What is function calling in AI Agents? A Guide for developers in fintech

Function calling is a pattern where an AI model decides when to invoke a specific external function, passes structured arguments to it, and uses the result to continue the task. In AI agents, function calling is how the model turns a user request into an action like checking an account balance, creating a claim, or fetching transaction data.

How It Works

Think of it like a bank teller with a clear internal playbook.

The customer says, “Can you check whether my card payment to ACME went through?” The teller does not guess. They look at the request, decide which internal system to call, pass the right details, wait for the result, and then respond with an answer.

That is function calling in practice:

• •The user asks something in natural language.
• •The model interprets intent and decides whether it needs help from a tool.
• •The model emits a structured call, usually JSON-like arguments.
• •Your application executes the real function against your systems.
• •The result goes back to the model so it can produce the final response.

For fintech teams, the important part is that the model does not directly “do” banking logic. Your code does. The model only chooses and formats the call.

A simple flow looks like this:

User: "What's my available balance after pending card charges?"

Agent:
1. Detects need for account data
2. Calls get_account_summary(customer_id)
3. Receives balance + pending charges
4. Explains result in plain language

Here’s a concrete tool definition:

{
  "name": "get_account_summary",
  "description": "Returns current balance, pending transactions, and available balance",
  "parameters": {
    "type": "object",
    "properties": {
      "customer_id": { "type": "string" },
      "account_id": { "type": "string" }
    },
    "required": ["customer_id", "account_id"]
  }
}

And here’s what the model might send:

{
  "customer_id": "cus_10291",
  "account_id": "acc_88421"
}

The analogy I use with engineers is this: function calling is like an API gateway plus a dispatcher. The model acts as the dispatcher deciding which service should handle the request, but your backend remains the source of truth.

Why It Matters

• •It reduces hallucinations by forcing the agent to retrieve real data instead of inventing answers.
• •It makes AI usable inside regulated workflows where every action must map to an auditable backend operation.
• •It lets you keep business logic in deterministic services while using AI for intent detection and conversation handling.
• •It supports safer automation because you can constrain exactly which actions the agent may take.

For fintech specifically, that matters because most useful AI features sit close to money movement, customer identity, risk checks, or policy decisions. Those are not places where free-form text generation is acceptable.

Function calling gives you a clean boundary:

• •The model handles language.
• •Your services handle rules.
• •Your logs handle auditability.

Real Example

Let’s say you are building an insurance assistant for claims intake.

A customer types: “I had water damage last night. Can I start a claim and see if my policy covers it?”

Without function calling, the assistant might give a vague answer based on general policy language. With function calling, it can do something useful and controlled:

1. •Check policy coverage for water damage.
2. •Verify whether the policy is active.
3. •Create a draft claim if coverage exists.
4. •Return next steps with exact references.

Possible tools:

{
  "name": "check_policy_coverage",
  "parameters": {
    "type": "object",
    "properties": {
      "policy_number": { "type": "string" },
      "incident_type": { "type": "string" }
    },
    "required": ["policy_number", "incident_type"]
  }
}

{
  "name": "create_claim_draft",
  "parameters": {
    "type": "object",
    "properties": {
      "policy_number": { "type": "string" },
      "incident_date": { "type": "string" },
      "incident_type": { "type": "string" },
      "description": { "type": "string" }
    },
    "required": ["policy_number", "incident_date", "incident_type"]
  }
}

Conversation flow:

• •User: “I had water damage last night.”
• •Agent: asks for policy number if missing.
• •Agent: calls check_policy_coverage.
• •Backend returns: water damage covered under standard home policy, subject to deductible.
• •Agent: calls create_claim_draft.
• •Backend returns claim ID.
• •Agent: responds with claim number and next steps.

This is better than letting the model improvise because:

• •eligibility checks stay in your rules engine,
• •claim creation stays in your claims platform,
• •customer-facing language stays conversational.

For banking teams, same pattern applies to things like:

• •fraud flag lookup,
• •card replacement requests,
• •beneficiary validation,
• •payment status checks,
• •dispute initiation.

Related Concepts

• •Tool use — broader term for giving models access to external actions beyond text generation.
• •Structured outputs — forcing responses into schemas so downstream systems can parse them reliably.
• •Agent orchestration — coordinating multiple steps, tools, and decision points across one workflow.
• •RAG (Retrieval-Augmented Generation) — pulling documents or records into context before generating an answer; useful when function calling needs supporting evidence.
• •Guardrails — policy checks that restrict what tools can be called, when they can be called, and what data can be exposed.

If you are building AI agents in fintech, function calling is one of the first patterns worth getting right. It is the difference between a chatbot that talks about banking and an agent that can safely work with banking systems.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.