Agentic AI in Banking

Artificial Intelligence is evolving rapidly, and its newest frontier, known as Agentic AI, is beginning to reshape how the banking industry operates. Unlike earlier AI models that merely responded to prompts, agentic systems are designed to act with autonomy. They can initiate actions, complete multi-step processes, and make decisions without constant human oversight. This shift promises enormous efficiency gains for banks but also raises significant concerns around security, ethics, and governance.

The appeal of agentic AI lies in its ability to transform routine operations. Already, around 41% of organisations in Australia report using some form of agentic AI and that's a huge number, and by 2029 it is projected that nearly 80% of customer service issues could be handled autonomously (Banker's jobs??). For banks, this translates into faster response times, reduced costs, and an enhanced customer experience. Compliance monitoring is also evolving, with solutions such as Proofpoint’s Human Communications Intelligence claiming to interpret human conversations including slang, shorthand, emojis, and tone in real time. By moving away from keyword-based monitoring and towards contextual understanding, such tools claim up to a 90% reduction in false positives, which is a significant leap in operational accuracy.

Yet the same technologies that empower banks can also empower adversaries. Cybercriminals are increasingly turning to AI to sharpen their attacks, with as many as 80% of ransomware campaigns now incorporating AI-driven elements such as advanced phishing, social engineering, and deepfakes. The banking sector finds itself in the middle of an arms race: as institutions use AI to safeguard systems, attackers use the same technology to evade detection. At the same time, the rise of real-time monitoring of employee and customer communications introduces new ethical and regulatory dilemmas. Constant surveillance can conflict with privacy laws, employee rights, and expectations of confidentiality. Moreover, AI systems often struggle with cultural nuance, sarcasm, or intent, raising the possibility of misclassifications that could result in compliance errors or reputational damage.

There are also risks inherent to the AI systems themselves. If compromised, these tools could be manipulated to miss threats, conceal fraudulent activity, or even leak sensitive communications. This makes the integrity and security of the AI a crucial issue in its own right. As banks adopt these technologies, they must not only consider how AI can be used to detect misconduct but also how to protect the AI from becoming a new point of vulnerability.

The adoption of agentic AI therefore cannot be viewed purely as a technological upgrade. It must be accompanied by robust oversight and governance. Human-in-the-loop systems will remain necessary for sensitive or ambiguous cases, and transparency will be vital so that regulators and customers alike can understand how AI-driven conclusions are reached. Regulatory frameworks will need to evolve to demand clearer auditing, accountability, and disclosure around AI use. For global institutions, harmonising standards across jurisdictions will be essential to avoid compliance conflicts.

Agentic AI is poised to redefine the future of banking security. If managed wisely, it offers the potential to enhance efficiency, protect assets, and build stronger customer trust. But if deployed recklessly, it could amplify the very risks it seeks to mitigate, fueling fraud, misinterpretation, and data misuse. 

The challenge for banks, regulators, and technologists is not whether to adopt agentic AI, but how to govern it responsibly. Efficiency and security must grow together, or the sector may find itself solving one problem only to create another.