As the financial services sector continues to evolve in the wake of technological advancements, a new player has entered the field: Agentic AI. Following the waves of Blockchain, Robotic Process Automation (RPA), and generative AI, agentic AI is poised to revolutionize the industry. But is it truly the next big thing, or just another passing trend? Let’s delve into the potential of agentic AI in financial services and explore its distinctive features.
Understanding Agentic AI
Agentic AI refers to autonomous artificial intelligence systems capable of performing tasks, making decisions, and interacting with their environment without constant human oversight. Unlike traditional AI models or generative AI, agentic AI emphasizes goal-oriented behavior and adaptive decision-making. These systems can execute actions in real-time, learning and optimizing their performance through continuous feedback.
The key distinction between agentic AI and its predecessors lies in its autonomy and ability to navigate complex, dynamic environments. While Gen AI excels at creating content based on learned patterns, agentic AI takes this a step further by actively engaging with its surroundings and making informed decisions to achieve specific objectives.
The Capabilities of Agentic
The capabilities of agentic AI are garnering much attention, and deservedly so. I see the most critical — and most exciting — capabilities of agentic AI as follows:
- Autonomy: The ability to take goal-directed actions with minimal human oversight
- Reasoning: The ability of contextual decision-making to make judgment calls and weigh tradeoffs
- Adaptable planning: Dynamic adjustment of goals and plans based on changing conditions
- Language understanding: Comprehending and following natural language instructions
- Workflow optimization: Fluidly moving between subtasks and applications to efficiently complete processes
Together, these features enable agentic AI to operate autonomously, proactively, and intelligently, increasing its ability to perform complex workflows across the enterprise.
Agentic AI in Financial Services
The potential applications of agentic AI in financial services are both vast and transformative. Here are some key areas where agentic AI could make a significant impact:
Automated Financial Planning and Analysis. Agentic AI systems can continuously analyze data to provide real-time budgeting, forecasting, and scenario analysis. This enables more accurate and dynamic financial planning, adapting to market changes and business needs in real-time.
Advanced Fraud Detection and Prevention. By autonomously monitoring transactions and detecting anomalies, agentic AI can predict and prevent potential fraud with greater precision than traditional methods. This proactive approach could significantly reduce financial losses and enhance security.
Sophisticated Credit Risk Assessment. Agentic AI can assess creditworthiness and monitor borrower risk continuously, making lending processes faster and more reliable. This could lead to more accurate risk pricing and improved loan portfolio management.
Intelligent Portfolio Management. In investment banking and asset management, agentic AI could autonomously manage portfolios, executing trades based on real-time market analysis and predefined investment strategies.
Streamlined Regulatory Compliance. This might be one of my favorite use cases for agentic AI, especially in the financial services industry. By continuously monitoring regulatory changes and business operations, agentic AI can ensure compliance with financial regulations, automatically generating necessary reports and flagging potential issues and working proactively to help ensure compliance.
Broader Applications in Shared Services
Beyond core financial functions, agentic AI has the potential to revolutionize shared services within financial institutions in myriad ways. Some that immediately come to mind include:
Customer Support Enhancement. AI-powered virtual assistants can provide consistent and immediate support for routine queries across multiple departments, improving customer satisfaction and reducing operational costs.
HR and Payroll Automation. Streamline recruitment, payroll processing, and benefits administration, improving accuracy and reducing the administrative burden on HR departments.
Procurement and Supply Chain Optimization. Managing vendor relationships, optimizing inventory, and automating reordering processes, can help reduce costs and ensure supply continuity.
Comprehensive Data Management and Analytics. Agentic AI systems can consolidate data from various sources, ensure accuracy, and provide advanced analytics for better decision-making across the organization.
The Road Ahead for Financial Institutions
As financial institutions consider adopting agentic AI, several factors will be crucial for successful implementation. These include:
Infrastructure Readiness. Organizations will need to assess and potentially upgrade their technological infrastructure to support agentic AI systems.
Ethical Considerations. Developing robust governance frameworks to ensure responsible and ethical use of agentic AI will be paramount.
Workforce Adaptation. Reskilling and upskilling initiatives will be necessary to prepare the workforce for collaboration with agentic systems.
Regulatory Compliance. As agentic AI takes on more autonomous decision-making roles, ensuring compliance with existing and emerging regulations will be critical.
While the full potential of agentic AI in financial services is yet to be realized, its promise of enhanced efficiency, accuracy, and decision-making capabilities makes it a technology worth watching. As with any emerging technology, careful evaluation and strategic implementation will be key to harnessing its benefits while mitigating potential risks.
UiPath on Agentic AI
I was talking with the team at UiPath recently about agentic AI and exploring where they see agentic AI delivering significant value for customers. Knowing they have many customers in the financial services sector, I asked for if there was early customer feedback or insights they could share. Chris Janiszewski, director of analyst relations, shared that based on customer conversations and feedback with regard to agentic, UiPath’s financial services customers are excited about integrating agentic for a number of use cases, especially those designed for accelerating onboarding, document processing and completion processes, and application verification processing. Janiszewski shared that the combination of AI and automation together make these use cases possible and said the team at UiPath is as excited as their customers about the value they can deliver.
UiPath has long been a leader in helping customers embrace the modern automation journey. Phase one of that journey was all about simplifying and democratizing RPA, phase two was centered on leveraging RPA toward broader enterprise automation with process mining and low code to accelerate RPA and software robot adoption more broadly.
We are now in phase three, which builds on phases one and two, bringing AI, NLP, LLMs, and integration together to create digital assembly lines of workflow automation. These assembly lines are significant, as they leverage what we call the “plumbing” UiPath has put in place over the years with trusted worker bots, which are directed by agents.
I see this foundation that UiPath has built as being a significant advantage, certainly with customers, who are eager to begin their agentic AI proofs of concept. Time will tell on that front, but UiPath is off to a good start.
Conclusion: Agentic AI is the Next Gen of AI in Financial Services
In conclusion, agentic AI represents a significant leap forward in the evolution of artificial intelligence in financial services. Its ability to autonomously navigate complex financial environments and make informed decisions positions it as a powerful tool for innovation and competitive advantage. As the technology matures, financial institutions that successfully integrate Agentic AI into their operations stand to gain substantial benefits in efficiency, customer service, and overall performance.
And beyond financial institutions, I will say with confidence that the future is, most assuredly, one powered by agentic AI — I’m very much looking forward to watching this gain traction.
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