Fraud doesn’t wait. Your infrastructure shouldn’t either.

With over $40B in global payment fraud losses in 2023, risk teams can no longer rely on static rules, manual checks, or siloed systems. Today’s fraud is adaptive, multi-vector, and fast. The only way to stay ahead is to build infrastructure that learns and responds faster than the threats.

Below are five critical capabilities modern platforms are adopting to rethink how risk is detected, assessed, and managed across the merchant lifecycle.

1. Merchant Risk Infrastructure That Understands Behavior

Legacy KYB verifies existence. Modern risk systems evaluate behavior.

Modern fraud doesn’t hide in fake names – it hides in real businesses behaving badly. Intelligent platforms now combine signals from payment processors, public registries, and behavioral logs to score merchants contextually – before and after onboarding.

Unlocks:

• Real-time, unified merchant profiles
• Thin file detection, template site flagging, synthetic identity correlation
• Risk thresholds that adapt by vertical and signal strength
  

Example: Identify a newly registered merchant mimicking past fraud ring behavior – even if all documents pass.

2. AI-Led Underwriting That Doesn’t Sacrifice Speed for Accuracy

Underwriting can’t scale if it’s manual.

AI-powered underwriting classifies risk based on live data, not static playbooks. These engines assign dynamic tiers, trigger workflows, and route exceptions to review – without flooding analysts with noise.

Delivers:

• Instant risk stratification
• Auto-approval for low-risk merchants
• Escalation logic for edge cases

Move fast, with intent.

3. Real-Time Transaction Monitoring That Flags What Matters

You don’t need more alerts. You need better ones.

The best monitoring platforms don’t just ingest transaction data – they interpret it. Behavioural models detect velocity spikes, refund surges, and pattern shifts, surfacing the anomalies that signal real threats.

Key features:

• Chargeback-aware payout control
• Custom, adaptive trigger thresholds
• Real-time response, not daily summaries

Example: Intercept a payout when a merchant’s refund rate increases 3x in 24 hours.

4. Predictive Fraud Models That Adapt With Every Signal

Static rules catch yesterday’s fraud. Predictive systems catch tomorrow’s.

Modern risk models triangulate signals across merchants, payers, and behaviors to calculate the probability of loss – before it happens. These systems self-tune with every data point, improving accuracy over time.

Enables:

• Risk scoring for transactions, payouts, and behavior
• Friendly fraud and ACH fraud detection
• Up to 95% reduction in false positives (2024 benchmarks)

Smarter systems = more signal, less noise.

5. Infrastructure That Connects Signals Across the Risk Lifecycle

Siloed tools create gaps. Integrated systems close them.

Managing merchant risk isn’t a single event – it’s an ongoing process that spans onboarding, underwriting, monitoring, and fraud detection. When each function lives in a separate tool, risk signals get delayed or lost.

Modern platforms are moving toward unified infrastructure – where all risk-relevant data is connected and continuously updated.

Why this matters:

• Risk profiles are enriched by both onboarding data and transaction behavior
• Escalations and decisions are informed by full-context history
• Teams don’t waste time reconciling data across multiple systems

This shift isn’t about “more features” – it’s about building intelligence that travels with every merchant touchpoint. The result: faster action, fewer surprises, and better long-term risk posture.

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Final Thought: Risk Isn’t a Feature. It’s providing assurance.

Risk management isn’t about only catching fraud at the edge. It’s about enabling the vast majority of good customers to conduct business – with assurance.

Coris is helping risk teams move from fragmented reviews to intelligent infrastructure that scales with them.

Want to rethink your approach to merchant risk?‍

Request a demo

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