AI Agents for Financial Services
We build agents that work inside regulated operations: servicing queues, alert triage, onboarding checks, claims files. They call your core, your case system and your screening vendor through tool contracts you approve, and the ledger stays the record of truth. The hard part is not the model. It is that every action has to be reversible, attributable to a named approver, and reconstructable months later when someone asks who decided.
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Teamvoy has delivered AI transformation and engineering work for Nasdaq, Panasonic, OSL, Iress, Afriland First Bank, and 150+ other companies.
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AI Agents for Financial Services Success Stories
Check out our product and experience design case studies. Learn how we’ve helped businesses like yours launch stunning solutions and succeed in target markets.
Eliminating the 90% Noise: How Teamvoy Re-Engineered Trade Surveillance Software for a Global Exchange
High-Performance Web Platform for Scalable Trade Surveillance
Banking App Refactoring & Performance Optimization: 10× Throughput at a Central African Consortium
AI Agent Services We Offer
Six offerings. Each one is scoped separately, starting from an intent inventory rather than a platform.
Customer Servicing Agents
Agents that work a servicing queue: read the case, gather the evidence, draft the action, and hand it to an advisor to approve. Every tool call the agent can make is a typed contract with an idempotency key and a scope check, so a retry cannot issue a second refund.
Financial Crime Alert Triage
Agents that assemble the evidence pack behind an AML or fraud alert and draft a disposition for the analyst. The draft carries the rule that fired and the records it was based on, so the analyst reviews evidence rather than a conclusion, and nothing reaches the case system until a human signs it.
Onboarding and KYC Agents
Agents that collect, check and chase documents across your KYC vendor, your CRM and the core. Every automated check records the document version and the vendor response ID it was based on, which is what a remediation exercise two years later actually needs.
Claims and Underwriting Support
Agents that build the claim or submission file, flag the missing evidence, and draft the decision letter for a human. Pricing and eligibility stay in your rating engine: the agent assembles the file and explains the outcome, it does not set the price.
Back-office Operations Agents
Agents for reconciliation breaks, payment repairs and investigations, where the work is reading four systems and proposing one correction. The agent proposes, your payment API executes behind a maker-checker step, and the ledger is never written to directly.
Agent Platform and Guardrails
The part that is not any single agent: the tool gateway, policy evaluation, the trace store, the eval harness and the cost controls. Policy is evaluated per tool call against your entitlement model, never written into the prompt where a copy edit can widen it.
One agent run, seven layers
It looks like one action: a request in, an answer out. It is seven handovers between systems and responsibilities, and each one fails differently in production than it does in a demo.
01. Intake and identity binding
A request arrives from a chat surface, a mailbox or an alert queue and is bound to a verified customer, an account and an entitlement scope before any tool is exposed to the model. Breaks when: identity is resolved once at the start of a session and reused. The customer switches to a second account halfway through and the agent reads it under the first account’s scope.
02. Policy evaluation
Before each tool call, the request is checked against what is permitted for this product, jurisdiction, customer segment and channel. Breaks when: the policy lives in the system prompt. Someone edits a paragraph to improve the tone and quietly widens what the agent may approve, with no diff that anyone in Risk would recognise as a control change.
03. Retrieval over the record
The agent grounds its answer in policy wording, product terms, transaction history and prior cases, filtered by what this customer and this agent are entitled to see. Breaks when: the index rebuilds nightly. A fee schedule changes at midnight and the agent quotes yesterday’s rate to every customer until the next build finishes.
04. Planning and tool selection
The model proposes a sequence of tool calls. The runtime, not the model, decides what is retried, in what order, and when to stop. Breaks when: a call times out, the model retries it, and the tool has no idempotency key. The refund goes out twice and the second one stays invisible until reconciliation.
05. Execution against systems of record
The calls run against the core, the card processor, the case system and the screening vendor. The agent proposes and your systems dispose. Breaks when: half the plan completes. The case note is written, the ledger call fails, and the agent reports success because the last message it saw was a success.
06. Human checkpoint
The action, the evidence behind it and the alternatives are put in front of a named approver, with the reversibility of the action deciding whether approval is required at all. Breaks when: the reviewer sees the conclusion and not the evidence. Approvals converge on [XX]% acceptance across [N] reviews a week, and the control is decorative.
07. Write-back and evidence capture
The decision is written to the case and the ledger, and the run is stored with its model version, prompt version, retrieved documents, tool responses and approver. Breaks when: only the final answer is kept. A complaint arrives six months later, the model has been upgraded twice, and nobody can reconstruct what the agent saw when it decided.
Not sure which of your queues an agent can
actually be trusted with?
Why choose Teamvoy for AI agents in financial services?
Creating an intuitive, captivating software product that attracts and retains your audience is the key to your business growth. Explore our digital product design services to get the expert support you need:
The engineers who scope the work build the work
No handover from a pre-sales team to a delivery team. The person who tells you what the work will cost is the person who then has to make that estimate true.
Costs us: we cannot start faster than we can staff, so we turn down work that needs a squad this month.
You own the source code from the first commit
The repository is yours, in your organisation, from day one. Not transferred at the end, not held until the final invoice clears.
Costs us: we give up the lock-in that comes from holding a repository, so we have to be re-chosen every quarter.
Every agent ships with a trace before it ships with an interface
The evidence trail is written on the first run, not added when the first complaint arrives: model version, prompt version, retrieved documents, tool responses, approver. A decision you cannot reconstruct in an audit is a decision you will end up reversing.
Costs us: our first demo lands weeks after the one from a vendor who builds the chat window first.
We scope by what an agent can undo, not by what it can do
Intents are sorted by reversibility. Read-only summarisation, draft-for-approval and act-then-notify are three different control regimes, and we tell you which one each of your intents belongs in before anything is priced.
Costs us: we usually propose fewer live intents in year one than the proposal you are comparing us against.
We engineer for:
What our AI agent engineers work in.
Models
Agent orchestration
Retrieval and data
Evaluation, tracing and guardrails
Runtime and languages
Systems we integrate with
Three ways to start.
Pick the smallest one that answers the question you actually have.
AI Outcome Audit
A senior AI engineer reviews your stuck pilots, maps the production gap, and delivers a fixed-price plan tied to a named business outcome. You get clarity — and a price — before you commit.
Sharp Sprint
Two senior AI engineers deployed against a fixed outcome. Working AI in production at the end. No discovery phase. Outcome bonus on success. For buyers who know what they need.
15-min Technical Call
A direct call with a senior AI engineer. We talk about your stuck pilots, the outcome you need, and what we can ship first.
The difference shows up after the pilot.
Most vendors hand you a deck. Teamvoy hands you a working API and a senior AI engineer who stays until the outcome lands. We charge for results, not for hours.
The difference shows up at the production line. An agent failing on real data. A regulator asking how the model decides. A board asking what AI actually changed.
Which of your queues should an agent touch first?
Talk to a Chief Technology Officer on the first call.
