AI can strengthen fintech cybersecurity, but risks remain

Amid the growing sophistication of cyber threats targeting financial institutions, cybersecurity and enterprise risk management professional, Omotola Akanni, has highlighted the increasing role of artificial intelligence (AI) in strengthening cybersecurity for financial technology (fintech) applications.

In a detailed paper made available to newsmen, she explained how AI-driven technologies are being deployed to detect, prevent and respond to cyber threats, particularly as traditional cybersecurity measures struggle to keep pace with increasingly complex attacks targeting digital financial systems.
The paper, titled “The Role of Artificial Intelligence in Enhancing Cybersecurity for FinTech Applications: A Comprehensive Review,” was co-authored by Akanni and six other researchers and published in the International Journal of Computer Engineering and Technology in 2024.

Akanni and her co-authors noted that the rapid growth of fintech has made financial services more accessible and efficient, but has also expanded the attack surface for cybercriminals. They identified AI as an increasingly important tool for protecting financial platforms because of its ability to process large volumes of data and identify suspicious activities in real time.

According to the researchers, one of the most significant applications of AI in fintech cybersecurity is threat detection and anomaly identification. Machine learning systems can analyse transaction patterns and user behaviour to identify activities that differ from established patterns, potentially exposing fraud, unauthorised access or other forms of cyberattack.

The paper further explained that deep-learning models can identify complex patterns that conventional rule-based security systems may overlook. By learning from historical transaction data, AI systems can distinguish between legitimate and suspicious activities, making real-time fraud detection particularly valuable in environments where millions of transactions may take place within short periods.
Beyond detection, the researchers highlighted the capacity of AI to automate responses to cybersecurity incidents. AI-powered systems can, for instance, isolate affected systems, block unauthorised access and alert security personnel once suspicious activity has been identified, thereby reducing the time between detection and intervention.

The authors noted that such automation could be particularly important during attacks such as distributed denial-of-service (DDoS) incidents and ransomware attacks. AI systems can help reroute traffic, reallocate resources or quarantine compromised systems, limiting the potential disruption to financial services.

The study also identified behavioural biometrics as another area where AI is changing fintech security. By analysing characteristics such as typing patterns, mouse movements and touchscreen interactions, AI systems can develop behavioural profiles that support continuous user authentication without relying solely on passwords or other conventional authentication methods.

Akanni and her co-authors, however, cautioned that the growing use of AI in cybersecurity comes with significant challenges. They pointed to data privacy and ethical concerns, noting that AI systems require large datasets and must therefore be developed and deployed in ways that protect sensitive financial information and comply with applicable privacy regulations.

Another concern raised in the paper is the difficulty of interpreting decisions made by complex AI models. The researchers said the “black box” nature of some deep-learning systems could make it difficult for cybersecurity teams and regulators to understand why a particular decision was reached, strengthening the case for greater development and adoption of explainable AI.

The researchers also warned that AI systems themselves can become targets of cyberattacks. Adversarial attacks, in which carefully manipulated data is used to deceive an AI model, could cause security systems to misclassify legitimate activities as threats or overlook malicious behaviour. They therefore identified adversarial training and other defensive measures as important areas for continued research.

The paper further noted that the computational demands associated with developing and deploying sophisticated AI systems could create scalability and cost challenges, particularly for smaller fintech companies. While cloud-based solutions can provide additional computing capacity, the researchers said such arrangements also raise questions around data security, privacy and regulatory compliance.

Looking ahead, Akanni and her co-authors identified the integration of AI with blockchain technology, the development of explainable AI, personalised security systems and closer collaboration between AI systems and human cybersecurity professionals as promising areas for further development. They argued that AI should complement rather than completely replace human expertise, with machines handling large-scale analysis while human professionals provide judgement and context in responding to complex threats.

Akanni, who has a background spanning banking, enterprise risk management, data analytics and cybersecurity, is currently pursuing work at the intersection of financial services, technology and security. Her research reflects the growing convergence of these fields as financial institutions increasingly rely on digital platforms while confronting new forms of cyber risk.