AI Tools Fuel Banking Fraud Surge, Industry Takes Action

Artificial intelligence (AI) has revolutionized the financial sector, enhancing operational efficiencies while simultaneously driving a sharp increase in fraud. Techniques such as phishing, deepfakes, and synthetic identities have become more prevalent, complicating the battle against fraud. According to The Global State of Fraud and Identity 2024 report by LexisNexis Risk Solutions, the complexity and frequency of banking fraud are surging.

In Mexico, financial fraud attempts in the banking sector rose by a staggering 700% in 2023, according to Trully, a financial fraud bureau. Fraudsters are increasingly leveraging AI to create synthetic identities, which makes detection significantly harder. “Fraud is no longer just about stolen identities; AI is being used to fabricate entirely new ones, complicating the task of identifying fraudulent activities,” said Fernando Paulin, CEO, Trully.

Globally, LexisNexis data shows a 19% increase in fraud attacks from 2023 to 2024. One emerging trend is the use of “money mules”—accounts used to launder illicit funds tied to identity theft, drug trafficking, and even terrorism. Yet fewer than 10% of these individuals are identified, and only 1% face legal consequences.

“With technology, banks can implement transactional fraud monitoring to detect and stop irregular activities in real-time,” said Juan Pablo Jiménez Isaza, Sales Director for Latin America, Lynx Tech. Despite these tools, LexisNexis found that only 60% of organizations have fraud prevention measures across all customer channels, and just 27% participate in data-sharing networks.

On the positive side, AI has proven effective in combating fraud. For instance, synthetic identities—which are 20 times more likely to surface across multiple credit applications in a short time—can be identified using AI-powered tools. Financial institutions adopting these solutions report saving millions in fraud losses each month.

In Peru, financial institutions have successfully implemented predictive analytics to enhance fraud detection and improve credit risk assessments. According to CANVIA, leveraging non-traditional data, such as digital behavior and social media activity, has allowed institutions to identify risks overlooked by traditional models.

“Analyzing large data sets with AI and machine learning captures complex patterns and relationships that conventional models cannot detect,” said Sandra Vásquez, Account Manager,  CANVIA’s financial sector. This technology has also reduced credit approval times by 20%, improving customer experience and expediting credit delivery, especially for small businesses. Globally, predictive analytics has improved fraud detection rates by 30%.