AI Agents for Financial Firms
Our AI Agents help financial firms attract more investors and improve operational efficiency
Implementing AI in your business doesn’t mean turning your entire company upside down. In today’s changing digital world, staying ahead means implementing smart changes. We help you with this by using AI Agents in a way that works for you. Our Operations AI Agents enhance operational efficiency and our Investor AI Agents improve customer interactions. Want to see an Investor AI Agent in action? Try FundNavigator AI, which makes it easy for investors to compare funds and discover which align with their preferences.
We equip AI Agents with customised features trained on your data. For instance, data retrieval via RAG, built-in calculators, content writing capabilities, natural language processing, task automation and more.
We integrate Investor AI Agents into your website and apps so clients can interact with them easily. For Operations AI Agents, we provide a secure link where your team can log in with a username and password to access the agent.
- More efficient operations, saving you time and money
- Enhanced client interactions, which make clients happier, attract new investors and provide valuable user interaction insights
- Demonstrating innovation in a changing world, which builds trust and confidence
Explore use cases for financial firms
Discover a few examples of how AI Agents enhance investor interactions and boost operational efficiency. These use cases highlight what’s possible, but the opportunities don’t stop here. Have a specific need in mind? Feel free to reach out to us.
Advanced Fund Comparisons
Smart Investment Simulations
FundScenario AI helps future investors and current clients get a better feel for potential investment outcomes. By simulating various market scenarios, it offers deeper insights into fund performance. Let’s build FundScenario for your firm.
Automated KID Compliance Check
KID Compliance AI checks if a fund’s KID meets ESMA regulations. We built it to replace the slow, manual review processes. Now, we simply upload the KID and the AI scans it line by line against ESMA regulations. We use it to identify fund managers who might benefit from our KID risk management services. This is a great example of how an AI Agent significantly improved our operational efficiency. Want your KID checked? Request your KID compliance check.
How we develop the AI Agents
Step 1: Define the AI Workflow
We analyse the problem and sketch a solution, as shown in the diagram. This helps us map the process to define sub-agents and select the best AI models, like OpenAI and Gemini models.
Step 2: Implement & Train
We develop and deploy the workflow using Python-based frameworks and APIs, which brings the agent to life. It is then trained with your firm’s data to ensure accuracy.
Step 3: Launch
We integrate Investor AI Agents into your website or app for client use. For internal use, we provide a secure login link.
Ensuring Data Privacy & Security
You have full control over which data our AI Agents use, ensuring information stays protected. Also, our AI Agents connect to AI models via secure APIs, rather than general AI interfaces like ChatGPT. Using APIs provides key security advantages – your business data is not used for AI training and all interactions follow strict encryption and security standards. Learn more about OpenAI’s API security here. If needed, we can connect the Agents to open-source models, offering even more data privacy. However, since open-source models often perform worse than closed-source ones, we only recommend this for highly sensitive data.
At Amsshare, we take data privacy seriously. Our approach ensures that you stay in control, maintain security and use AI solutions that align with your privacy needs.
Explain How The Diagram Works
This AI Agent is designed to assist investors in analysing investment funds of financial firms. The diagram shows how the AI agent, powered by a Large Language Model (LLM), processes user queries while remembering past interactions via an SQL database.
Agent 1 acts as a routing agent, directing queries to specialised sub-agents based on the type of request:
- Agent 2 retrieves data on individual investment funds.
- Agent 3 compares multiple investment funds.
- Agent 4 handles investment calculations and projections.
How it works – example queries:
- “What was the return of Fund X in 2024?” → The routing agent (Agent 1) sends this query to Agent 2, which retrieves the data via Retrieval-Augmented Generation (RAG). Agent 5 then processes the data and formulates an accurate answer.
- “How do the costs of Fund X and Y differ?” → Agent 1 sends this query to Agent 3., which retrieves and compares fund costs using RAG. Agent 5 then delivers a concise comparison.
- “If I invest €10.000 in Fund X, what’s it worth in 10 years?” → Agent 1 directs this request to Agent 4, which runs investment projections using calculation tools. The result is verified by the Conditional Node, ensuring accuracy before delivering the response. If needed, the system loops back for recalculations.
This is a simplified example. The system can be customised with enhanced features, additional sub-agents or other specialised tasks.
Let's develop your custom AI Agent
Thinking about using AI in your financial firm but not sure where to start? No worries, we’re happy to help.
We build custom AI Agents – a great way to introduce AI at your own pace, without turning your company upside down. Whether you just have a few questions or are ready to take the next steps, feel free to reach out to us. Fill out our contact form or email us to get started.
