Financial fraud causes billions in losses every year for banks and financial institutions (FIs). By 2027, the global cost of fraud could reach $40.62 billion. In the UK alone, £753.9 million was stolen through fraud this year, an increase of 30% compared to the same period in 2020.

The rapid adoption of digital and real-time payments and new payment capabilities means banks and FIs must constantly evolve their fraud protection strategies to keep on top of the ever-changing threat types and stop fraudsters from getting the upper hand.

Identifying and preventing fraud at the right time is imperative to protecting customers. It’s time banks and FIs start to fight fraud in real-time.

The rapid adoption of real-time brings new opportunities but also risks

While the rapid adoption of real-time and digital payments presents a huge opportunity for banks and FIs to modernise and increase profit margins, it also increases the threat of fraud.

Consumers demand convenience, and this is contributing to the sharp increase in the number of digital and real-time payments being made. Yet there is a price to pay for this convenience. Payments made on real-time rails in lieu of cash leads to consumers leaving a digital identity trail over the internet. For instance, while using public WiFi while entering personal data online. And this is widening the window of opportunity for cybercriminals to attack.

To help protect consumers from such fraud, banks and FIs must have fraud management solutions in place that can quickly, in real-time, identify risk signals before it's too late – crucially without diminishing the user experience.

Keep pace with evolving scams

Fraudsters know card payments are much more secure these days - this year we saw Authorised Push Payment fraud rise 71%, taking over card fraud losses for the very first time. As a result, we’re seeing fraudsters revise classic phishing scams, but with a unique digital slant.

Social engineering scams for instance have increased since the start of the pandemic, as real-time payments adoption accelerated. This type of fraud is where victims are unwittingly tricked into transferring funds into a fraudster’s account as they pose as a legitimate payee. For instance, fraudsters convincing a homeowner that they are indebted to their energy company or posing as senior personnel to trick employees into making large corporate transactions.

Due to current regulations, in many cases, account holders will not recover funds if they fall victim to social engineering scams. Given that real-time payments are settled instantly if they are not stopped in real-time, the money is often lost forever.

The power of Machine Learning

Machine learning (ML) is an indispensable weapon in the fight against fraud. Especially in a real-time world, where fraudsters are becoming far more creative with how they commit crime, banks and FIs need effective fraud-management tools.

Not only does ML drastically reduce false positives through powerful, automated capabilities, it can also serve as part of a multi-layered fraud strategy designed for rapid deployment and ease of use - providing access to the most reliable information in real-time.

Incremental learning technology is particularly effective in the fight against fraud. While traditional ML models need to be “re-trained” as fraud evolves, models using incremental learning make small adjustments on an ongoing basis. It adapts itself in production when new behaviours are observed. For banks and FIs, this would put less strain on their fraud analysts and increase their operational efficiency, while creating a smoother payments experience for customers.

Collaborate to combat fraud

While ML is crucial to combating fraud, it's only as strong as the data that is fed into the algorithms. Systems must be supplied with the widest possible view of risk in real-time. This requires access to internal and external data which can be achieved through collaboration - from regulators to law enforcement to banks, every party involved in combating fraud must collaborate effectively.

One solution that is extremely helpful with collaboration is network intelligence. This allows banks, processors, acquirers and networks to securely share and consume industry-wide fraud signals to feed their ML models alongside proprietary data.

Collaboration enables both banks and FIs to complement their ML with signals exchanged within the community and from third-party fraud intelligence sources. As real-time transactions are irreversible, cross-industry collaboration will prevent funds from being lost by holding the transaction on the receiving end.

Fraudsters are always on the lookout for new ways to commit financial crimes. Keeping up to date and evolving strategies by harnessing modern fraud management solutions in real-time, will help banks and FIs beat fraudsters and win the fight against fraud.