By Rob Marchiori, CEO ANZ Cognizant
In an era where cyber threats are evolving at an exponential rate, Natural Language Processing (NLP) has emerged as an indispensable ally for financial institutions. Its ability to interpret and analyse vast volumes of unstructured data, such as emails, voice recordings and transaction notes, empowers organisations to rapidly identify anomalies or compliance breaches that traditional, rule-bound systems simply cannot detect.
This technological edge has never been more crucial, but NLP is just the beginning.
As we navigate an accelerating AI landscape, the fast-evolving role of the technology presents a high-stakes scenario and for many businesses the risks may feel beyond their control. Crafting more convincing scams than ever before, fraudsters are now using advanced tools like AI-generated deepfake voices or automated synthetic identities that can scale across entire corporate networks. The scale and sophistication of these attacks is ever-increasing, with 78% of CISOs admitting that AI-powered cybersecurity threats are having a significant impact on their organisation (The State of AI Cybersecurity 2025, Darktrace).
While these techniques are increasingly advanced, they must be met with equally innovative approaches in fraud detection to move from passive risk monitoring to active threat mitigation. In fact, the very tools used to attack businesses, can be leveraged by the defenders in ways that prioritise explainability, transparency and regulated trust.
Yes, the banking industry is in a vulnerable period as AI creates gaps between bad actors, who can innovate rapidly unburdened by privacy or regulatory concerns, and the defenders. However, if the industry can move quickly, we will have a chance at closing that gap and protecting the vital corporate data that underpins our business operations.
‘Agentifying’ the architecture of fraud detection
While NLP is a powerful tool for detecting fraud through language-based cues, its true effectiveness emerges when integrated into a multi-agent AI system. These collaborative ecosystems of specialised intercommunicating agents can monitor behaviour in real time across networks, assess risks contextually, and autonomously prompt protective responses, such as locking accounts or coordinating alerts for human review.
For example, an agent monitoring language in emails (NLP) can firstly detect unusual network activity, such as flagging suspicious language. It can then communicate this with another agent which is monitoring privacy, allowing rapid, coordinated responses. This structure allows for parallel task execution, dynamic adaptation and scalable defence, markedly enhancing the speed and accuracy of threat detection, incident response and threat simulation.
These systems can reduce triage times from hours to minutes in the event of a cyber-attack.
Still, the technology alone isn’t enough.
AI tools, such as NLP, for fraud detection and compliance are powerful, but raw algorithms alone cannot succeed without effective implementation.
Navigating the new frontier
As banks look to adopt AI-driven tools for cybersecurity, the challenge lies in balancing the use of vast datasets while maintaining data privacy. AI thrives on large, diverse and reliable datasets to effectively detect anomalies, identify threats, and predict future attacks. Yet financial institutions are under stringent privacy regulations that restrict the kind of information AI systems can access and analyse. This ‘tug of war’ between data protection and security effectiveness often slows model training and reduces the potential impact of advanced solutions. Whilst bad actors are rapidly innovating, banks must keep up pace by investing in privacy-preserving AI techniques, such as federated learning or differential privacy, that allow models to learn from data without compromising user confidentiality.
With the sheer scale and complexity of cyber threats increasing exponentially, banks must evolve from reactive to proactive defence strategies. However, integrating advanced systems that can autonomously respond in real time demands a foundational shift in operations, including building interoperable platforms and upskilling teams to manage and trust AI outputs. Multi-agentic systems, where AI entities collaborate and adapt to novel attack vectors, offer promising potential, especially as threats become more unpredictable and sophisticated. Yet, this maturity doesn’t happen overnight. Financial institutions need a phased approach: start by augmenting existing systems with AI, such as NLP, for threat detection, progress to automated incident response, and eventually adopt fully integrated cybersecurity mesh architectures (CSMA) that enable dynamic, decentralised protection.
For AI to become a cornerstone of cybersecurity in banking, it must be trustworthy, transparent, and aligned with evolving regulatory expectations. Today, banks face a confidence gap, not just from customers, but also internally, when it comes to relying on autonomous AI systems. This hesitation stems from a lack of explainability in how AI models make decisions, which is critical in high-stakes environments like fraud detection or incident response. Simultaneously, regulatory frameworks are struggling to keep pace with technological advancements, often acting as a hinderance to innovation. To move forward, regulators and financial institutions must co-create outcome-focused, technology-neutral regulations that support innovation without compromising oversight. Transparent AI governance, clear audit trails, and “human-in-the-loop” models can help bridge the trust gap while ensuring compliance. Only by fostering alignment between innovation, regulation, and public trust can banks harness AI’s full potential to safeguard the future of digital finance.
Creating the ultimate cyber defence model
The future of financial cybersecurity won’t be won by technology alone, it will be won by how effectively we deploy, govern, and evolve it. NLP paired with multi-agent AI systems, offer banks a transformative opportunity to shift from passive risk management to dynamic, intelligent defence. Seizing this opportunity demands a fundamental change in mindset, one that embraces AI as a strategic partner in building resilience, accountability and trust.
The time to act is now.
By harnessing the full potential of NLP and multi-agent AI responsibly, transparently and collaboratively, the financial industry will not only keep pace with emerging threats but outpace them. In doing so, banks won’t just safeguard systems, they’ll safeguard trust, stability and the future of finance itself.
