Financial agent security platform

Your AI agent can already spend money. We secure every decision it makes.

One platform, three decisions, one lifecycle — identity & authorization, payment risk & token issuance, and dispute management, covering an agentic transaction from first intent to final settlement.

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Three decisions · one lifecycle

01 · Before AgentTrust 02 · At Payment Risk & Token Issuance 03 · After Dispute Management

Decision API — sample output

The problem

The identity graph changed under the industry's feet

Card-era fraud tools evaluate a consumer. Agentic commerce adds a principal, a developer, a runtime, a model, a wallet, and an intent — all before the transaction a card network actually sees.

Then — what commerce verified

Consumer→ email→ device→ card→ history

Now — what agentic commerce has to verify

Principal→ developer→ runtime→ wallet→ behavior→ intent→ transaction

A valid payment credential no longer means a valid autonomous transaction.

The thesis

P(loss) = f(P, A, U, I, B, M, T)

The atomic unit of agentic-payment risk is no longer the transaction alone. Agent Secure in Finance evaluates the full relationship — before any money moves.

The platform

Three decisions, one lifecycle

Agent Secure in Finance is the financial agent security platform for agentic commerce — one continuous lifecycle covering an agent-initiated payment from first intent to final settlement.

01 · Before the transaction

AgentTrust — identity & authorization

Agent identity, delegated authority, intent match, and behavioral trust, scored before the payment is attempted.

See the decision API →

<100ms

02 · At the transaction

Payment risk & token issuance

Instrument risk, stablecoin & wallet safety, scored on every rail.

See rail coverage →

03 · After the transaction

Dispute management

Evidence, assembled before the chargeback lands.

See the evidence model →

End-to-end

Two engines, one continuous decision

AgentTrust validates the agent before checkout ever starts. Only once it's cleared does a payment credential reach the second engine, which scores that specific instrument's risk before a token is issued.

Engine 01

Trust & Authorization Engine

Identity, authority, intent, and behavior — all seven signals scored before checkout even starts.

Handoff

Checkout · Payment Credential

Only a validated agent reaches this step. It presents a payment credential to begin the transaction.

Sink

Declined, before checkout

An agent that fails validation never reaches payment — dropped here, safely.

Engine 02

Token Issuance Risk Engine

Instrument and wallet risk, scored on the specific credential presented, before a token is issued.

Sink

Token declined

A high-risk instrument never gets issued a token — dropped here, safely.

Output

Rail-independent issuance

Card, ACH, RTP, wallet, or stablecoin — same risk framework, every rail.

Walkthrough

Same agent, same wallet, three outcomes

An intent — “buy 20 engineering software licenses for under $5,000” — and the three transactions that follow it.

TXN — 01Approve

Software Vendor

$4,200

94trust 97%intent match 8risk

TXN — 02Decline

Crypto Exchange

$4,800

Category prohibited
Intent mismatch (11%)
Behavioral anomaly

TXN — 03Step-up

Software Vendor

$4,700

New runtime
Abnormal API sequence
Unusual transaction velocity

Rail-independent

One risk framework. Every rail.

Agent Secure in Finance applies the same identity, authority, and risk evaluation whether the money moves over a card network or a stablecoin.

Card ACH RTP FedNow Wallet USDC USDT

Early access

The trust layer machine commerce doesn't have yet.

We're working with a small set of design partners — agent platforms, merchants, and payment teams who are already watching agent-initiated transactions move through their systems.

hello@agentsecureinfinance.ai Back to top

Pre-seed · building with early design partners · name in progress

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01 Before the transaction

AgentTrust — Identity & Authorization

Every agent gets a persistent identity bound to its developer, runtime, and credential. Every payment is checked against what the principal actually delegated — and against what they actually asked the agent to do.

IDAgent Identity — agent_id persisted across developer, runtime, version, and credential history.
AUTHDelegated Authority — spending limits, category & geography rules, human-approval thresholds.
MATCHIntent Match — the requested payment scored against the original instruction, not just the credential.
BHVRBehavioral Trust — tool-call sequences, retry patterns, velocity, and runtime drift, scored like a fingerprint.
RPTAgent Trust Score — a persistent reputation that compounds across every merchant the agent touches.

POST /v1/transactions/evaluate94ms

{
  "decision": "APPROVE",
  "risk_score": 0.07,
  "agent_trust_score": 94,
  "intent_score": 0.97,
  "behavior_score": 0.93,
  "reasons": [
    "Verified agent",
    "Within delegated authority",
    "Strong intent match"
  ]
}

See AgentTrust in your stack.

Agent identity, delegated authority, intent match, and behavioral trust — scored before the payment is attempted.

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02 At the transaction

Payment Risk & Token Issuance

A verified agent can still carry a stolen card, a provisioned token with no history, or a wallet pointed at a substituted beneficiary. Agent Secure in Finance evaluates the consumer, the instrument, the agent, the merchant, and the transaction together — not just the agent holding the credential — and stays agnostic about which rail wins.

CNSRConsumer Risk — chargeback frequency, refund rate, return behavior, account-takeover indicators.
INSTPayment Instrument Risk — provisioning age, issuer & authorization pattern, card-testing detection.
CHAINStablecoin & Wallet Safety — token & contract validation, beneficiary-change and address-poisoning detection.
MRCHMerchant & Beneficiary Trust — merchant age, dispute rate, category consistency, vendor-relationship status.
TXNTransaction Risk — purchase velocity, price deviation, intent mismatch, settlement finality.

Representative caseDecline

An approved vendor's wallet is 0xABC, but the submitted destination is 0xXYZ — changed two hours earlier, sourced from an unverified email. Agent Secure in Finance declines the 18,000 USDC transfer before it settles instead of letting an irreversible beneficiary-mismatch payment go through.

$500 transaction × 6% chargeback probability$32

+ return-abuse exposure (12% probability)$18

Total expected loss$50

Estimated merchant margin$90

Expected contribution margin$40

Rail	Success	Cost	Settlement
Card	96%	2.2%	T+1
ACH	91%	$0.30	T+1
USDC	99%	$0.03	Seconds

How the decision connects

Five risk dimensions, one explainable decision

Consumer, payment instrument, agent, merchant, and transaction risk all feed one decision API before checkout — the same explainable reason codes whether the outcome is an approval, a step-up, or a decline.

Agent platform or merchant checkout

↓

Agent Secure in Finance decision API

↓

Consumer risk

Payment instrument risk

Agent trust

Merchant & beneficiary risk

Transaction & policy risk

↓

Risk aggregation + intervention selection

↓

Approve

Step up or request human approval

Decline or manual review

↓

Processor, bank, or stablecoin wallet

Score every instrument, on every rail.

One risk framework for card, ACH, RTP, wallet, and stablecoin — applied before a token is ever issued.

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03 After the transaction

Dispute Management

When a transaction is disputed, the instruction, the agent action, the payment, and the fulfillment record usually live in four different systems — fragmented across agent platforms, payment processors, merchants, shipping providers, and card-network dispute tools. Agent Secure in Finance keeps them on one timeline, with source, timestamp, and hash preserved for every fact, so the case doesn't have to be reconstructed from scratch.

EVDEvidence Ingestion — source, timestamp, hash, and verification status preserved per artifact.
TMLUnified Timeline — instruction → agent action → payment → fulfillment → claim, on one record.
CASECase Management — an agent-specific dispute taxonomy with an exportable, source-backed evidence package.
RESPPrepare Dispute Response — when existing evidence already supports the merchant's response workflow — signed authorization, matching order details, confirmed delivery.
REQRequest Additional Evidence — flags exactly which source artifact or merchant record is missing before a case can be assembled.

Representative casePrepare Dispute Response

Signed intent, exact product, approved merchant, matching price, confirmed delivery, and prior undisputed orders are all on file. When the customer later disputes the charge, Agent Secure in Finance assembles a source-backed evidence package for the merchant's existing dispute channel — no manual source-hopping across agent platform, processor, and fulfillment systems.

$750 order × 8% dispute probability × $900 avg. dispute$72

+ return-abuse exposure$6

Total expected exposure$78

− expected cost reduction from human confirmation−$54

+ confirmation friction cost$12

Net expected exposure$36

How the evidence connects

One evidence graph, from first signal to final outcome

Agent-platform intent, merchant orders and fulfillment, processor and network signals, and consumer instrument history all normalize into a single evidence graph — so the dispute case draws from the same record as the original approve-or-decline decision, not a separate reconstruction after the fact.

Agent platforms and intent records

Merchant orders and fulfillment

Processor and network signals

Consumer and instrument history

↓

Evidence ingestion + source verification + normalization

↓

Agent transaction evidence graph

↓

Intent and authorization analysis

Dispute and consumer-abuse risk

Evidence integrity and policy rules

↓

Loss-aware decision + intervention engine

↓

Approve, confirm, hold, or decline

Fulfillment and refund safeguards

Dispute evidence and investigation

↓

Outcome feedback: disputes, refunds, returns, approvals, and final loss

Walk into every dispute with the case already built.

Source, timestamp, and hash preserved for every fact on the timeline — from instruction to claim.

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Approach

LLMs don't make the irreversible call

Hard financial authority should never depend solely on a language model. Intent is extracted upstream; the transaction-time decision runs on deterministic rules and calibrated models.

Natural-language intent→ LLM→ Structured constraints→ Stored policy Transaction arrives→ Deterministic + ML engine→ Decision

Policy

Deterministic

Spending limits, category and geography restrictions, credential validity — rules, not probabilities.

Intent

LLM + embeddings

Extraction and semantic matching of what the principal actually asked for.

Behavior

Isolation Forest → sequence models

Statistical baselines first, moving toward behavioral embeddings as data accrues.

Transaction

Gradient-boosted trees

Calibrated risk scoring on transaction-level features at decision time.

<100ms

p95 decision latency (target)

99.9%

API availability (target)

>95%

intent-extraction accuracy (target)

Built for decisions that can't be undone.

Deterministic policy, calibrated models, and an LLM that only ever extracts intent — never approves a payment.

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