Fraud and bot prevention operations portal

For

Product and risk teams running sign-up, login and payment flows for apps and websites

Solves

Fraudulent users, bots and suspicious transactions pass through sign-up, login and payment flows, and evidence is scattered across several rented tools.

Delivers

Reviewer-approved allow, block or challenge decisions linked to case evidence

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce fraudulent account and transaction losses while keeping legitimate users moving.

1. Collect device and browser signals to identify and track devices.
2. Identify and block automated bot activity.
3. Evaluate user or transaction risk instantly as events occur.
4. Use machine learning models to spot suspicious patterns and zero-day threats.
5. Set custom rules that determine allow, block or challenge actions.
6. Apply fine-grained rate limits to specific devices or groups.
7. Build user management and automation without coding.
8. Detect when multiple accounts are linked to the same user or device.
9. Identify users hiding behind proxies or VPNs.
10. Analyze user behavior signals to assess authenticity.
11. Detect fake identities and temporary emails using face and email intelligence.
12. Find duplicate or near-duplicate transactions using vector search and fuzzy matching.
13. Process large transaction files in chunks for scalability.
14. Provide clear explanations for alerts to help investigators triage incidents.
15. Offer API keys, webhooks and asynchronous workers for automation and workflow integration.
16. Provide a full dashboard for monitoring alerts and system activity.
17. Integrate with existing authentication systems without disrupting user flows.

Everything these tools do, in one app

• Device fingerprinting Collects device and browser signals to identify and track devices.Found in Stytch Fraud & Risk Prevention, Verisoul
• Bot detection Identifies and blocks automated bot activity.Found in Stytch Fraud & Risk Prevention, Verisoul
• Real-time risk assessment Evaluates user or transaction risk instantly as events occur.Found in Verisoul, FraudLens AI
• Machine learning detection Uses machine learning models to spot suspicious patterns and zero-day threats.Found in Stytch Fraud & Risk Prevention, Verisoul, FraudLens AI
• Configurable rules engine Allows users to set custom rules that determine allow, block, or challenge actions.Found in Stytch Fraud & Risk Prevention
• Intelligent rate limiting Applies fine-grained rate limits to specific devices or groups to reduce collateral impact.Found in Stytch Fraud & Risk Prevention
• No-code workflows Enables user management and automation without coding.Found in Verisoul
• Account linking detection Detects when multiple accounts are linked to the same user or device.Found in Verisoul
• Proxy and VPN detection Identifies users hiding behind proxies or VPNs.Found in Verisoul
• Behavioral analysis Analyzes user behavior signals to assess authenticity.Found in Verisoul
• Fake identity detection Detects fake identities and temporary emails using face and email intelligence.Found in Verisoul
• Duplicate detection Finds duplicate or near-duplicate transactions using vector search and fuzzy matching.Found in FraudLens AI
• Large file processing Processes large transaction files in chunks for scalability.Found in FraudLens AI
• Human-readable explanations Provides clear explanations for alerts to help investigators triage incidents.Found in FraudLens AI
• API and webhook integration Offers API keys, webhooks, and asynchronous workers for automation and workflow integration.Found in FraudLens AI
• Dashboard monitoring Provides a full dashboard for monitoring alerts and system activity.Found in FraudLens AI
• Authentication integration Integrates with existing authentication systems without disrupting user flows.Found in Stytch Fraud & Risk Prevention

What goes in, what comes out

The workflow

1. In
   Start with

   Device signals, behavioral events, transaction records and identity checks

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect device signals

4. 3

   Behavioral events

5. 4

   Transaction records and identity checks

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved allow, block or challenge decisions linked to case evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed event schema and approved signal set; final fraud decisions and account actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Risk policy and data sources, Live alert triage, Case review and decision log. Use a queue of open alerts, a large central case view, and a right-hand panel for signals, linked accounts and reviewer notes. Let users compare decisions against policy versions side by side. Display open, challenged, blocked and cleared states. Provide a client-facing summary link with evidence anchored to the relevant event. Make the task-specific outcome reviewer-approved allow, block or challenge decisions linked to case evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, signal source versions, reviewer comments, decision states, usage allowances, case limits, export history and a rights record for supplied data. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned authentication systems, event streams and transaction databases. Cloud storage, identity providers and alerting destinations. Start with file exchange and validate destination specifications before promising direct enforcement. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: collect device and browser signals to identify and track devices; identify and block automated bot activity. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product and risk teams running sign-up, login and payment flows for apps and websites use it to solve "fraudulent users, bots and suspicious transactions pass through sign-up, login and payment flows, and evidence is scattered across several rented tools"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Confirmed fraud loss per reviewed case and false-positive rate on legitimate users.
4. Measure, then decide. Track confirmed fraud loss per reviewed case and false-positive rate on legitimate users; accepted-decision rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed event schema and approved signal set; final fraud decisions and account actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: collect device and browser signals to identify and track devices; identify and block automated bot activity. Support the third module with operator review: evaluate user or transaction risk instantly as events occur. Include source references, corrections, basic organization access, decision states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved allow, block or challenge decisions linked to case evidence. Retain the explicit scope boundary: One fixed event schema and approved signal set; final fraud decisions and account actions remain human.

What the build depends on. Event upload and preview, asynchronous scoring jobs, editable decision history, reviewer access and tested export formats. High-fidelity risk scoring requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed event schema and approved signal set; final fraud decisions and account actions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined event package. Offer a monthly production allowance after repeat demand. Quote complex multi-region or high-volume deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved allow, block or challenge decisions linked to case evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce fraudulent account and transaction losses while keeping legitimate users moving. Demonstrate a concrete reviewer-approved allow, block or challenge decisions linked to case evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and risk teams running sign-up, login and payment flows for apps and websites professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved allow, block or challenge decisions linked to case evidence from a small authorized input set, with a transparent calculation of confirmed fraud loss per reviewed case and false-positive rate on legitimate users and no promised savings.

The first 30 days

1. Week 1: interview five product and risk teams running sign-up, login and payment flows for apps and websites and inspect a recent example of fraudulent users, bots and suspicious transactions pass through sign-up, login and payment flows, and evidence is scattered across several rented tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure confirmed fraud loss per reviewed case and false-positive rate on legitimate users, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Confirmed fraud loss per reviewed case and false-positive rate on legitimate users. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Confirmed fraud loss per reviewed case and false-positive rate on legitimate users; accepted-decision rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved allow, block or challenge decisions linked to case evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved rules, signal mappings and review examples, together with reliable delivery for a narrow risk niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and risk teams running sign-up, login and payment flows for apps and websites. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Stytch Fraud & Risk Prevention, Verisoul and FraudLens AI, plus manual review queues and generic analytics tools. Compare this product with the buyer's present method on confirmed fraud loss per reviewed case and false-positive rate on legitimate users. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Signal processing, model inference, storage, reviewer hours, client revision rounds and licensed data sources. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved allow, block or challenge decisions linked to case evidence. Track cost per accepted decision, including correction work, unsuccessful cases and support.

Preserve user privacy, source attribution, evidence accuracy and data permissions. Named reviewers approve account actions and enforcement scope. One fixed event schema and approved signal set; final fraud decisions and account actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.