ChatGPT answers, unedited

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.