How agentic AI is revolutionizing financial strategy and operations
DEC. 16, 2024
Agentic AI: The game-changing technology transforming financial strategy with autonomous intelligence.
The financial services industry often has a reputation for lagging in technology adoption. However, it has made strides in adopting generative AI, AI-powered fraud detection, and conversational analytics to vastly improve how they operate and serve their customers.
Most recently, financial institutions have found themselves in a position to evaluate the benefits of agentic AI systems. Named by Forrester as one of the top 10 emerging technologies of 2024, agentic AI is revolutionizing what teams can accomplish. It autonomously analyzes data, makes layered decisions, and executes complex, multi-step actions. And agentic AI is able to learn continuously from outcomes to improve its operational decision-making and efficiency.
As financial institutions face increasing pressure to optimize operations, reduce costs, and deliver personalized services at scale, agentic AI offers a powerful solution for automating processes while remaining accurate and compliant.
Agentic AI's autonomous decision chains set it apart from traditional AI
Agentic AI goes beyond what traditional and generative AI can deliver with autonomous decision-making and complex task execution. Whereas traditional AI systems rely on predefined rules, and generative AI creates content in different formats based on specific prompts, agentic AI leverages advanced language models to go further.
Using a “chaining” capability, where it can take a sequence of actions in response to a single request, agentic AI can:
- Independently analyze situations and determine appropriate actions
- Learn from outcomes to improve future decisions
- Execute complex, multi-step processes without human intervention
- Adapt strategies based on changing conditions in real time
This autonomy makes agentic AI particularly valuable for financial operations, where speed, accuracy, personalization, and compliance are crucial. Rather than merely assisting human decision-makers, these systems can take initiative and handle entire processes end-to-end, from data analysis to execution and monitoring.
Some early examples of this technology include Microsoft Copilot, a self-called AI companion, and Google AI Teammate, an AI assistant that can manage projects by monitoring company processes.
Growing complexity in finance demands AI that can think and act independently
Today’s financial institutions face growing complexity in operational decision-making. This is largely driven by:
- Increasing data volumes from multiple sources
- The growing need for a robust omnichannel customer onboarding process
- Real-time market fluctuations requiring immediate response
- Complex regulatory requirements
- Growing customer expectations for rapid, personalized service
Traditional decision-making processes, even those augmented by conventional AI, can struggle to keep pace with these demands. With agentic AI, teams can face this head-on by providing continuous, autonomous decision-making capabilities that scale with organizational needs while maintaining consistency and compliance.
Seven ways agentic AI is already revolutionizing financial services
Traditional AI models and generative AI have already played an important role in optimizing core financial services functions, including fraud detection, market analysis, and customer service. Agentic AI systems take these capabilities further, streamlining operational decision-making and enhancing how much impact a financial institution can make on its customers and the industry as a whole.
Dynamic risk analysis now adapts to market changes in real time
Agentic AI is transforming risk assessment with dynamic, multi-dimensional analysis. Its ability to act independently while learning changing contexts means that it can accomplish:
- Real-time monitoring of market conditions and risk factors
- Autonomous adjustment of risk thresholds based on changing conditions
- Predictive analysis of potential future risks so that teams can make informed decisions
- Integration of multiple data sources for comprehensive risk evaluation
It vastly reduces the chance of risk exposure, ensuring that financial institutions can deliver better services to their customers, regardless of their risk appetite.
Self-learning systems are winning the race against AI-powered fraud
Cybercriminals are continually becoming more and more sophisticated. In fact, the use of AI itself has made attacks like social engineering and identity theft much more effective. However, agentic AI’s ability to continuously learn from new data patterns allows it to successfully adapt to evolving fraud tactics. This helps financial institutions significantly improve detection rates and reduce false positives.
Autonomous portfolio rebalancing can outperform traditional methods
Because of their autonomous nature, agentic AI systems can independently rebalance portfolios based on real-time market conditions and individual client goals. This approach has the potential to outperform traditional portfolio optimization methods, as agentic AI can process contextual real-time information.
Every customer interaction makes service smarter and more personal
When used as a customer service feature, agentic AI has the power to vastly improve how financial institutions serve their customers. With real-time market, product, and customer information, AI agents can intelligently interact with customers, providing timely advice and anticipating needs. Plus, agentic AI systems learn from every interaction they have with each customer, allowing them to provide more personalized experiences (and higher customer satisfaction) over time.
Back-office automation frees teams for strategic work
Behind the scenes, agentic AI systems can help automate back-office functions, including payroll, internal communications, and record management. This streamlined automation can help reduce costs while improving efficiency, and redirecting team members’ time to more high-value functions.
Proactive compliance monitoring reduces financial and reputational risk
Financial services institutions operate in a highly regulated industry—and compliance missteps can prove costly both from a financial and reputation perspective. With agentic AI, institutions can keep close tabs on their compliance status, proactively addressing any concerns and reducing the risk of non-compliance. Agentic AI can also help with accurately reporting against specific standards and regulations.
Early adopters of agentic AI will build impactful advantages
The most valuable element of agentic AI is that it doesn’t just focus on creation; it focuses primarily on decision-making. This makes it a vital technology for companies that want to scale quickly and grow their impact. The key to success lies in viewing agentic AI not just as a tool for automation but as a strategic partner in operational excellence and business growth.
For financial services companies, agentic AI empowers them to improve their decision accuracy and speed with real-time data, continuous learning, and access to multiple inputs.
In addition, agentic AI can also support financial institutions to:
- Increase operational efficiency by speeding up processes, reducing the chance of human error, and employing team members for more strategic, high-value tasks.
- Enhance their adaptability to market changes—a common thread over the last few years—and support them in making better decisions for themselves, their partners, and their customers.
- Roll out automated processes that reduce costs.
- Deliver improved experiences to customers, ensuring higher customer satisfaction scores and improving retention rates.
- Enhance risk management and fraud detection with evolving, self-learning methods.
These benefits compound over time as agentic AI systems continuously learn and refine their decision-making capabilities. Financial services organizations that implement agentic AI early will gain a significant competitive advantage, building institutional knowledge and optimizing their systems, while others are just beginning to explore the technology.
Five critical success factors for agentic AI implementation
As with any new technology, agentic AI won’t come without its challenges. To make the most of this next generation of AI, financial institutions must consider the following:
- Data quality and integration: In order to make accurate decisions, agentic AI will need access to data stores that are complete, up-to-date, and easily accessible throughout the tech stack.
- Infrastructure readiness: Tech infrastructure will need to be assessed and potentially upgraded to ensure it can support agentic AI systems. And the agentic AI pipeline will need to have fine-grained observability.
- Ethical considerations: Teams will need to establish robust governance frameworks and parameters for the use of agentic AI. Maintaining human oversight will be key, where appropriate.
- Regulatory compliance: As financial institutions adopt these technologies, regulators are likely to establish specific rules for how they are used. It’s imperative to stay up-to-date with these evolving regulations.
- Change management: Staff should be trained, reskilled, and upskilled as necessary to collaborate with agentic systems.
The key is to view these potential challenges not as barriers but as critical checkpoints in the implementation journey. I believe that teams that proactively tackle these considerations while developing their agentic AI strategy will be better positioned to realize the technology’s full potential. Start by creating a balanced framework that embraces innovation while maintaining appropriate controls and human oversight.
The roadmap to successful agentic AI deployment
If you’re planning on rolling out agentic AI at your organization, a phased approach is wisest to incorporate all facets of a sensible agentic AI strategy.
Phase 1: Infrastructure and data readiness drive initial success
- Evaluate current technological infrastructure
- Assess data quality and availability
- Review existing processes for AI integration
- Identify key stakeholders and champions
Phase 2: Solution selection must align with long-term strategy
- Align AI capabilities with business needs
- Review available AI solutions and their use cases
- Consider scalability requirements
- Assess integration capabilities
Phase 3: Start small, learn fast, scale smart
- Start with high-impact, low-risk processes
- Establish clear, measurable metrics
- Gradually expand successful implementations
- Build on lessons learned
Phase 4: Clear metrics drive continuous improvement
- Track against key performance indicators
- Monitor cost reduction and efficiency gains
- Measure customer satisfaction improvements
- Calculate return on AI investments
Phase 5: Knowledge graphs unlock advanced AI capabilities
- Review existing data
- Determine new avenues for agentic AI use
Financial institutions must act now to lead in the AI-first future
Financial institutions are at an exciting inflection point in their AI journey. Teams are only just uncovering the potential of agentic AI. Looking ahead, the future points to increasingly sophisticated capabilities that will transform how financial institutions operate.
As these systems become more advanced, we’ll see deeper integration into core business processes, with AI agents taking on more complex decision-making roles while working seamlessly alongside human teams. Agentic AI’s ability to learn and adapt is bound to continue improving, enabling even more precise, contextualized decision-making that accounts for nuanced market conditions and individual customer needs.
Financial institutions that take proactive steps to implement agentic AI today will be better positioned to thrive in this evolving landscape. The key to success lies not just in adopting the technology but in fostering a culture that embraces AI-driven innovation while maintaining the human expertise and oversight that define truly excellent financial services.
