Agentic AI for Insurance Agents & Bank Advisors – The complete guide
Domitille Dien, Head of Product Marketing
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that demonstrate autonomous decision-making and action capabilities. These AI systems are designed to perform tasks, solve problems, or make decisions without constant human intervention. They act as “agents,” operating based on predefined goals, rules, or learned behaviors, and can adapt to changing conditions in real time.
Key characteristics of Agentic AI include:
- Autonomy: The ability to perform actions or make decisions independently.
- Goal-Oriented Behavior: Working towards specific objectives set by humans or the system itself.
- Adaptability: The ability to learn from experiences or data and adjust behavior accordingly.
- Interactivity: Communicating and collaborating with humans or other AI systems.
What’s the difference between Agentic AI (LLM-Agent) and LLMs?
Think of LLMs (Large Language Models) as comprehensive financial encyclopedias. They store extensive knowledge on banking, insurance, and more, offering detailed answers to your inquiries.
Now, imagine LLM agents as financial advisors or assistants. They not only draw from this wealth of information but also take action to support you. They can recommend tailored insurance policies, calculate loan options, send reminders for policy renewals, or even help you prepare for financial reviews.
In this way, while LLMs are invaluable for providing information, LLM agents go further by managing tasks and delivering personalized solutions.
| Feature | Large Language Models (LLMs) | LLM Agents |
| Definition | Advanced AI models trained on vast amounts of text data to understand and generate human-like text. | Systems that use LLMs to perform specific tasks autonomously. |
Primary Function | Text generation, language understanding, translation, summarization, etc. | Task execution, decision-making, and interaction with other systems. |
| Autonomy | Operate based on user input and predefined algorithms. | Can operate independently to achieve goals. |
| Interactivity | Respond to queries and generate text based on input. | Interact with multiple systems and users to complete tasks. |
Use Cases | Chatbots, content creation, language translation, etc. | Virtual assistants, automated customer service, workflow automation. |
| Complexity | High complexity in language understanding and generation. | Higher complexity due to integration with various systems and decision-making capabilities. |
| Examples | GPT-4, BERT, T5 | Zelros Copilot for bank and insurance, autonomous customer service agents |
Agentic AI in the insurance and banking contexts
Agentic AI automates customer interactions, provides personalized recommendations, and manages routine tasks, enhancing efficiency and service quality. In sectors like insurance and banking, where client interactions must be highly personalized and regulatory oversight is stringent, these AI agents are transforming financial services by autonomously handling repetitive tasks, identifying risks, and adapting to new scenarios.
Imagine AI agents collaborating seamlessly under the guidance of a supervising agent, orchestrating interactions and coordinating outcomes efficiently. By combining teamwork with oversight, Agentic AI ensures smooth operations, provides real-time insights, and enhances decision-making. Available 24/7, these agents empower banks and insurers to stay competitive while meeting growing customer expectations and regulatory requirements.
Use case: Agentic AI for insurance producers – Agentic AI can transform the way producers handle policyholder portfolios. With AI agents automatically monitoring policy renewals, claims, and customer behavior, insurance producers can focus on more complex cases and higher-value customer interactions. In the event of a claim, for example, an AI agent can provide pre-filled documentation, anticipate the next steps, and predict potential claim outcomes based on historical data, saving both time and effort.
Agentic AI for bank advisors – Agentic AI brings a similar advantage. It can assist with wealth management strategies, anticipate market changes, and offer clients personalized investment suggestions based on real-time data. AI agents can monitor customer portfolios, flagging any anomalies or opportunities for review, ensuring advisors are always one step ahead when advising clients.
“Agentic AI is the next level of artificial intelligence designed to pursue goals with human supervision. The agent accomplishes work and invokes tools to do so. Agentic AI uses generative AI but goes further than a system of request and response.” – David Vellante, George Gilbert
When shall Agentic AI be used?
Agentic AI is overkill for repetitive tasks that can be described entirely and explicitly with a prompt, or a ‘if / then / else’ chain of actions. In this case, it is safer and more robust to implement directly the desired workflow without an AI agent (a unitary LLM being sufficient enough).
However, there are a lot of situations where a task is ill-defined, unique, and needs several back and forth reasoning steps, verifications, hypothesis testing, research, strategy planning, etc. … (e.g. : Is this company eligible for this commercial line insurance?). This is where Agentic AI is the only solution to automate the task.
Agentic AI and financial services regulation
AI systems in general, and Agentic AI systems in particular, may be subject to particular attention in the context of specific regulation.
Let’s give 3 examples among others in Europe:
- AI act, regulating in particular ‘General-Purpose AI Models with Systemic Risk’ and ‘High-Risk AI System’ like ‘AI systems intended to be used to evaluate the creditworthiness of natural persons or establish their credit score, with the exception of AI systems used for the purpose of detecting financial fraud’
- DORA (Digital Operational Resilience Act), about information and communication technology (ICT) risk management of financial institutions
- IDD (Insurance Distribution Directive) to protect consumers, ensuring that they are given appropriate advice and clear information when purchasing insurance products
In this context, certified Agentic AI systems (e.g. through the recent ISO 42001 AI Management system framework), may be required.
Conclusion – Are we ready for Agentic AI?
“Agentic AI holds tremendous potential for the enterprise sector,” said Damien Philippon, CEO of Zelros. “Unlike consumer AI, where trust is still evolving, enterprise agents operate with clear objectives, a defined path, and the ability to autonomously achieve results faster and more cost-effectively than traditional methods. That said, the human touch remains crucial.”
While the idea of AI working independently may raise concerns about human job displacement, it’s important to remember that Agentic AI is still used as a tool designed to support, not replace, human advisors. The real power comes when AI and humans work together, combining the best of both worlds: the efficiency and speed of AI with the empathy and intuition of human agents.
As banks and insurers increasingly rely on digital transformation, the adoption of Agentic AI will likely become the norm. This AI will help financial services institutions operate more efficiently, respond to customer needs faster, and stay compliant with evolving regulations. The future isn’t about displacement—it’s about empowering advisors to deliver more personalized and informed service. The real challenge is how quickly businesses can adopt this technology to stay competitive.