More than a dozen years ago, an innovative online security solution was discovered that allowed for real-time monitoring of web and mobile transactions as they traversed the network. For the individual overseeing online security, this represented the most valuable data they had encountered. Remarkably, it enabled them to set up real-time alerts for any anomalous transactions without needing to write a single line of code. The impact was immediate; unauthorised wire transfers were effectively halted before any funds could leave the bank.
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Fast-forward to 2025, and the landscape has changed dramatically, necessitating entirely new approaches to combating digital fraud in the banking sector.
In recent times, there has been a notable surge in social engineering tactics aimed at consumer financial scams, often referred to as “authorised” transactions. Concurrently, banks and their clientele are facing an escalating array of sophisticated attacks executed through web platforms, mobile devices, and even APIs that access sensitive customer information. This raises the question: what tools are needed to navigate this evolving threat landscape? First, however, it’s essential to examine the new types of threats emerging today.
The New Threats
In March 2024, the Wall Street Journal highlighted that “Banks and other financial institutions are under constant attack from cybercriminals aiming to disrupt their websites and applications temporarily, largely driven by an increase in hacktivist activities targeting companies in politically unstable regions.” A DDoS service provider noted a staggering 154% rise in attacks among its clients in 2023.
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Earlier this year, Change HealthCare suffered a significant cyberattack. As reported by The Washington Post, this firm processes around half of all medical claims in the United States, leading to many customers experiencing delays in accessing critical medications.
Even major technology firms are not spared from these threats. One prominent vendor has been engaged in a prolonged battle against a state-sponsored cyberattack affecting its data centres, placing their vital source code at risk. Additionally, Apple, once regarded as a bastion of security, is now releasing updates multiple times each month to address vulnerabilities.
The current landscape reveals a surge in cyberattacks aimed at crippling various businesses, particularly within the financial sector. Banks find themselves under siege from a diverse array of threat actors, including hacktivists, traditional cybercriminals, and even state-sponsored entities. The motivations behind these attacks are manifold:
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– Disrupting or halting banking operations
– Exfiltrating sensitive bank data
– Seizing control of data centre environments
– Facilitating financial crimes, such as setting up money mule accounts
– Profiting from fraudulent activities
The tactics employed by these malicious actors have evolved significantly. Gone are the days of solitary attackers using simple bots and fraud farms; now, we see the rise of cybercrime-as-a-service (CaaS). This model involves teams of skilled programmers creating sophisticated attack codes that can be purchased and utilised by other criminals, leading to increasingly intricate assaults. As technology progresses, the complexity of these attacks is expected to escalate further with the integration of Generative AI models.
In response, banks must adopt robust solutions to defend against these evolving threats. While there has been considerable dialogue surrounding defences against high-volume Distributed Denial-of-Service (DDoS) attacks, there has been less focus on lower-volume but brilliant assaults—particularly those leveraging Generative AI to gain unauthorised access to applications and generate fraudulent transactions through targeted automated bot activities. This aspect is well understood within the industry.
However, the more challenging issue lies in recognising that cybercriminals continually experiment with new tactics to breach their targets, often with remarkable frequency.
Recently, a trend has emerged among vendors who specialise in swiftly identifying shifts in attack vectors and adapting their solutions in real-time. For this approach to be practical, these vendors require a diverse clientele that attracts the attention of cybercriminals—such as banks, major internet service providers, companies involved in Generative AI (a hot target nowadays), healthcare organisations, and more. When a new attack vector is identified targeting one client, these vendors can quickly pivot their solutions to protect their other customers.
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Kevin Gosschalk, the founder and CEO of Arkose Labs, shared an illustrative incident where one of their internet service clients fell victim to an attack. The company rapidly learned from this event and deployed a comprehensive solution across its entire customer base, demonstrating the agility required in today’s cybersecurity landscape.
The issue at hand is the alarming ease with which fraudsters can access Generative AI models. These malicious actors are adept at manipulating AI prompts to produce harmful outcomes, leading to a potential surge in innovative attack methods that could become relentless over time. Unfortunately, there appears to be no effective means to prevent these wrongdoers from exploiting Generative AI.
To combat this challenge, the only viable software solution must be capable of continuously learning from initial attacks and rapidly disseminating updated defences across its customer network. Consequently, a more extensive customer base becomes crucial for facilitating this “on-demand” software update capability.
This scenario resembles a consortium approach. Typically, when one thinks of data sharing within a consortium, it involves customers providing information, such as a known money mule account number. However, in this context, it is the vendor that shares data related to the attacks on transactions. This information is conveyed indirectly through protective updates and also includes raw data points with explanations to assist customers in safeguarding channels that the vendor does not directly oversee.
For such a solution to be effective, it necessitates a vendor with a round-the-clock operations centre staffed with personnel who monitor customer traffic closely. The rationale is straightforward: adversaries often strike when security teams are off duty—during evenings, weekends, and even holidays.
In discussions with Patrice Boffa, Chief Customer Officer at Arkose Labs, he emphasised the importance of their 24/7 operations centres. Image may be NSFW.
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He explained that this setup allows them to detect attacks promptly, analyse their mechanics by recognising attack signals, and adjust their defences accordingly. Boffa further noted that a significant aspect of their strategy involves utilising challenge technology and Generative AI to swiftly create or modify challenges as part of their defence mechanism.
Looking ahead, Boffa predicts an escalation in malicious actors’ use of Generative AI to imitate human behaviour in executing these attacks.
Summary
In the opening of this blog, the author reminisces about a remarkable fraud detection solution discovered over a decade ago and its significant impact. However, in the present day, the focus shifts to the ever-evolving threats that jeopardise account openings, logins, and various online and mobile transactions. The author emphasises the necessity for a vendor capable of monitoring these changes in real time and swiftly enhancing security measures.
The individuals targeting financial institutions aim to cripple operations, and disrupting digital channel activities serves as an effective strategy. Consequently, the author underscores the importance of having a continuous monitoring solution that identifies new attacks and provides immediate protection following their detection.
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Maxthon
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