AI Fraud Detection Systems Evolve to Reduce False Positives and Protect Legitimate Businesses

The Rising Cost of False Positives

Artificial intelligence systems designed to prevent fraud are increasingly causing collateral damage to legitimate businesses, according to industry reports. Sources indicate that companies operating in sectors like CBD, telehealth, gaming, crypto, and alternative finance frequently face account freezes and higher transaction fees despite operating legally. Analysts suggest that automated fraud systems often treat unfamiliar patterns as dangerous, leading to significant business disruptions.

The problem extends beyond small businesses, with even mainstream payment processors reportedly freezing accounts without explanation. According to reports from Fraud.com, false positives cost merchants approximately 2.8% of their annual revenue. Recovery from being blacklisted can be challenging, as many platforms don’t provide recourse or reasons for such shutdowns.

Deepfake Threats and Defensive Evolution

The need for robust fraud prevention was highlighted in early 2024 when The Guardian reported that U.K. engineering firm Arup lost $25 million to a sophisticated deepfake scam. Fraudsters used AI-generated video and voice technology to impersonate company executives, convincing an employee to wire funds in what became one of the most expensive synthetic fraud incidents to date.

This incident demonstrated how fraud has evolved, but analysts suggest the defensive systems have created unintended consequences. Kirk Fredrickson, founder of compliance company 2Accept, explained that most fraud engines treat anything unfamiliar as dangerous. “We’ve seen companies lose accounts overnight for nothing more than a keyword in their product description,” he stated. “That kind of overreach doesn’t just hurt business; it undermines trust in the system.”

New Generation of AI Solutions

Companies are now developing more sophisticated AI systems that can better distinguish between actual fraud and legitimate business activity. According to industry reports, 2Accept uses onboarding models that monitor patterns across transactions, chargebacks, and merchant behavior to help businesses maintain good standing. The company’s systems reportedly reduce account termination risk by up to 60% and help thousands of merchants across CBD, telehealth, and fintech stay compliant.

Major financial players are also implementing advanced detection systems. Mastercard now uses Decision Intelligence Pro, an AI system analyzing 160 billion transactions annually in real time. The technology combines behavioral and device data to better distinguish fraud from legitimate activity.

Measurable Improvements in Accuracy

The push for more accurate fraud detection is yielding significant results across the industry. According to Business Insider, Riskified recently helped a U.S. ticketing platform recover $3 million in sales by deploying adaptive AI at checkout to reduce unnecessary blocks. HSBC also reported that its AI models reduced false positives by 60% while detecting two to four times more real fraud.

2Accept reports that merchants on its platform see up to 48% fewer chargebacks and benefit from partnerships with tier-1 acquiring banks. These improvements are particularly crucial in sectors like CBD or wellness, where up to 70% of merchants reportedly face closure within their first year.

Regulatory Push for Transparency

The industry is increasingly moving toward explainable AI and hybrid systems that blend automation with human review. According to Fredrickson, “The tools we build have to be explainable. It’s not enough to flag a transaction. You have to be able to say why and what can be done about it.”

This expectation of accountability is now being backed by legislation. The EU AI Act and frameworks like the Digital Operational Resilience Act are requiring that automated systems used in high-risk domains like fraud detection offer transparency and accountability by design. In the U.S., agencies like the Consumer Financial Protection Bureau are investigating whether financial institutions’ AI tools are unfairly limiting access to credit or financial services.

Balancing Security and Access

Experian’s recent report revealed that AI-powered fraud targeted 35% of U.K. businesses in Q1 alone, driving over half of companies to invest in tools that not only catch more fraud but avoid mistaking legitimate customers or companies for criminals.

Fredrickson believes the next phase of fraud prevention focuses on fairer systems rather than just tighter controls. “You can’t build trust with one hand and take it away with the other,” he said. “If AI is going to govern access to financial infrastructure, then it has to work for everyone, especially those trying to do things right.”

Modern fraud prevention is shifting from defining strict boundaries to comprehending the true nature of data. Instead of immediately cutting off businesses at the first sign of risk, today’s systems are beginning to pause, assess, and adapt. The evolving goal is not only to prevent fraud but to shield legitimate businesses from becoming collateral damage in the process.

References & Further Reading

This article draws from multiple authoritative sources. For more information, please consult:

• https://www.hsbc.com/news-and-views/views/hsbc-views/harnessing-the-power-of-ai-to-fight-financial-crime
• https://www.experianplc.com/newsroom/press-releases/2025/new-report-from-experian-reveals-surge-in-ai-driven-fraud-
• http://en.wikipedia.org/wiki/Electoral_fraud
• http://en.wikipedia.org/wiki/Artificial_intelligence
• http://en.wikipedia.org/wiki/Blacklisting
• http://en.wikipedia.org/wiki/Telehealth
• http://en.wikipedia.org/wiki/Central_business_district

This article aggregates information from publicly available sources. All trademarks and copyrights belong to their respective owners.

Note: Featured image is for illustrative purposes only and does not represent any specific product, service, or entity mentioned in this article.