How Agentic AI will transform financial services with autonomy, efficiency and inclusion
Agentic AI could advance financial access in marginalized communities Image: Getty Images/iStockphoto
Kieran Garvey
AI Research Lead, Cambridge Centre for Alternative Finance, Cambridge Judge Business SchoolBryan Zheng Zhang
Executive Director, Cambridge Centre for Alternative Finance, the University of Cambridge Judge Business SchoolHunter Sims
Associate Director of Business & Operations, Cambridge Centre for Alternative Finance, the University of Cambridge Judge Business School- Agentic artificial intelligence (AI) goes beyond generative AI (GenAI) by enabling autonomous decision-making, collaboration, and learning to revolutionize financial services.
- While offering efficiency and innovation, Agentic AI raises concerns about labour disruption, privacy, market volatility and governance, necessitating robust oversight and ethical frameworks.
- Agentic AI holds promise for advancing financial access in underserved communities, though global resource disparities and governance challenges must be addressed to prevent inequity.
As AI accelerates, the financial industry faces a transformative era defined by Agentic AI.
Unlike today’s GenAI models, which respond to specific human prompts, Agentic AI can independently perceive, reason, act and learn, without constant human guidance.
Research by the Cambridge Centre for Alternative Finance and the World Economic Forum in 2020 highlighted AI’s growing influence on finance. Building on advancing GenAI, Agentic AI is poised to deepen this impact on financial services.
This development can enhance finance by rapidly processing data, increasing decision accuracy, personalizing customer interactions and adapting to complex market conditions, bringing finance closer to process autonomy.
Agentic AI and ‘human-like’ approach
Agentic AI is now possible because computers have become much better than humans at recognizing images and understanding language. This progress, driven largely by advanced transformer-based technologies, has been highlighted in Stanford’s 2024 AI Index.
GenAI models are trained on enormous collections of text, images, audio, video, and numbers. They can now handle wide-ranging tasks, such as summarizing information, translating languages, answering questions, editing images, creating sounds and transcribing speech.
However, these models are not truly autonomous – they need specific prompts or instructions to produce results.
Enter Agentic AI, a new step forward in AI. Just as human experts take on specific roles and responsibilities, Agentic AI creates groups of independent AI agents. These agents work together using advanced reasoning and planning skills to solve complex, multi-step problems, with large language models acting as their “brains” for decision-making.
Agentic AI is designed to work more like humans by handling tasks independently, collaborating as a team, reflecting on progress and improving through repetition.
By comparison, GenAI depends on human instructions and cannot independently handle complex, multi-step reasoning or coordination. On the other hand, Agentic AI uses networks of agents that learn, adapt and work together – making decisions and improving continuously, much like humans do.
Agentic AI in financial services
Imagine a trading AI agent that analyzes market data and autonomously monitors market trends, deciphers trading signals, adjusts strategies and mitigates risks in real time. Agentic AI will make this increasingly possible, integrating tools via APIs, sensors and advanced reasoning, to respond to new information and data, and automate and enhanceing financial tasks beyond GenAI’s capabilities.
Agentic AI’s increased autonomy, relative to GenAI, enables it to handle repetitive, data-intensive processes. This gives financial institutions and market participants an increased capability to optimize workflows, enhance compliance and improve decision-making, transforming how financial services conduct business and interact with customers in the age of Agentic AI.
As the figure below on use cases of Agentic AI in financial services shows, such applications could advance financial services by delivering autonomous, adaptive and proactive solutions.
In compliance, it could refine risk assessments in real time, dynamically responding to emerging threats and anomalies. In customer engagement, it could augment static advisory models into dynamic financial coaching tailored to individual behaviours. Though still nascent, Agentic AI promises to enhance productivity, precision and decision-making, driving financial services towards deeper process autonomy.
Benefits and new customer interactions
Agentic AI could transform financial services in several key ways:
- Streamlining operations: By automating repetitive tasks such as data entry, compliance checks and transaction processing, Agentic AI boosts productivity and reduces human error, freeing employees for more strategic work.
- Driving innovation: It enables the creation of new financial tools, such as personalized robo-advisors or adaptive asset management systems that adjust strategies in real time based on market changes and customer preferences.
- Enhancing customer interaction: Agentic AI builds on trends, such as open banking and embedded finance, to offer consumers highly personalized AI agents. These agents can manage finances, make optimized decisions and align strategies with individual goals and risk levels, empowering users like never before.
A ‘human above the loop’ approach remains essential, with AI complementing human abilities rather than replacing the judgment and accountability vital to the sector.
”Challenges, risks and uncertainties of Agentic AI
Along with benefits come obstacles. For example:
- Labour market disruption: Agentic AI may reduce roles e.g. data entry, compliance, investment, asset management and auditing. This shift will need reskilling and retraining efforts, as highlighted by OpenAI’s 2023 report and the Forum’s Jobs of Tomorrow report.
- Human oversight: Pawel Gmyrek of the International Labour Organization notes: “A ‘human above the loop’ approach remains essential, with AI complementing human abilities rather than replacing the judgment and accountability vital to the sector.”
- Privacy and cybersecurity: Agentic AI’s reliance on vast amounts of data raises privacy concerns. Balancing personalization with privacy is essential, while its autonomy introduces new cybersecurity risks.
- Market volatility: By lowering barriers to automated market interactions, Agentic AI could increase systemic risks and market volatility. Synchronization of AI-driven decisions may lead to herding behaviour and sudden market swings.
- Governance and regulation: Autonomous AI poses unique governance challenges. Updated regulatory frameworks must ensure accountability, oversight and ethical standards to address biases in decision-making (e.g. in credit underwriting). Transparency is crucial for maintaining trust.
- Explainability: Stakeholders need clear insights into AI agents’ decision-making, particularly in high-risk areas. The European Union’s AI Act offers an initial framework for responsible AI deployment, emphasizing the importance of defining responsibility and liability.
- Fiscal policies and collaboration: The International Monetary Fund suggests “automation taxes” to support workforce adaptation. Collaboration between financial institutions and regulators is essential for a safe, inclusive, and sustainable future in finance.
Agentic AI, financial inclusion and empowerment
Understanding how Agentic AI could advance financial inclusion in emerging economies where traditional banking remains limited is crucial. By harnessing Agentic AI, FinTechs and banks could reach underserved communities cost-effectively.
Agentic AI could autonomously assess micro-loans for smallholder farmers, using local data to evaluate risk without direct human involvement. Similarly, mobile banking powered by Agentic AI could offer personalized, real-time micro-insurance products based on real-time weather data.
It also holds the promise of an “AI leapfrog” effect, enabling these economies to bypass costly infrastructure and access advanced tools directly.
Yet, challenges remain. Large firms in the Global North hold most AI resources, risking market concentration and sidelining local players.
Clear governance frameworks are needed to protect communities and align with financial inclusion and empowerment goals, ensuring the power of Agentic AI will serve the needs of many, not few.
Don't miss any update on this topic
Create a free account and access your personalized content collection with our latest publications and analyses.
License and Republishing
World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.
The views expressed in this article are those of the author alone and not the World Economic Forum.
Stay up to date:
Artificial Intelligence
Related topics:
The Agenda Weekly
A weekly update of the most important issues driving the global agenda
You can unsubscribe at any time using the link in our emails. For more details, review our privacy policy.
More on Emerging TechnologiesSee all
David Elliott
November 25, 2024
