Fraud prevention systems haven’t kept up with the market they serve.

Most legacy models were built for midmarket and large businesses - those with long histories, stable volume, and well-documented operations. But in today’s landscape, SMBs make up the majority of new merchant accounts on fintech platforms, marketplaces, and software platforms. And they operate very differently.

The Problem with Traditional Models

SMB behavior often mimics fraud on paper:

• Spikes in volume from weekend-only businesses
• New accounts with minimal digital footprint
• Seasonal or irregular activity patterns
  
  

To a static, rules-based system, these merchants raise red flags - even when they’re perfectly legitimate.

The result?
Risk teams are flooded with alerts that don’t reflect actual risk. And many of the real threats? They blend right in.

Too Many Alerts. Not Enough Context.

When fraud detection relies on predefined thresholds - chargeback rates, refund ratios, or transaction velocity it misses the bigger picture.

And because these systems don’t benchmark activity or adapt to platform-specific norms, they generate 70–80% false positives, according to early testing across our customers.

This means:

• Wasted time on low-risk accounts
• Alert fatigue
• Delays and misses in spotting real fraud
  

What’s Changing

Leading platforms are moving toward AI-powered infrastructure that goes beyond rules:

• Daily or real-time merchant-level scoring
• Behavioral pattern detection across time
• Benchmarking within the broader merchant portfolio
• Explainability behind every score
  
  

That’s exactly what we built with the Coris Intelligence Score - a model that adapts to your portfolio and surfaces risk with clarity, not noise.

Final Thoughts

Fraud is evolving and so should the tools used to stop it.
For platforms onboarding and monitoring SMBs at scale, traditional models fall short.

The future is adaptive, context-rich, and continuously improving.

👉 Read how Coris is helping risk teams manage fraud in the SMB space

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