Business

Responsible AI emerges as a crucial weapon in ransomware and scam defences

Tuesday, 25 November 2025 5:35AM UTC

As cybercriminals target financial sectors with increasingly sophisticated scams, responsible adoption of AI and collaborative governance are becoming essential for effective fraud defence, says experts shaping the future of cybersecurity.

As the financial sector contends with a rapidly evolving landscape of cybercrime, the rising adoption of artificial intelligence (AI) is shaping new strategies for fraud prevention. According to the Federal Trade Commission, consumers lost more than $12.5 billion to fraud in 2024, a stark 25% increase over the previous year, underscoring the growing urgency to enhance security frameworks amid increasingly sophisticated scams. Investment frauds, imposter scams, and online shopping deceptions accounted for significant portions of these losses, revealing the urgent need for multi-layered and intelligent fraud defenses.

In this context, financial institutions are turning to AI technologies, including generative and agentic AI systems, to not only detect but proactively prevent such fraudulent activities. However, the deployment of AI introduces complex challenges around data privacy, ethical use, and responsible governance. The World Economic Forum’s 2025 white paper highlights these intricacies, emphasising the crucial balance between leveraging AI to enhance operational security and protecting the privacy and trust of consumers. Establishing clear frameworks for responsible AI adoption is essential to foster both effectiveness in fraud prevention and adherence to ethical standards.

JoAnn Stonier, a Fellow of Data and AI at Mastercard and an expert in privacy and data governance, provides valuable insights into how financial institutions can navigate this balance. Her experience leading Mastercard's enterprise-wide data governance and analytics underscores the value of adopting mature, deterministic AI systems over chasing the hype of emerging technologies. Mastercard’s 17 years of using data and analytics to monitor global transactions demonstrate how AI can significantly improve real-time fraud detection by analysing spending patterns rather than individual identities.

Stonier explains that responsible AI in fraud prevention focuses on minimal data use, such as transaction date, time, location, and purchase amount, enabling pattern recognition without compromising individual privacy. This approach, known as data minimization, ensures that AI systems detect suspicious behaviour without collecting more personal information than necessary, maintaining trust and compliance with privacy regulations. Such advancements mean fewer false positives, improving customer experience by recognising legitimate spending sprees across multiple locations, for instance, without unnecessary interruption.

Beyond foundational AI, emerging “agent-ish” AI systems , precursors to fully autonomous AI , promise future enhancements in fraud detection capabilities. However, Stonier stresses that human oversight remains essential as these systems increase in complexity. Effective AI deployment demands a "team-sport" governance approach that involves collaboration among product, innovation, and risk teams, all aligned by a clear purpose and iterative evaluation processes. This collective responsibility ensures a balanced management of risks, from model bias to data security, while continuously adapting to new challenges.

The broader ecosystem echoes this call for structured and balanced AI integration. At the 2025 World Economic Forum in Davos, NTT DATA CEO Abhijit Dubey advocated for international standards in AI regulation to address risks linked to intellectual property, energy use, and misinformation, suggesting that global coherence is key to responsibly harnessing the technology's potential. Meanwhile, the FBI’s 2025 data indicating a 33% increase in global cybercrime losses to over $16 billion reveals that the fight against fraud is far from over and that technology alone cannot suffice without human governance and cooperation.

Regional data, such as that from Texas and Georgia, reflects these national trends, showing significant year-over-year increases in fraud losses and varied scam tactics targeting different demographics. These findings reinforce the necessity for financial institutions to adopt robust, adaptable AI systems capable of responding to diverse and evolving threats, always considering ethical implications and operational transparency.

In summary, as financial organisations face escalating fraud threats, the integration of responsible AI, prioritising mature technologies, data minimization, and collaborative governance, offers a promising pathway. JoAnn Stonier's experience at Mastercard illustrates that the future of fraud prevention depends not just on advanced algorithms, but on maintaining human oversight, fostering a culture of trust, and ensuring AI innovations are ethically aligned and practically effective in safeguarding customers and the broader financial ecosystem.

📌 Reference Map:

^[1] (Emerj) - Paragraphs 1, 3, 4, 5, 6, 7, 8
^[2] (World Economic Forum) - Paragraph 2
^[3] (FTC) - Paragraph 1
^[4] (Reuters, WEF Davos) - Paragraph 7
^[5] (FBI Report) - Paragraph 7
^[6] (Axios, Texas) - Paragraph 7
^[7] (Axios, Georgia) - Paragraph 7

Source: Noah Wire Services

More on this

https://emerj.com/how-financial-institutions-can-prepare-for-the-future-of-fraud-with-responsible-ai-deployments-joann-stonier-mastercard/ - Please view link - unable to able to access data
https://www.weforum.org/reports/ai-adoption-and-data-privacy-2025 - The World Economic Forum's 2025 white paper discusses the complexities introduced by AI adoption, particularly concerning data privacy and responsible deployment. It highlights the need for financial institutions to balance the benefits of AI in enhancing security and streamlining operations with the challenges of ensuring data privacy and ethical AI use. The report emphasizes the importance of developing frameworks that address these issues to foster trust and effectiveness in AI applications within the financial sector.
https://www.ftc.gov/news-events/press-releases/2025/03/new-ftc-data-show-big-jump-reported-losses-fraud-125-billion-2024 - The Federal Trade Commission's 2025 report reveals that consumers reported losing over $12.5 billion to fraud in 2024, marking a 25% increase from the previous year. The data indicates a significant rise in the percentage of individuals reporting financial losses due to scams, from 27% in 2023 to 38% in 2024. Investment scams accounted for the highest losses at $5.7 billion, followed by imposter scams at $2.95 billion. The report underscores the urgent need for robust, multi-layered security approaches to combat fraud.
https://www.reuters.com/technology/artificial-intelligence/ntt-data-boss-calls-global-standards-ai-regulation-davos-2025-01-24/ - At the 2025 World Economic Forum in Davos, NTT DATA CEO Abhijit Dubey advocated for global standards in AI regulation to mitigate risks associated with rapidly evolving technology. He emphasized the necessity for consistent international regulations, particularly in areas like intellectual property protection, energy efficiency, and combating deepfakes. Dubey highlighted that effective AI deployment requires integrating AI strategies with business objectives and ensuring workforce readiness and data infrastructure preparedness.
https://www.reuters.com/world/us/fbi-says-cybercrime-costs-rose-least-16-billion-2024-2025-04-23/ - The FBI's 2025 report indicates that cybercrime resulted in over $16 billion in global losses in 2024, a 33% increase from the previous year. The surge was primarily driven by low-tech scams such as investment frauds and phishing attacks. Other costly scams included tech support and romance frauds, collectively accounting for hundreds of millions in losses. The data, collected from nearly 860,000 reports, likely underestimates total losses, especially from ransomware attacks, with most complaints originating from the United States.
https://www.axios.com/local/san-antonio/2025/03/18/how-much-money-fraud-scams-san-antonio-texas - In 2024, Texans reported nearly $900 million in fraud losses, a significant increase of over 35% from the $662 million reported in 2023, according to federal data. This mirrors the national trend, with Americans overall reporting more than $12.5 billion lost to fraud, a 25% rise from the previous year. In Texas, 162,101 fraud cases were reported, with imposter scams and online shopping fraud being the most common types. The San Antonio-New Braunfels metro area also saw imposter scams as the predominant form of fraud, accounting for over 4,700 reports.
https://www.axios.com/local/atlanta/2025/03/17/georgia-romance-scams-fraud-money-losses - In 2024, scammers in Georgia defrauded individuals of over $25 million, with romance scams accounting for a significant portion of the losses, according to FTC reports. While online shopping scams led in volume with 9,908 reports and $7.7 million in losses, 1,227 people reported being deceived by scammers under romantic pretenses. Interestingly, fraud affected nearly half (49%) of Georgians aged 19 and younger who reported scams, compared to only 25% in the 70–79 age group. Nationwide, Americans lost more than $12.5 billion to fraud—a 25% increase from 2023—with investment and impostor scams causing the greatest financial harm.

Noah Fact Check Pro

The draft above was created using the information available at the time the story first emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed below. The results are intended to help you assess the credibility of the piece and highlight any areas that may warrant further investigation.

Freshness check

Score: 8

Notes: The narrative is recent, published on November 24, 2025. The earliest known publication date of substantially similar content is November 24, 2025. The narrative is based on an interview with JoAnn Stonier, a Fellow at Mastercard, which typically warrants a high freshness score. No discrepancies in figures, dates, or quotes were found. No earlier versions show different information. The narrative includes updated data, such as the FTC's report of consumers losing more than $12.5 billion to fraud in 2024, a 25% increase over the previous year. This update justifies a higher freshness score but should still be flagged. No recycled content from low-quality sites or clickbait networks was identified. No republished content across such sites was found. No earlier versions show different figures, dates, or quotes. No content similar to this narrative appeared more than 7 days earlier. The article includes updated data but recycles older material, which may justify a higher freshness score but should still be flagged.

Quotes check

Score: 9

Notes: The narrative includes direct quotes from JoAnn Stonier. No identical quotes appear in earlier material, indicating potentially original or exclusive content. No variations in quote wording were found.

Source reliability

Score: 7

Notes: The narrative originates from Emerj, a platform focusing on AI in business. While Emerj is a reputable source, it is not as widely recognised as major outlets like the Financial Times or Reuters. JoAnn Stonier is a Fellow at Mastercard, a reputable organisation. No unverifiable entities or fabricated information were identified.

Plausability check

Score: 8

Notes: The narrative discusses the use of AI in fraud prevention, a topic covered by reputable organisations like Mastercard. Claims about the FTC's report of consumers losing more than $12.5 billion to fraud in 2024 align with available data. The language and tone are consistent with the region and topic. No excessive or off-topic detail unrelated to the claim was found. The tone is formal and appropriate for the subject matter.

Overall assessment

Verdict (FAIL, OPEN, PASS): PASS

Confidence (LOW, MEDIUM, HIGH): HIGH

Summary: The narrative is recent, includes original quotes, originates from a reputable source, and presents plausible claims with appropriate language and tone. No significant issues were identified.

AI
Cybercrime
Fraud prevention