The broader financial services space, including banking, is experiencing a significant shift due to rapid advancements in AI. A particularly impactful innovation is agentic AI, which offers a new level of autonomy compared to traditional automation.
In contrast to conventional systems, agentic AI can learn from data, adapt its actions to different situations and make decisions in real-time. By 2028, according to some research, at least 15% of day-to-day work decisions will be made autonomously through agentic AI.
As financial institutions struggle to meet rising customer expectations and navigate increasingly stringent regulations, agentic AI enables the transition from static automation to dynamic autonomy. This technology enhances or automates crucial operational processes, enabling the industry to develop more adaptable, intelligent and robust business models. It is important for banking and financial organizations to be prepared and learn how AI can be beneficial to their current business.
Agentic AI’s impact across loan processes, regulation, fraud detection and more
Agentic AI is beginning to show tangible value across banking activities including loan processing, fraudulent transactions or portfolio management. In fraud detection and prevention, traditional systems rely on fixed rules, leading to frequent false alarms and overlooked threats. In contrast, agentic AI learns from behavior patterns and acts proactively by blocking transactions, highlighting irregularities, or increasing authentication measures before fraud occurs. This adaptive capability allows for quicker and increasingly accurate fraud detection.
Regarding loan processing, agentic AI goes beyond traditional models by gathering information from diverse sources, including bank statements, credit reports and employment data, to drive intelligent recommendations. From verifying identities to tailoring loan offers in real time, these agents streamline the end-to-end lending process.
Portfolio management is being transformed as agentic AI analyzes real-time market data alongside regulatory guidelines to automatically adjust asset allocation and rebalance portfolios. These AI agents proactively identify opportunities, enabling quick response in asset management without needing human input. This introduces a new level of fast responsiveness.
In terms of regulatory compliance, agentic AI is evolving monitoring and enforcement into a real-time operational function. Instead of relying solely on static dashboards and reporting, these AI agents can absorb evolving regulatory requirements—such as changes to KYC policies or capital adequacy standards—and dynamically apply them across systems and workflows.
By seamlessly integrating AI into the know your customer (KYC) processes, the platform ensures regulatory compliance while enhancing efficiency, accuracy and security—ultimately contributing to a smarter, more customer-centric financial sector. In today’s global digital marketplace, streamlined KYC processes are essential for scalable operations. However, digital transformation brings challenges in expanding solutions beyond initial pilot phases. This is especially true for KYC, where legacy systems often impede seamless data integration and interoperability. The benefits of AI agents are not just limited to the stated functions and uses can be customized to help the specific enterprise.
AI agents have helped 82% of financial institutions reduce operational costs. By minimizing manual intervention and streamlining workflows, AI agents enable financial institutions to reallocate resources towards strategic growth initiatives. As AI technology continues to evolve, its impact on cost efficiency and overall productivity is expected to grow even further.
Challenges during agentic AI implementation
Despite its potential, agentic AI brings with it a set of complex challenges. The autonomy of AI agents raises questions about accountability, fairness and security. Decision-making processes in these models can be difficult to explain as they can lack transparency, which can create questions around compliance and trust. Training data biases can worsen inequalities in lending and credit scoring. Furthermore, dependence on autonomous systems can increase an organization’s vulnerability to fraud and other risks if these systems malfunction during crucial times. Without proper safeguards, this can create distrust among customers, regulators and internal stakeholders.
Financial institutions must implement AI agents strategically by adopting responsible AI frameworks, establishing governance practices for ethical oversight, fully utilizing cybersecurity and maintaining a hybrid approach with a human-in-the-loop system can all be put in place to mitigate risks and help solve the uncertainty.
A platform-centric strategy supports an organization’s ability to manage the risks associated with shadow AI agents. A unified AI platform eliminates silos between people, processes, data and technology and provides visibility at the enterprise level over all agentic deployments, intentional—as well as rogue agentic deployments. The end-to-end transparency enables an organization to discover and manage every AI actor in the system and address compliance blind spots and security holes.
The next step for financial institutions
The financial services industry is anticipated to significantly increase its use of AI agents. These agents will autonomously manage complex decision-making in fraud prevention, risk assessment, tailored customer support and compliance. Beyond optimizing internal workflows, AI agents will reshape customer interactions with financial institutions, offering more user-friendly, secure and efficient banking experiences.
To experience widespread transformation through AI agents, financial institutions should embrace a platform-centric strategy. By leveraging unified AI platforms, these firms can implement agentic AI across various business areas while ensuring consistent governance, security and compliance. Establishing connected enterprises that combine AI-driven operations with human insights, financial institutions can enhance their ability to digitize processes, streamline customer interactions, securely share data and automate intricate tasks.
Building platforms on a responsible AI framework is more important than ever. Establishing strong guardrails ensures that autonomous agents operate safely, ethically, and transparently, preventing unintended or rogue behavior. Using a poly-AI approach further improves the platform by allowing companies to integrate and manage the best models for specific tasks, maximizing efficiency across different functions. Additionally, platforms should provide complete visibility into agent actions. Adding human oversight allows for timely interventions, helping maintain accountability and trust during the system’s operation.
This strategic integration ultimately delivers what matters most which is improved organizational resilience, enhanced customer experiences, industry-leading process efficiency, and the agility to develop new business models in an increasingly competitive digital economy. Institutions that embrace this approach will likely find themselves at a significant competitive advantage, better positioned to navigate the complex challenges of modern banking while delivering superior value to customers.
Arvind Rao is Chief Technology Officer, Edge Platforms at EdgeVerve.
