AI-assisted fraud detection, built for SaaS abuse.

Most fraud detection was built for banks, payment processors, and enterprise e-commerce. The problems are real, but they're not your problems. SaaS teams face a different fraud surface: free trial abuse, fake signups, duplicate accounts, bot traffic, content spam, and credential stuffing: pattern-matching problems that don't need a transaction stream to detect.

Dregs is AI-assisted fraud detection purpose-built for SaaS. One tracking script, one webhook, a near-instant fraud score on every user, and a pipeline of 22 analyzers running automatically from the first event.

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What Fraud Detection Actually Does

Fraud detection identifies suspicious users and abusive behavior by combining signals (device characteristics, identity data, network context, behavioral patterns) into risk scores or rule-driven alerts. The category is broad enough that "fraud detection" means very different things depending on the workload:

Payment fraud detection Transaction-level: card testing, chargebacks, money laundering. Lives next to your payment processor. Stripe Radar, Sift Payments, Riskified.

Account fraud detection Signup and login: stolen credentials, fake accounts, account takeover. Sift, SEON, Castle, and Stytch are the broad SaaS-and-fintech vendors here.

SaaS abuse detection What Dregs does: free trial abuse, duplicate accounts, bot signups, content spam, behavior-driven misuse. Patterns specific to self-serve SaaS.

These categories overlap, but the right tool depends on what you're actually losing money to. If chargebacks are your problem, your payment processor's fraud tools are the right answer. If freeloaders cycling through your trials are your problem, that's where Dregs lives.

What Dregs Brings to SaaS Fraud Detection

Dregs combines four capabilities that traditional fraud detection products handle separately, if at all. Every Dregs plan includes all of them.

Device Fingerprinting Cookieless device identification that survives incognito and VPNs. Catches the same person signing up with a fresh email on the same laptop, every time.

Identity Scoring Four identity scores per user (Humanity, Authenticity, Uniqueness, Behavior), updated with every event and surfaced in dashboard, API, and webhook.

Behavioral Analytics Score every user against your real-customer baseline. Bots, scrapers, and freeloaders show up as behavioral outliers without any rules to write.

Identity Graph Linked-account detection through shared devices, sessions, IPs, and behavioral overlap. Multi-account fraud rings unwind in a few graph hops.

How Dregs Fits Into Your Stack

The integration shape is deliberately boring:

Add the tracking script to your app's pages. Same shape as a product analytics tag.
Call identify and track when meaningful things happen: signup, login, purchase, key actions.
Dregs starts scoring immediately. Fraud scores appear in your dashboard from the first event.
(Optional) Set up webhooks so your application receives score updates and can react automatically: shadow ban a freeloader, gate a feature, require extra verification.
(Optional) Set up channels and escalations so manual review is reserved for the ambiguous cases, routed to your team via Slack, email, or webhook.

Most teams have steps 1–3 running the same day they sign up. Steps 4 and 5 are where you get to "fully automatic abuse prevention", and that's typically a week or two of work.

Where AI Fits Into Dregs

"AI fraud detection" can mean a lot of things, from product marketing on top of a logistic regression to genuine large-language-model reasoning over user data. Dregs is honest about the mix:

Deterministic analyzers The 22 built-in analyzers use regex, similarity functions, fingerprint matching, and rule-based logic. Fast, auditable, and consistent: every observation is traceable to specific signals.

Statistical baselining Behavioral and Uniqueness scoring continuously aggregates data across your identities to define what "normal" looks like for your application, then flags users whose patterns diverge.

LLM-assisted review On the Advanced plan, Anthropic's Claude reviews ambiguous identities and produces narrative explanations of why a user looks suspicious. Useful where deterministic signals stack up but don't tell the whole story, and for keeping false positives down on borderline cases.

AI-assisted analyzer development Custom JavaScript analyzers on the Advanced plan can be drafted and refined with LLM assistance, encoding domain-specific signals you describe in plain English into analyzer code that runs alongside the built-ins.

The deliberate choice: AI is genuinely useful for ambiguous cases and natural-language explanations, but most fraud signals are clearer and more auditable when computed deterministically. Every Dregs score opens into its observations, so you can see exactly why a user was flagged, which is harder to do with a pure ML black box. AI assists; it doesn't replace the underlying analyzer pipeline.

Dregs vs. Enterprise Fraud Detection Software

Enterprise fraud detection vendors (Sift, Riskified, ThreatMetrix, SAS) were built for transaction streams measured in billions. They have powerful platforms, custom rules engines, integration teams, and procurement processes that take months to navigate. They're not wrong for the workloads they were built for. But for a SaaS team trying to stop free trial abuse next week, they're the wrong shape.

Pricing Enterprise vendors quote based on annual transaction volume; Dregs bills per active identity per month. Plans start at $17/month.

Time to value Enterprise integrations typically take 2–6 months. Dregs runs the same day the tracking script lands.

Configuration Enterprise products require professional services to tune rules. Dregs ships with 22 built-in analyzers running automatically.

Integration Enterprise vendors push their own SDKs, complex APIs, often a managed service. Dregs is a tracking script plus optional webhook.

What Dregs Catches

Free Trial Abuse

Repeat signups with fresh emails, caught through device fingerprinting and the four-score system.

Duplicate Accounts

One person, several accounts. Linked through shared devices and behavioral overlap.

Bot Detection

Automation signatures, inhuman timing, and behavioral patterns expose bots that fake-signup at scale.

Fake Signups

Disposable emails, keyboard-mashed names, and junk profile data scored at submission.

Credential Stuffing

Velocity, identity cycling, and automation signatures catch stuffing attacks before they succeed.

Referral Fraud

Self-referrals exposed through shared devices, IPs, and sessions linking referrer to referred.

Pricing

Dregs bills against active identities rather than transactions. Plans start at $17/month for 100 active identities and scale linearly. Every plan includes the full platform: fingerprinting, scoring, behavioral analytics, identity graph, alerts, escalations, webhooks. See the pricing page for details.

Frequently Asked Questions

Q: What is fraud detection?

A: Fraud detection is the practice of identifying suspicious or abusive activity in an application before it causes harm. In a SaaS context, that usually means catching free trial abuse, fake signups, bot traffic, duplicate accounts, content spam, and credential stuffing: patterns that don't match the behavior of legitimate customers. Modern fraud detection combines device intelligence, identity attributes, network signals, and behavioral patterns into continuously updated risk scores, then routes those scores to dashboards, alerts, or directly into the application for automated response.

Q: Is Dregs AI fraud detection?

A: Dregs is AI-assisted, but not pure AI. The fraud detection pipeline combines deterministic analyzers (regex, similarity functions, fingerprint matching), statistical baselining against your real-customer behavior, and AI assistance from large language models for tasks like analyzer review and identity narratives on the Advanced plan. The choice is deliberate: AI is genuinely useful for ambiguous cases and natural-language explanations, but most fraud signals are clearer and more auditable when computed deterministically. Every Dregs score traces back to specific observations you can read and verify, which is harder to do with a pure ML black box.

Q: How is Dregs different from enterprise fraud detection software?

A: Enterprise fraud detection vendors like Sift, Riskified, and SAS were built for large e-commerce, fintech, and banking workloads: billion-dollar transaction streams with custom rule engines, integration teams, and procurement processes that can take months. Dregs is built for SaaS teams that need fraud detection running this week, billed against active identities rather than transactions, integrated through one tracking script and a webhook. The capabilities overlap; the operational shape doesn't.

Q: Do I need to write rules to use Dregs?

A: No. Dregs ships with 22 built-in AI-assisted analyzers covering bot detection, device intelligence, identity authenticity, behavioral patterns, and shared-account detection, all running on every user automatically with no configuration. You can add your own custom rules as JavaScript analyzers on the Advanced plan if you want to encode domain-specific signals, but the out-of-the-box pipeline produces useful fraud scores from the first event. Most teams never need to write a custom analyzer.

Q: What kinds of fraud does Dregs detect?

A: The patterns that hit SaaS applications: free trial abuse (one person cycling through trials with disposable emails), duplicate accounts (one person, multiple accounts to game limits or referrals), bot signups and credential stuffing, fake signups with junk profile data, content spam, and shared-account abuse. Dregs is not built for payment fraud (chargeback prevention, card testing); for that, your payment processor's fraud tools are a better fit.

Q: How long does fraud detection take to integrate?

A: With Dregs: minutes to hours, not weeks. Add the tracking script to your application, call identify and track at the points your users sign up and act, and Dregs starts producing fraud scores moments after the first events arrive. The first useful integration is just the tracking script; everything beyond that (webhooks, escalation rules, custom badges) is incremental and optional. Most teams have a working fraud detection pipeline running on the same day they sign up.

Q: Can automated fraud detection replace manual review?

A: For routine cases, yes. The point of automated fraud scoring is to handle the obvious cases without human attention so your team can focus on the edge cases. Dregs's webhook integration lets your application act on scores automatically: shadow ban a freeloader, gate a feature for low-Humanity accounts, require extra verification for users with low Uniqueness scores. Manual review is still useful for ambiguous cases and for tuning your rules over time, but it shouldn't be your primary defense.

AI-assisted fraud detection that runs this week, not next quarter.

Drop the Dregs tracking script into your application and start catching free trial abuse, duplicate accounts, and bot signups from the very first event.

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