How can neobanks meet the challenges posed by cybercrime?

With the rise in the number of digital-only “neobanks” in the UK, the top four alone have more than 12 million account holders, according to research by Insider Intelligence.

Advances in AI and ML could help neobanks better protect themselves against cybercrime

As a growing niche, they represent a major shift in a traditional market, driven by the ubiquity of connected devices and a younger generation of customers gravitating towards digital native services.

Operating without physical branch networks or complex legacy technology infrastructure, neobanks are seen by many as the future of the industry. And while their emergence has done much to challenge the status quo, promote innovation and competition, they operate within a financial oversight ecosystem that is still based on the assumption of a relationship centered on between the bank and the customer.

With neobank customer relationships defined by applications and APIs, this creates a particular set of challenges at a time when financial crime is becoming increasingly sophisticated and successful.

If that were not enough, the integration of digital technologies and criminal strategies that exploit cybersecurity vulnerabilities threaten to elevate issues such as fraud to a scale never seen before. The desire to acquire as many customers as possible through frictionless onboarding processes can often leave insufficient time for rigorous financial crime prevention methods.

Indeed, financial crime and cybercrime are becoming increasingly unified as criminals seek to exploit cybersecurity vulnerabilities to focus on everything from document and identity fraud to account takeovers and menu.

This contributes to a significant financial burden for consumers and businesses. For example, a recent study by Juniper Research indicates that between 2021 and 2025, online payment fraud will cost more than $206 billion.

Complex problems require innovative solutions

While neobanks have focused on innovation in an effort to differentiate themselves from mainstream rivals, what hasn’t changed is their approach to financial fraud, which tends to follow the same processes and rules as their long-established counterparts. Often these processes can be under-resourced as neobanks strive to be as lean as possible to prioritize growth.

Digital innovation is also improving the efficiency of financial criminals who are turning to automation techniques to increase the reach and volume of their attacks. This puts immense strain on legacy detection and prevention systems and processes.

The problem is that identifying and combating fraud largely depends on human-centric and labor-intensive processes, even in neobanks. Teams are often overworked, so their limited resources are stretched on too few cases. Add to that the major burden placed on fraud teams by enormous levels of false positives – where legitimate transactions are falsely flagged as suspicious – and defenses are arguably more strained than ever.

The ripple effect is that neobanks are using valuable resources to manually fight fraud when they could be devoting the time, expertise and creativity of their employees to pushing the boundaries of banking innovation even further. . And it’s not just about the impact on the bottom line. There is also the human cost of finance professionals facing burnout and organizations facing the accompanying staff turnover, which has the potential to further degrade performance.

Faced with this scenario, how can neobanks or any financial institution keep pace with automated cybercrime unless they can use similar techniques to meet the challenge head-on?

Technologies based on artificial intelligence (AI) and machine learning (ML) allow financial organizations to extend prevention and detection to a level that manual processes cannot hope to reach. It can, for example, be applied when authenticating genuine documents so that teams can spend their valuable time fighting more sophisticated document fraud.

In anti-money laundering (AML) use cases, AI can also be applied to analyze hidden relationships between identities and transactions to draw a better decision boundary between legitimate and criminals. And for buy now, pay later (BNPL) and payment fraud, it can be applied to accurately detect real versus stolen identities, reduce the frequency and impact of account takeovers, and minimize the commercial burden of false refusals.

The list of use cases keeps growing. Not only do these technologies improve productivity, they also increase detection rates while reducing waste. Plus, they help deter fraudsters who have to spend more time and resources on their efforts for a less favorable return.

Without innovation-driven change driven by advances in AI and ML, neobanks could find themselves severely impacted by rising levels of fraud. At a time when surveillance and compliance are increasing dramatically, with several leading organizations having been fined lately, addressing the challenges posed by cybercrime will help them focus on transforming the industry transformation forecast in reality.

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