Double Agents in AI: What Banks Need to Know

Banks and their financial service peers have recently increased deployments of autonomous AI “agents” to streamline operations and enhance services. AI agents are built with one goal or task to complete. They can be powerful tools to fight fraud, improve customer experience and optimize efficiency.

Then again, without proper supervision, AI agents can act like double agents. They’ll introduce new risks or unintended consequences that undermine their intended purpose and compromise the organization’s values. Let’s learn how to properly mitigate risk when deploying AI agents.

How to Use Agentic AI’s Autonomous Nature in the Financial Sector

Autonomous or agentic AI makes decisions at superhuman speed and scale, making AI agents a critical part of any forward-looking financial strategy. By using them to automate tasks like enhanced fraud detection and compliance checks, employees can focus on relationship-building and strategic advising.

In the financial sector, agents can be implemented in a few different ways – they can be added to existing workflows and infrastructure or built from scratch, requiring an overhaul of the ecosystem. Wherever they sit, they must align with internal InfoSec requirements and meet regulatory obligations like GDPR or CCPA to avoid risks. When deployed properly with appropriate confines in a use case, AI agents are trusted partners. They can empower financial services to operate more efficiently, helping employees stay agile and organizations stay competitive in a rapidly evolving market.

An example of an AI agent’s positive impact comes from a customer engagement project with an international financial institution. By implementing a tailored AI model to automate data enrichment and profile generation, we ensured the model not only met regulatory standards but also aligned with the company’s broader information governance framework. This is just one of the infinite ways financial organizations have chosen to use autonomous AI to make work more efficient.

Vulnerabilities: How to Mitigate These in Deployment

AI’s speed and scale superpowers give it value and bring great risk. Without clear constraints, AI-driven systems can go awry, making biased or non-compliant decisions that a human would catch. An unchecked, biased AI algorithm could deny l**** to certain groups or ignore early fraud signals. This does reputational damage and regulatory harm to the bank before a human has grown wiser to the tricks.

To mitigate the risk, it’s incredibly important to think through every step of the use case process. Where will the agents live and how you will use them determine which processes and data are at risk?

AI should serve as a force for good while minimizing risks. In short, banks adopting autonomous AI must plan for the dark side of empowerment: every AI agent needs an internal set of laws and limits, much like a junior employee under supervision, to prevent costly mistakes or malfeasance.

AI solutions must be tailored with industry-specific ethical and legal guardrails to avoid a one-size-fits-all approach. These can overlook critical nuances in banking and cause massive consequences for organizations down the road.

Before deployment, banks and financial institutions must ask themselves – does your organization have the capacity to deploy and maintain the AI?

Read More on AiThority: AI Agents: Transformative or Turbulent?

The Need for Human Oversight, Explainability and Accountability

Because of the unintentional risks that the system poses, human oversight and explainability are paramount when deploying agentic AI in finance. Even the most advanced AI should function as an assistant; not a rogue actor that is left to their own decision-making capabilities. Final decisions must remain in the hands of human experts.

When deciding how to deploy AI agents, a human-on-the-loop approach ensures that AI suggestions or actions are reviewed and can be overridden. Since AI is used to solve business problems, financial organizations must think of which specific use they’d like the AI agent to take on and have the business process end with human quality assurance.

For example, the AI could be deployed to flag anomalous transactions or suggest credit decisions, but humans must be a part of the process by overseeing and validating these results at the end. Having humans there to take responsibility for the end outcomes ensures accountability and trust remain between the bank and its employees and the bank and its customers.

Explainability is an important factor when tracking oversight and staying compliant. Banks need AI systems that can explain how and why a decision was made. This is increasingly important as regulators set expectations for algorithmic transparency.

According to fintech leaders, companies may soon be required to audit algorithms to prevent bias and ensure customers have the “right to human review” for high-stakes AI decisions like loan approvals. In practice, this means deploying AI models whose reasoning can be interpreted, or at least traced, by developers and risk officers. If a model can’t explain its decision in understandable terms, it becomes a “black box.” This is an unacceptable scenario when customers’ livelihoods are at stake.

This is why at the beginning stages, when companies are pursuing innovation, compliance and governance must be built into the AI agents and business processes from the ground up. Every AI agent deployment should have auditable decision logs and documentation for how it works.

In fact, next-gen banking strategies explicitly advise designing auditable, traceable compliance processes alongside any new AI deployments. By ensuring transparency in the logs and human review, banks create an environment where AI tools can be used and trusted by internal stakeholders, regulators and customers alike.

For example, we recently worked with a global bank to implement an AI-powered call routing system to triage customer inquiries. The AI model understood classified incoming requests by priority and even handled simple queries automatically. The result was 95% accuracy in prioritizing calls and an 89% forwarding efficiency, meaning customers got faster service and staff focused on the most urgent cases. This AI agent was closely monitored and tuned to ensure it didn’t misroute sensitive customer calls, illustrating the benefit of careful configuration and human oversight during deployment.

AI delivers ROI (from day-zero) when it’s applied to well-defined problems under careful governance. The bank started with a clear objective (speed up call handling) and developed its AI agent through rigorous testing and oversight. The AI agent wasn’t given free rein. It operated within boundaries set by business rules and compliance requirements. This kind of disciplined approach allows banks to reap the benefits of autonomous agents (speed, scale) without unpleasant surprises.

Best Practices for Risk Mitigation

To avoid “double agent” pitfalls and ensure AI initiatives stay on track, banks should adopt proven best practices for AI governance and risk management. These include:

• Establishing Strong Guardrails: Define ethical and operational boundaries from the start to keep AI agents aligned with finance-specific compliance and organizational risk tolerance.
• Keeping Humans on the Loop: Always route high-impact decisions through a human reviewer to ensure accountability and catch unexpected AI agent actions.
• Building Auditable and Explainable Systems: Deploy AI agents with transparency in mind so that every output can be traced and justified by auditors, regulators and internal teams.
• Embedding Ethics and Privacy from Day Zero: Prioritize justness, privacy and accountability during AI agent development to reduce bias, protect data and avoid compliance failures.
• Adopting a Proactive Governance Framework: Organizations can have AI agents and their own agency. They should set internal AI rules before regulators do, utilizing cross-functional oversight, documented model intent and real-time risk monitoring from day zero.

The era of autonomous AI in banking is here, bringing with it unprecedented opportunities and new dimensions of risk to organizations and customers. Banks that treat their AI as a double-edged sword, using it with strategy and caution, will emerge as winners of the AI innovation race.