Agentic AI in retail banking
Over the last two and a half decades, artificial intelligence (AI) and technological innovation have evolved exponentially within the banking sector. Since the early 2000s, AI in banking has evolved from basic fraud detection and customer support to include credit scoring, risk assessment and algorithmic trading. Today, it has become integral to personalized banking, chatbots, fraud prevention, and compliance monitoring.
This article explores the shift from reactive to autonomous, goal-driven AI, focusing on how agentic AI is redefining retail banking by enhancing customer experiences and streamlining operations through proactive decision-making.
How agentic AI is transforming retail banking
This integration marks a shift from experimental to enterprise-wide implementation, with AI in retail banking establishing itself as an intrinsic component of the digital experience. Moving away from pilot programs, some form of AI is now integrated across most banking operations. AI-powered assistants, machine-learning models and fraud detection are now all at the heart of most modern banking infrastructures.
With rising consumer demands for faster digital service, the need for more autonomous, goal-driven AI models is more crucial than ever to enhance the customer experience and ensure optimal efficiency. An article by the IBM Think team explains it best:
“The banking sector, in particular, is increasingly relying on the advantages of AI technologies to remain competitive. Customers want seamless digital banking experiences: apps that anticipate their needs and the ability to interact with people or virtual assistants, depending on the complexity of their issue. Companies need to improve the user experience to keep those customers happy. Adopting and deploying generative AI solutions, coupled with effective data management, is a key step toward that goal.”
What is agentic AI and how is it different from traditional AI systems?
Traditional reactive AI systems perform tasks based on predefined rules or respond to specific prompts. They excel at analysing data and following instructions, but are unable to take initiative. Stepping beyond this and disrupting the status quo, proactive intelligence identifies opportunities and makes decisions autonomously without human guidance.
Generative AI assistants in financial services specifically are helpful tools in providing solutions for users when prompted with specific questions and information. Agentic AI assistants offer a more conversational and dynamic route, programmed to plan and execute tasks in alignment with predefined business goals. In addition to developing a deeper foundation of trust with consumers, autonomous AI agents can deliver faster solutions for more complex issues within the banking sphere.
Agentic AI architecture and design
As discussed, agentic AI models function as intelligent, adaptive systems capable of independent decision-making across sophisticated banking environments, complete with the capacity for continuous interaction with their environment. Unlike conventional AI models, agentic AI models adapt their actions based on contextual feedback and evolving conditions.
Agentic AI architecture typically comprises three core components:
- Perception: Analyzes and interprets data from a range of internal and external sources.
- Planning: Formulates strategies, establishes priorities and determines optimal courses of action.
- Action: Executes decisions, monitors outcomes and refines subsequent behavior through iterative learning.
Key use cases for agentic AI in retail banking
Enhancing customer experience with AI agents
With a clear understanding of the core components of agentic AI in retail banking, attention turns to its impact on customer engagement and the digital experience. By delivering 24/7 service and automating interactions that closely mirror human conversations, agentic AI enables faster issue resolution and more responsive customer support.
Further, these AI agents can offer personalized financial guidance by assessing transaction history, spending patterns and financial goals. By analyzing these, they can offer tailored services such as product recommendations and budgeting tips.
Supporting bank staff
The introduction of AI in banking also streamlines workloads by performing repetitive tasks, from document processing to customer onboarding and fraud detection, all tasks that previously required human input. By automating internal workflows, AI can support bank staff more effectively by allowing them to focus on higher-value tasks and create more agile, efficient banking services. As Gartner predicts, ‘Agentic AI will resolve 80% of customer issues as they arise without human intervention by 2029.’
Autonomous AI agents also factor in decision support systems, a factor we’ve explored in this whitepaper. The capacity to interpret unstructured data, as well as its context, and to make informed decisions without predetermined rules. Leveraging intelligent operations allows banking lenders to spot patterns, predict outcomes and automate tasks based on this data analysis without human input.
AI agents acting on behalf of customers
AI agents provide customer service options through a mobile interface, allowing users to receive direct, immediate solutions wherever they are. Chatbot services that adapt to real-time information and tasks, such as transaction processing and individual inquiries, are available 24/7, allowing for enhanced customer experience, operational efficiency and round-the-clock fraud monitoring.
The employment of millions of autonomous AI agents interacting with banking systems could prompt systematic failures caused by an overload of requests or problems arising from identity and authentication issues. We also need to prepare for ethical issues surrounding accountability and trust. To mitigate such risks, practical safeguards are imperative. Financial institutions must implement agent authentication, monitored activity, and strict human oversight, while also ensuring transparency around AI use and investing in workforce reskilling.
The role of data in agentic AI success
In our whitepaper, we found that 76% of customers and 85% of businesses bypass banks for financial services, putting traditional institutions at risk of significant revenue loss. Using advanced API (Application Programming Interface) integrations, agentic AI can identify customer needs in real time, eventually eliminating product development cycles and offering instant offers to customers.
Working with proprietary banking data trains agentic AI to create tailored solutions unique to the individual and the bank lender’s business goals. Data quality, integration and security are all critical factors that must be considered to ensure reliable and safe operations within the complex banking environment.
To reach more accurate predictions and outcomes, enterprise AI agents must be exposed to high-quality data to carry out unbiased and effective fraud detection. Providing fully integrated data covering a unified view of individual profiles, transaction history, and spending habits allows for sharp adaptability and more holistic solutions. It is also imperative that robust data security is implemented to mitigate ethical violations that may breach consumer trust and compliance regulations.
The above factors are instrumental in building differential, personalized AI experiences, particularly in conjunction with contextual enterprise knowledge. Further, with agentic AI systems, whereby each agent works together to carry out subcomponents of a higher goal, the flexibility and operability can be extended to create an even more efficient customer care ecosystem.
Governance and risk management for AI in banking
When it comes to risk management of AI in banking, serious protocols need to be established to avoid widespread functional, compliance and ethical issues. Governance frameworks and safeguards for autonomous AI agents must be established to maintain stability and operational integrity, particularly as these systems grow more complex.
As outlined in our whitepaper, inadequate policies and controls can lead to unauthorized model changes, biased outputs, regulatory non-compliance and financial mistakes. To prepare for Agentic AI and maintain customer trust, retail banks must apply strict access controls and logging, such as role‑based access controls (RBAC), which ensure that only authorized employees can gain entry.
Preparing for the future of AI in banking
Agentic AI has the potential to overcome current administrative limitations in banking, reduce risk and foster deeper customer relationships. As we have found, retail banks that still rely solely on traditional operational methods and reactive AI may face unprecedented risks and costs.
Technological advancements and increasingly complex regulations are becoming central to the industry's evolution. To prepare for widespread agentic AI in banking, one of the most pivotal adjustments banks must make is updating systems and equipment. Modern, flexible infrastructure not only supports the high-volume, autonomous operations of AI agents but also ensures compliance with regulatory requirements, strengthens security and enables a seamless, responsive experience for customers.
Alongside AI-ready infrastructure, high-quality data must be regularly filtered into systems for seamless, adaptable solutions. Equally as important, strict governance and human oversight must be implemented to monitor against ethical errors and bias. Further, retail banks will need to address mass staff reduction, particularly for white-collar jobs, due to AI automation. As Gartner observes, ‘90% of banking lenders recognize that current workforces will need to be reskilled to work alongside AI.’
As we transition from reactive to proactive AI, retail banks must leverage the opportunity to revamp systems and streamline workflows. Navigating the adoption of agentic AI does not have to be a journey that retail banks have to take alone. By partnering with strategic fintechs like Finastra, modernization can happen at scale. Discover our next-generation Digital, Retail & Commercial Banking solutions.
