battles / Fraud

FraudNet vs NoFraud

FraudNet ($899/mo/mo, vibe code 5/10) vs NoFraud ($250/mo/mo, vibe code 5/10). FraudNet is the easier one to rebuild yourself — here is what you lose either way.

Fraud

$899/mo/mo

Vibe code5/10
Moat7/10
MVP
1 week
Full replacement
Never (Requires cross-merchant global transaction network and device intelligence)

easier to rebuild

get the build prompt

Fraud

$250/mo/mo

Vibe code5/10
Moat7/10
MVP
2-3 weeks
Full replacement
12-18 months
get the build prompt

price gap / year

$7,788/mo

running both / year

$13,788/mo

our call

Start with FraudNet — highest vibe code, weakest moat.

FraudNet

Building a rule engine with MaxMind or Radar APIs takes a weekend, but you cannot replicate FraudNet's global network of shared fraud signals and device fingerprint intelligence. Without global transaction data across thousands of stores, custom AI models hallucinate or generate false positives that destroy legitimate conversion.

you can rebuild

  • Rule-based transaction scoring engine (e.g., velocity checks, threshold limits)
  • Order risk review dashboard and manual approval workflow
  • Blacklisting and whitelisting by email, IP, shipping address, or BIN
  • Basic geo-IP mismatch detection and address verification triggers
  • Webhook alerts and automated order holding in Shopify or WooCommerce

what you lose

  • Cross-merchant network intelligence tracking fraud rings across thousands of stores
  • Proprietary device fingerprinting heuristics bypassing browser privacy protections
  • Chargeback guarantee coverage options that shift financial liability away from you
  • Real-time proxy, VPN, TOR, and residential botnet IP reputation databases
  • Continuous machine learning model retrain cycles backed by global dispute feeds

real moats

  • Cross-merchant consortium data linking malicious devices and emails across clients
  • Proprietary device fingerprinting scripts resilient to modern privacy browsers
  • Chargeback liability shifting and financial guarantee models

open source escape hatches

NoFraud

NoFraud (Wyllo) is fundamentally an insurance product wrapped in an API. While the order scoring and Shopify tagging features can be cloned with standard LLM tools in a couple of weeks, you cannot write code that underwrites thousands of dollars in credit card chargebacks.

you can rebuild

  • Automated order risk scoring based on standard rules (IP, distance, proxy, email).
  • Shopify order tagging and automatic cancellation API triggers.
  • Manual order review dashboard with signal visualizations.
  • Basic velocity and heuristic-based risk engine.

what you lose

  • 100% financial reimbursement guarantee on fraudulent chargebacks passed by the engine.
  • 24/7 human analyst team conducting manual order reviews on borderline transactions.
  • Network-level risk detection trained on shared cross-merchant fraud signals.
  • Direct chargeback dispute handling and representment operations.

real moats

  • Balance sheet capital to guarantee chargeback reimbursements at scale.
  • Operational infrastructure of human fraud analysts performing 24/7 manual reviews.
  • Consolidated network intelligence across thousands of high-volume e-commerce storefronts.

Questions people ask

Which is easier to rebuild with AI, FraudNet or NoFraud?

FraudNet. It scores 5/10 on vibe code with a moat of 7/10, so an AI-assisted MVP takes about 1 week and a full replacement about Never (Requires cross-merchant global transaction network and device intelligence).

Which one costs less, FraudNet or NoFraud?

NoFraud at $250/mo/mo for a typical mid-market store. The gap between the two is about $7,788/mo a year.

What do I lose if I replace FraudNet?

Cross-merchant network intelligence tracking fraud rings across thousands of stores Proprietary device fingerprinting heuristics bypassing browser privacy protections Chargeback guarantee coverage options that shift financial liability away from you

What do I lose if I replace NoFraud?

100% financial reimbursement guarantee on fraudulent chargebacks passed by the engine. 24/7 human analyst team conducting manual order reviews on borderline transactions. Network-level risk detection trained on shared cross-merchant fraud signals.

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