AI Agents for Financial Services

An AI agent in finance is defined by its constraints as much as its capability: what it reads, the limits it works within, the moment it defers to a person. It reaches production only when the structure around it can be trusted as much as the team's own.
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Tecla builds and operates these systems inside your environment, held to the same controls your team answers to.

The shift underway

Financial firms have moved from testing AI to running on it, and the value at stake is now measured in the hundreds of billions. The numbers show where that stands, and what it puts at risk.

$200–340B

in potential annual value for global banking.

Source: McKinsey

91%

of financial firms already use or are testing AI.

Source: NVIDIA

$15.9B

lost to fraud in the US in 2025, a record.

Source: US FTC

Find out where an agent would earn its place in your operation

A scoping conversation identifies the workflow worth automating first, and what it would take to build it. Free of charge.

Scope a Build

The state of AI in finance

Financial services has already crossed the adoption question. What remains is quieter, and harder

No sector has moved faster. Nearly two-thirds of financial institutions now use AI actively, and more than half are already piloting or scaling agentic systems that plan and act across multi-step work.

The interesting number is a smaller one. Only about 16% of banking executives report agentic use cases actually deployed, and roughly one in ten has reached scale. Capability, it turns out, was never the constraint.

What decides whether an agent can be trusted on its own is less the model than the structure around it: what it is allowed to touch, what gets reviewed, where it stops to ask, and who answers for what it does. That is closer to how a firm manages people than how it buys software.

Tecla has spent more than a decade placing senior engineers inside US companies. It builds agents the same way it places people, scoped and supervised, and operates them so they hold up in production long after the demo.

The structure around the model

How a system gets built, run, and owned

The reliability an agent needs comes from the arrangement around it: a scoped brief, a named team, and someone accountable at every stage. This is what that looks like from first call to a system you own.

Sprint

Scope, fixed price

We map the workflow, the data access, the decision limits, and the metrics that will tell you it works, then return a plan and a price before you commit.

AI Solutions Lead

Build

Built into your environment

Senior engineers build the agent in your environment and integrate it with the data and systems you already run.

AI Systems Lead

Run

Monitored and improved

Through Managed Operation we keep the system reliable, report against the metrics set at scope, and improve it as data and workflows change.

Systems Lead + Engineers

Own

Yours, improving

You own the system end to end, and it sharpens over time instead of aging into a tool nobody maintains.

Your team

Where we put it to work

The systems we build across finance

Each area below is a set of agents built for one part of the business, ordered by where demand is heaviest. Every agent takes on a single workflow and runs it inside the systems your team already uses.

Accounting & Operations

The highest-volume back-office work in finance, where manual matching and month-end pressure still set the pace.

Accounts Payable Agent

Reads each invoice, matches it to the PO and receipt across your ERP, and moves clean approvals through while routing only the exceptions to a person.

accounts payable automation · invoice

Reconciliation Agent

Matches transactions across accounts, surfaces each exception with a rationale, and shortens a month-end close that runs on manual spreadsheets today.

account reconciliation automation

Bookkeeping & Close Agent

Categorizes entries, drafts the journal, and keeps the ledger current between closes, leaving the review and sign-off to your accounting team.

ai accounting · bookkeeping automation

Compliance

The regulatory work that carries the heaviest audit burden and the least tolerance for a missed step.

Compliance Monitoring Agent

Screens communications and transactions against your policy set, flags what needs review, and leaves the audit trail an examiner expects to see.

compliance automation · monitoring

Regulatory Reporting Agent

Assembles the numbers for each filing from source systems, checks them against the rules, and drafts the report ahead of the deadline for a person to approve.

regulatory reporting automation

Audit & Controls Agent

Tracks control tests and evidence across the audit cycle, flags gaps, and keeps the documentation an examiner asks for ready before they ask.

compliance audit automation

Financial Crime

Where alert volume outpaces the team, every false positive costs a customer, and a missed case costs far more.

Fraud Detection Agent

Works every alert end to end: gathers the evidence, builds the case, and recommends the call, so an analyst reviews judgment instead of raw data.

ai fraud detection · fraud detection system

AML & Transaction Monitoring Agent

Screens transactions for laundering and mule activity, clears the false positives that flood the queue, and drafts the SAR for a person to file.

transaction monitoring · aml automation

KYC & Identity Agent

Verifies identity, checks documents for tampering, synthetic identity, and deepfake forgery, and clears onboarding cases that used to wait in a queue.

kyc automation · identity verification

Lending & Credit

The decisioning work between an application and an offer, where every output has to be explainable to a regulator.

Underwriting Agent

Gathers the file, runs eligibility, and builds an explainable risk memo for the credit committee, who keep the decision itself.

automated underwriting system

Loan Origination Agent

Collects documents, checks completeness, and moves applications through origination without the manual chase for missing paperwork.

loan origination system

Credit Risk Agent

Scores credit risk on behavioral and alternative data and returns explainable output the committee can act on, never an unexplained decline.

ai credit risk management · credit scoring

Wealth Management

A dedicated set of agents for advisory firms and RIAs, built as its own hub. Client lifecycle, portfolio, planning, reporting, and compliance.

Portfolio Monitoring Agent

Watches thousands of accounts for drift, risk breaches, and rebalancing needs, preparing each action for an adviser to approve.

ai portfolio management · monitoring

Client Onboarding Agent

Runs KYC, form population, and multi-custodian account opening, catching NIGO errors before they stall a new relationship.

client onboarding automation · ria

Client Reporting Agent

Builds performance and portfolio reports across every household on schedule, drawn from live custodial data, ready for an adviser to review.

investment reporting automation

Start the build

Begin with the workflow that matters most

A scoping call maps your highest-value finance workflow and returns what an agent would cover, how success gets measured, and what the fixed-price Sprint to build it costs. One system, measured in production, before you expand.

Scope your first finance agent

Read the use-case guide

Fixed-price AI Systems Sprint. No commitment until scope is confirmed.

What finance leaders ask about AI agents

What is agentic AI in financial services?

Agentic AI describes systems that work through a task the way a person would: reading context, pulling data, deciding, and acting. In finance, an agent can work a fraud alert, an onboarding case, or an invoice end to end. Tecla builds these into your own stack, not as a fixed product.

How is an AI agent different from the automation we already run?

Rules and RPA follow fixed steps and break when the input changes. An agent weighs context, handles exceptions, and explains its reasoning, covering the judgment work scripts cannot. Tecla builds on top of your existing automation, taking over the steps where analyst time goes.

Which financial workflows are the best fit for AI agents?

The strongest fits are high-volume, data-intensive workflows with a clear decision at the end: fraud detection, AML monitoring, KYC, underwriting, accounts payable, and reconciliation. Tecla starts with one, ships it, then expands, rather than attempting everything at once.

Do we own the agents Tecla builds, or license them?

You own them. Tecla builds each system in your environment, on your data, with no platform lock-in. The system runs under your control and access rules, which is usually the first thing a regulated institution asks about.

How does Tecla handle security and compliance for regulated institutions?

Agents run inside your environment, with human-in-the-loop controls, tool-call limits, and audit trails from day one. A person makes the final call on any risky decision, and every action is logged. Compliance requirements are scoped per engagement, not promised in the abstract.

Why do so many AI agent pilots fail to reach production?

A demo is quick to build. A system a regulated team trusts needs real data integration, evaluations, and lasting oversight, which is where efforts stall. Tecla's model targets that gap: a Sprint to scope it, a named pod to build it, and Managed Operation to keep it current.

How long does it take to get an agent into production?

Tecla starts with a fixed-price AI Systems Sprint that scopes and prices the build before you commit. Because each engagement begins with a focused build, the agent reaches your stack in weeks rather than a multi-quarter program, then continues to improve once live.

We have an internal data or engineering team. Where does Tecla fit?

Alongside them. Many institutions have the talent but not the spare capacity to take an agent from prototype to a maintained production system. Tecla builds it with your team, hands over a system you own, and stays on for ongoing operation only where that keeps creating value.

Who works on a Tecla engagement?

A small forward-deployed pod: a US AI Solutions Lead on the commercial side, a senior LatAm AI Systems Lead who owns the technical build and stays your point of contact, and AI Systems Engineers who build and maintain it. US commercial leadership, nearshore technical delivery.

How do we get started with Tecla?

Book a scoping call. Tecla reviews your highest-value finance workflow, the data, and the systems already in place, then returns what an agent would cover, where it plugs in, and what the AI Systems Sprint to build it would cost. No commitment until scope is confirmed.

Have any questions?

Schedule a call to discuss in more detail.

Book a Call

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