battles / Fraud
Accertify vs NoFraud
Accertify ($5,000/mo/mo, vibe code 3/10) vs NoFraud ($250/mo/mo, vibe code 5/10). NoFraud is the easier one to rebuild yourself — here is what you lose either way.
Fraud
$5,000/mo/mo
- MVP
- 2 weeks
- Full replacement
- 12-24 months, with missing cross-merchant threat data and lack of chargeback dispute automation
Fraud
$250/mo/mo
- MVP
- 2-3 weeks
- Full replacement
- 12-18 months
easier to rebuild
get the build prompt →price gap / year
$57,000/mo
running both / year
$63,000/mo
our call
Start with NoFraud — highest vibe code, weakest moat.
Accertify
Building a custom rules engine with an AI assistant takes a couple of weeks, but Accertify's moat is its vast cross-merchant transaction database and hardware fingerprint network. An isolated self-hosted application has no access to global stolen card data or collective threat intelligence. You can easily build the admin workflow, but your fraud engine will be blind to external threat actors.
you can rebuild
- Manual order review queue and analyst decision workflow UI
- Custom merchant-defined risk scoring rules engine
- Basic velocity checking and IP geolocation distance verification
- Webhook triggers to hold or release risky orders in ecommerce backends
- Basic analytics dashboards tracking approved versus flagged transactions
what you lose
- Cross-merchant global transaction and stolen card threat network
- Automated chargeback representment and dispute resolution pipelines
- Hardware-level mobile and web browser device fingerprinting SDKs
- Dedicated 24/7 enterprise threat analyst team and performance SLAs
- Pre-built processor and card brand network dispute connections
real moats
- Proprietary cross-merchant fraud signals accumulated across billions of checkout events
- Direct integrations with issuing banks and chargeback networks
- Contractual performance SLAs and financial chargeback protection options
open source escape hatches
- Zeek BSD-3-Clause
- PostHog MIT
- Fraud-Detection-System (Community) MIT
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.
open source escape hatches
- FraudLabs / open rules engines (Drools) Apache-2.0
- Feedzai alternatives — River BSD-3
- MaxMind minFraud open clients Apache-2.0
Questions people ask
Which is easier to rebuild with AI, Accertify or NoFraud?
NoFraud. It scores 5/10 on vibe code with a moat of 7/10, so an AI-assisted MVP takes about 2-3 weeks and a full replacement about 12-18 months.
Which one costs less, Accertify or NoFraud?
NoFraud at $250/mo/mo for a typical mid-market store. The gap between the two is about $57,000/mo a year.
What do I lose if I replace Accertify?
Cross-merchant global transaction and stolen card threat network Automated chargeback representment and dispute resolution pipelines Hardware-level mobile and web browser device fingerprinting SDKs
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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