How AI finds and stops payments fraud

The promise of AI and the beauty of large language models sets radical expectations in how banks can improve overall payment experiences for customers. This technology is re-orienting the way financial institutions, and payment service providers operate, and its advocates have identified (not without low hubris) numerous use cases, all offering new ways for banks to improve their ways of work.

From boosting transactional efficiency, to deepening transactional insights, automating customer support and hyper-personalising services, AI offers immense potential. However, any seasoned banker or payment practitioner will hastily emphasize the need for careful and strategic calibration, and to employ AI strategically and intentionally where benefits are clear.

Traditionally, banks have been slow to adopt new technologies. However, the democratisation of AI, exemplified by tools like ChatGPT, marks a new era where integrating AI into payment systems is not just a tech upgrade – it’s a transformation in serving and protecting clients.

In today’s fast-paced digital economy, payment systems are rapidly evolving, with innovations like real-time clearing and card-not-present transactions enhancing convenience and speed. However, these advancements also bring new risks.

Payments are now in the ‘triple-zero’ paradigm of zero processing time, zero risk, and zero cost, as regulatory expectations for smarter, seamless transactions that maintain trust and security are a minimum expectation. Recent liability shifts between payment participants in the UK for fraud losses experienced by clients highlights the payment providers’ responsibility to protect vulnerable consumers from the omnipresence of fraud typologies and fraudsters.

While it’s not new to banking – having been used for forecasting, personalising offerings, and risk mitigation – its most significant potential lies in fraud detection and prevention. AI has already enhanced data anomaly detection, sped up business intelligence reporting, and automated daily analytics.

But we need to do more. Any hardened fraud specialist will tell you that as banks invest millions to expand their application of AI, fraudsters are also exploiting AI to execute increasingly sophisticated scams at a pace unrivalled in human history. A 2023 PwC report, in collaboration with Stop Scams UK, found that many banks and technology companies believe it’s only a matter of time before fraudsters adopt AI on an even larger scale. To stay ahead, banks and payment providers must intensify their AI efforts to combat these evolving threats.

This is where predictive analytics – a core component of AI – comes into play. AI identifies unusual patterns and behaviours that humans might miss, significantly reducing the risk of unnoticed fraud. By leveraging predictive analytics, payment technologies can stop fraudulent activities in real time, preventing significant losses before they happen.

AI’s capabilities are advancing rapidly. Its continuously self-evolving nature allows ongoing refinements. In fraud risk mitigation, AI has the potential to distinguish between legitimate anomalies and actual fraud, reducing the number of legitimate transactions mistakenly blocked. By learning from fraud trends, AI powered systems can adapt and evolve, providing robust protection against new threats.

The sheer pace at which AI-powered tools can consume and interrogate data sets, analyse patterns, and yields outputs, enables tasks that previously required significant human effort to be completed with minimal resources. This allows teams to focus on complex reasoning assessments that traditional AI cannot do and client interactions, enhancing the overall risk management process.

However, AI is not a silver bullet. Effective fraud prevention requires a multi-pronged strategy, combining AI with customer education, strong compliance frameworks, and collaboration with regulators. One of the key roles AI can play in this multi-layered approach is in enhancing compliance and reporting. By automating transaction monitoring, AI can ensure faster and more accurate reporting to regulatory bodies.

In conclusion, while AI is not a cure-all, it is a powerful tool in the fight against fraud. Its ability to process and analyse data at unprecedented speed and scale offers the financial sector – particularly in payments – a fresh approach to identifying and preventing fraudulent activity. Payments are the first line of defence against fraud losses, and as we continue to refine and develop AI tools for this area, we can expect to see even more sophisticated fraud detection and prediction systems that will help the entire sector curb the alarming rise in major fraud losses.