Fraud losses keep climbing. The FBI’s Internet Crime Complaint Center (IC3) reported over $16 billion in internet crime losses in 2024, up 33% from the previous year [1]. For fintechs, the bigger issue isn’t only fraud itself—it’s the false positives and slow, manual fraud rule updates that drain time, money, and customer trust.

Traditional static fraud rules simply can’t keep pace. Adjusting thresholds or adding a new rule can take weeks or even months. In the meantime, fraud evolves daily, leaving risk teams either over-blocking good customers or under-protecting against real threats.

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Why Static Rules Fail

Legacy fraud detection relies on pre-set rules and manual review. That creates three major problems:

• High false positives: Many financial institutions report false positive rates as high as 95% [2].
  
• Operational drag: Global compliance spending reached $274B in 2024, with much of it tied to manual investigations [3].
  
• Customer impact: 75% of ecommerce leaders say fraud checks cut into conversion rates [4].
  

False positives and slow reviews don’t just hurt efficiency—they erode trust and margins.

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The Rising Threat of Synthetic Identity Fraud

One of the fastest-growing schemes is synthetic identity fraud—where fraudsters create fake identities by blending real and fabricated data. It’s already one of the most costly fraud types, with the Federal Reserve noting it as the fastest-growing financial crime in the U.S. [5]. Static fraud rules are ineffective here, since patterns constantly change and don’t fit rigid models.

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Why AI Changes the Game

AI-driven fraud detection overcomes these limitations by learning from data in real time. Key advantages include:

• Rapid rule creation: Build and deploy new fraud rules in minutes, not months.
  
• Continuous monitoring: AI adapts as fraud patterns shift, reducing reliance on static thresholds.
  
• Smarter scoring: Risk models evaluate context across transactions, merchants, and platforms simultaneously.
  
• Fewer false positives: AI-native systems lower manual review rates while improving fraud catch accuracy [6].

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The Coris Approach

Coris was built for AI-native merchant risk management, combining fraud detection, merchant profiling, and monitoring into one platform:

• Fraud Rule Engine: Launch or update fraud rules in minutes with AI assistance.
  
• Real-Time Monitoring: Continuous oversight reduces blind spots and flags risk instantly.
  
• Integrated Merchant Intelligence: Data from multiple processors, web sources, and partners in one view.
  
• Human + AI Collaboration: Analysts can test, tune, and trust AI-driven rules with transparent workflows.
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Proof in Action

Fintechs using Coris report:

• 70% fewer manual reviews, freeing analysts for higher-value cases.
  
• Faster onboarding, with automated scoring cutting approval times.
  
• Reduced false positives, improving both efficiency and customer experience.

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Rethinking ROI

Fraud prevention is no longer just about blocking losses. With AI-native platforms like Coris, ROI also comes from:

• Lower compliance costs
• Fewer wasted analyst hours
  Faster onboarding → higher revenue
• Better customer trust and retention

Static fraud defenses can’t deliver this. AI-native systems can.

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The Way Forward

Fraud is growing more complex by the day. Manual rules and siloed tools leave fintechs vulnerable. Coris provides the infrastructure to fight fraud with speed, accuracy, and scale—so risk teams can build rules in minutes, not months, and keep fraudsters out while keeping good customers in.

Ready to rethink your fraud defenses? Book a demo and see how Coris helps fintechs cut false positives, move faster, and protect their bottom line.