The rapid evolution and global adoption of real-time payment schemes marks a pivotal shift in the global financial ecosystem, improving economies and financial inclusivity…and introducing new opportunities for crime. One unintended benefit of legacy systems that take days or weeks to process transactions is additional time for financial institutions to identify and prevent fraud. Transactions that process in seconds have a profoundly positive impact on efficiency and customer experiences, but that very speed makes detecting and responding to fraud incredibly challenging, especially at scale. The relative newness of instant payments also creates fertile ground for crime, as fraudsters look to exploit potential loopholes in companies’ digital transformations. These challenges come at a steep cost: US News & World Report found that 65% of adults are worried about cyber-attacks, and in the US, fraud-related losses topped $10 billion last year.
The integration of artificial intelligence (AI) in financial services has added another layer of complexity, both in terms of enabling sophisticated financial crimes and in fortifying defenses against them. These tools give fraudsters unprecedented speed, precision, and scale, which can overwhelm traditional security measures. As a result, AI-backed financial crime is on the rise. In particular, synthetic identity fraud – where fraudsters can scramble real data with fake data to create fake profiles that look real – has seen an astronomical rise in the past year; by some estimates, 95% of synthetic identities are not detected by financial institutions.
Understanding these dynamics and strategically deploying AI to counter AI-backed crime is paramount to protecting the global financial ecosystem.
It all starts with signals
The more granular an organization’s anti-fraud data, the better prepared it is to train AI systems to recognize and flag attempted fakes. AI systems need the insights that data provide, also referred to as signals; once connected to a framework that enables these signals to be shared between peers, the greater the ability to protect the actual data. The more personal information a criminal’s AI has, the more it is able to convincingly slip through security nets. Limiting criminals’ access to data signals is a vital part of safeguarding individuals and businesses, but frequent breaches have flooded the criminal market with a slew of highly personal data. The cost to buy an average American’s “full credentials” – social security number, name, date of birth, etc. – on the dark web is only $8.
The better option is to make sure banks’ anti-fraud AI systems have access to more and better data signals than criminals do. When it comes to real-time payments, this means larger, global payments companies who have been in the market for decades have a distinct advantage. Sophisticated organizations that process billions of transactions and trillions of dollars have far more information at their disposal, have been using AI for years, and are light years ahead in terms of know your customer (KYC) behaviors and patterns. For example, behavioral biometrics – typing patterns, mouse movements, touch dynamics, etc. – can help analyze unique behavior and flag deviations. As a continuous authentication process this can give financial institutions an edge over criminal actors. Taken as a whole, this vast quantity of global data can help financial institutions not just prevent attempted fraud but anticipate future fraud.
Network effects as protection for banks
Small and mid-sized banks are the most vulnerable to AI-backed financial crime because they generally have less data than their larger peers, and fewer resources to invest in security. One solution is to partner with global payments processors, gaining access to much larger signals and more sophisticated crime-fighting AI. Because it is in the payment company’s interests to prevent as much fraud as possible, there’s no meaningful differentiation between security offered to different tiers of banks; small/regional banks’ customers are as protected as their larger peers.
Another benefit of participating in this large ecosystem is banks’ ability to learn more about their own customers. More and better customer data helps banks identify macro trends sooner, as well as potentially overlooked loopholes or customer needs. This information helps mobilize them to develop needed products and services. Beyond unlocking new potential revenue streams for the bank, better products improve customer satisfaction and – with appropriate guardrails – help contribute to a safer financial ecosystem overall.
The proliferation of real-time payments and the concurrent rise of AI-driven financial crimes necessitate a paradigm shift in security strategies. The future of financial security lies in the seamless integration of AI into all aspects of security operations. By harnessing the power of AI and the network effects of large payments partners, financial institutions can not only protect themselves against current threats and losses, but also anticipate and mitigate future risks. Collaboration between financial institutions, regulators, and technology providers will be critical in developing robust security frameworks that can keep pace with evolving threats.
