Closing the AI Data Gap: Empowering Smaller Financial Institutions Against Financial Crime

For the past three decades, financial institutions (FIs) have heavily relied on artificial intelligence (AI) to combat scammers. Initially, card networks and payment processors primarily used AI for transaction decision-making. However, its application has now expanded across various organisations and institutions to identify harmful transactions of all kinds. Despite its benefits, not all companies can leverage AI equally, which poses significant challenges for smaller FIs in their fight against fraud.

The Growing Divide in AI Capabilities

As criminals adopt more sophisticated tactics, the disparity in AI capabilities becomes more pronounced, making it harder for smaller FIs to identify and prevent fraud cost-effectively. These institutions often turn to suppliers with extensive datasets from multiple clients, even if the data isn't precisely tailored to their needs. Smaller FIs face increased risk due to this reliance on external suppliers, especially as bad actors exploit new AI technologies.

The Escalating Threat of Financial Crime

Financial crime, encompassing bribery, human trafficking, money laundering, and terrorism financing, poses significant risks to financial institutions, their clients, and the broader community. Current biometric and AI measures are proving inadequate in deterring these crimes. Recent years have seen a worrying rise in financial crime, a trend expected to continue through 2024 and beyond. Governments and consumers are increasingly looking to financial institutions to take a stronger stand against these threats.

The Cost of Financial Crime

The impact of financial crime extends beyond financial losses, affecting reputations and leading to negative perceptions among clients, potential customers, and investors. Non-compliance with anti-money laundering (AML) regulations can result in severe penalties. For instance, in 2023, the Federal Reserve fined Deutsche Bank $186 million for AML failures, and Binance faced a $4.3 billion fine for AML violations. These examples underscore the critical need for investing in advanced tools to combat financial crime effectively.

The AI Conundrum for Smaller FIs

According to a recent study by BioCatch, 73% of FIs globally use AI for fraud detection. However, smaller FIs face a significant challenge: AI's effectiveness is heavily dependent on the quality and quantity of data used for training. With fewer data at their disposal, smaller institutions find it challenging to justify investments in internal AI development, often turning to third-party providers to address rising fraud and financial crime.

This dynamic creates an AI Data Gap similar to the wealth gap, where lower-income consumers must rely on more expensive credit options. Unable to develop robust internal AI solutions, smaller FIs must allocate more of their budgets to external AI vendors, further entrenching their reliance on third-party solutions.

Emerging Threats from AI-Enhanced Scams

AI's ability to detect activities that human analysts might miss is one of its greatest strengths. However, this same capability makes AI tools attractive to criminals. Generative AI (GenAI), for example, has shown immense potential to enhance the quality and quantity of malicious activities. Fraud and financial crime professionals are increasingly concerned about AI's role in automating scam tactics, locating personally identifiable information (PII), and creating more convincing scam messages.

The rise of deepfake technology, which can create realistic images, voices, or videos, poses additional threats to identity and authentication controls. AI's widespread adoption by criminals means that the volume and sophistication of attacks will likely increase, placing smaller FIs at a greater disadvantage.

Strategies for Smaller FIs

To address these challenges, smaller FIs need to supplement their AI capabilities with tools that are versatile and effective against adversarial AI threats. Traditional identity verification and authentication controls may soon become obsolete, necessitating investment in more resilient solutions such as behavioural biometric intelligence. This technology, which analyses patterns of behaviour to detect fraud, offers a robust defence against AI-enhanced scams.

Levelling the Playing Field

The implications of the growing illegal use of AI technologies will exacerbate the existing AI Data Gap. Smaller FIs must adapt by investing in advanced, adaptable solutions rather than continually relying on vendor AI systems that may quickly become outdated. Behavioural biometric intelligence can help level the playing field, making smaller FIs more resilient to attacks and enhancing their ability to detect and prevent fraud.

By focusing on outcomes rather than hype, smaller FIs can bridge the AI Data Gap, ensuring they remain competitive and secure in an increasingly digital financial landscape.