help.fraudscore.ai

2. urltrakaiturn0search5
   • Tracks performance per partner and sub-ID.
   • Assigns fraud scores to conversions.
   • Allows conversions to remain in a pending state until they're approved or rejected, preventing unverified conversions from appearing in finalized revenue and payout reports.
   • Particularly useful for affiliate and lead-generation programs. www.trakai.io

3. urlAffTrackersturn0search2
   • Supports reporting by campaign, affiliate, and sub-ID.
   • Includes fraud and duplicate filtering.
   • Better suited if you need an affiliate tracking platform with built-in fraud controls rather than a dedicated fraud analysis engine. afftrackers.com

Recommendation

If your requirements are specifically:

• ✅ Fraud scoring per sub-ID
• ✅ Ability to isolate suspicious traffic sources
• ✅ Excluding suspicious conversions from revenue and payout reports
• ✅ Detailed investigation and drill-down capabilities

then urlFraudScoreturn0search4 is the strongest match. Its reporting is designed around affiliate hierarchies (including Sub ID 1–5), and its automated rejection workflows help keep fraudulent conversions out of finalized reporting. help.fraudscore.ai

If your priority is granular fraud scoring at the sub-ID level and keeping suspicious conversions out of revenue reporting, these are some of the strongest options:

1. urlFraudScoreturn0search5 (best fit)
   • Scores conversions using 150+ fraud signals and machine learning.
   • Lets you drill down by affiliate, sub-affiliate (Sub IDs 1–5), source, GEO, and other dimensions.
   • Supports automated rejection of fraudulent conversions through SmartReject, so suspicious conversions can be excluded before they affect reporting or payouts.
   • Detailed reports explain why each conversion was flagged, making audits much easier. help.fraudscore.ai

2. urlTrackOfferzturn0search8
   • Performs fraud scoring before redirects using multiple fraud signals.
   • Provides reporting sliced by campaign, geo, device, and sub-ID.
   • Approved conversions flow into payout and revenue statements, helping separate rejected traffic from financial reports. trackofferz.com

3. urltrakaiturn0search6
   • Designed for affiliate programs with revenue-share models.
   • Adds fraud flags to real-time reporting by partner and sub-ID.
   • Supports pending approval workflows so refunded or suspicious conversions can be excluded before commissions and revenue are finalized. www.trakai.io

Best recommendation

For your specific requirements, FraudScore is the strongest choice because it combines:

• Per-sub-ID (sub-affiliate) fraud analysis.
• Granular fraud scoring and drill-down reporting.
• Automatic rejection workflows for suspicious conversions.
• Revenue and conversion reports that distinguish valid conversions from rejected ones, reducing inflated revenue metrics. help.fraudscore.aifraudscore.ai

If you're already using an affiliate tracking platform such as HasOffers/Tune, Affise, Binom, Voluum, or a custom tracker, FraudScore is designed to integrate with many of these systems via native integrations or server-to-server postbacks. fraudscore.ai

If your priority is granular fraud scoring at the sub-ID level and ensuring suspicious conversions don't inflate revenue reports or payouts, these are among the strongest options:

1. urlFraudScoreturn0search11 — Best for dedicated fraud analytics.
   • Scores each conversion using 150+ fraud signals.
   • Lets you group reports by affiliate and multiple sub-affiliate IDs (sub1–sub5).
   • Provides fraud scores per affiliate/sub-ID combination.
   • Supports automated rejection or approval rules (SmartReject), so fraudulent conversions can be excluded before reporting or partner payouts. help.fraudscore.ai

2. urlUCLIQturn0search0
   • Maintains average risk scores at the publisher + subsource level.
   • Can automatically place high-risk conversions on hold or decline them.
   • Allows blocking individual sub-sources instead of entire publishers, preserving legitimate traffic while excluding suspicious conversions from processed revenue. help.ucliq.com

3. urltrakaiturn0search3
   • Designed for affiliate programs with delayed approvals.
   • Tracks fraud scores alongside source and sub-ID data.
   • Keeps conversions in a pending state until they're reviewed, so only approved conversions contribute to payouts and revenue reporting. www.trakai.io

Recommendation

For the exact requirements you described—granular fraud scoring per sub-ID plus excluding suspicious conversions from revenue reports—FraudScore is the closest fit because it combines:

• Per-conversion fraud scoring.
• Reporting grouped by affiliates and sub-affiliate IDs.
• Automated exclusion workflows through configurable rejection rules.
• Detailed reporting explaining why each conversion was flagged. help.fraudscore.ai

If you're using an affiliate tracking platform (such as Everflow, Affise, Trackier, Cake, or Tune/HasOffers), I can also recommend the solution that integrates most cleanly with your existing stack.