Experian: AI, Ally and Threat for Banks

AI: A Double-Edged Sword for Financial Institutions

In its Future of Fraud Forecast 2026 report, Experian highlights a central issue: the artificial intelligence (AI) technology used by financial institutions for protection is also exploited by fraudsters. In 2024, according to the FTC, consumers lost over $12.5 billion due to fraud. Between 2024 and 2025, nearly 60% of businesses reported an increase in losses due to fraud, according to Experian's data.

The fraud prevention solutions developed by Experian helped avoid approximately $19 billion in losses globally in 2025. This figure illustrates the magnitude of the problem and demonstrates how defense against fraud now relies on AI to match the speed and autonomy of attacks.

The Challenge of Agentic AI Systems

Experian's report emphasizes the concept of machine-to-machine chaos, where agentic AI systems, designed to perform transactions autonomously, become indistinguishable from fraudulent bots. Fraudsters exploit these systems to carry out large-scale fraud, posing a significant challenge in terms of accountability. According to the report, while organizations strive to integrate AI agents capable of making independent decisions, fraudsters exploit these same systems to execute high-volume digital fraud at a scale and speed that no human operation could sustain.

Kathleen Peters, Director of Innovation for Fraud and Identity at Experian North America, emphasizes that technology makes fraud more sophisticated and harder to detect. She stresses the importance of using differentiated data and advanced analytics to strengthen defenses against fraud. Experian predicts that this will reach a tipping point in 2026, forcing substantial conversations within the industry around accountability and governance of agentic AI in commerce. Some organizations are already taking preventive measures. For example, Amazon has stated that it blocks third-party AI agents from navigating and transacting on its platform, citing security and privacy concerns.

Emerging Threats Identified

The report also identifies four other major threats for 2026:

• Deepfakes in Recruitment: Generative AI tools enable the creation of CVs and deepfake videos, facilitating the infiltration of fraudulent candidates into companies. According to the report, this could allow malicious actors to access internal systems.

• Website Cloning: AI facilitates the creation of replicas of legitimate sites, making their removal difficult and forcing fraud teams to adopt reactive strategies. Even after takedown requests are implemented, counterfeit domains continue to reappear.

• Emotionally Intelligent Bots: These bots can conduct complex scams without human intervention, making fraudulent interactions indistinguishable from authentic human interactions. They build trust over long periods, making them particularly dangerous.

• Smart Home Vulnerabilities: Connected devices create new entry points for fraudsters, who can access personal data and monitor home activity. Experian predicts that these devices will be increasingly exploited as the connected home becomes an integral part of daily financial behavior.

Responses from Financial Institutions

According to Experian's Perceptions of AI Report, 84% of decision-makers view AI as a strategic priority, and 89% believe it will play a crucial role in the loan lifecycle. However, 73% of respondents are concerned about regulations surrounding AI, and 65% identify data readiness as a major challenge. Governance is where institutions face difficulties. According to the same report, 73% of respondents are worried about the regulatory environment surrounding AI, and 65% identify AI-ready data as one of their biggest deployment challenges.

Experian's AI Assistant for Model Risk Management aims to automate model documentation, addressing the growing regulatory requirements. According to an Experian study from 2025 involving over 500 global financial institutions, 67% struggle to meet their country's regulatory requirements, 79% report more frequent supervisory communications from regulators than a year ago, and 60% still use manual compliance processes. Vijay Mehta, EVP of Global Solutions and Analytics at Experian Software Solutions, described the challenge the product addresses: “The speed of data analysis and model development enabled by AI creates unprecedented business opportunities for financial institutions, but it comes with a challenge: global regulations that require time-consuming documentation. Experian's AI Assistant for Model Risk Management helps address this labor-intensive and resource-heavy requirement by automating end-to-end model documentation.”

The Importance of Data Quality

Data quality is crucial for the reliability of AI, as highlighted in the Perceptions of AI Report. 65% of decision-makers see data preparation as a major challenge, and data quality is the most critical factor for trust in AI providers. This requirement for reliable data is essential for financial institutions, particularly for credit decisions, fraud detection, and regulatory compliance. This constraint is shared by giants like IBM and Salesforce, who also emphasize the importance of robust data for informed and reliable decision-making. This is not a coincidence in messaging. It reflects a constraint faced by financial services institutions as they transition from pilots to production credit decisions, fraud detection, and regulatory reporting; functions where explainability and auditability are not optional.