Six integrated modules for authorizing, monitoring, and governing autonomous AI agents operating on existing payment rails. From real-time authorization to compliance reporting — everything an issuer needs to safely enable agent transactions.
Every agent transaction traverses a seven-gate verification pipeline — from agent identity resolution through behavioral analysis, budget enforcement, sanctions screening, and post-decision hooks. The Decision Trust Protocol evaluates intent, not just attributes.
Every agent builds a behavioral fingerprint across seven signal dimensions. The KYA trust score evolves with each transaction — good behavior earns trust, anomalies trigger investigation. Behavioral drift is detected before fraud occurs.
Live feed of every authorization decision with KYA scores, risk signals, and one-click investigation. Drill into any transaction to see the full 9-gate pipeline trace — which gates passed, which flagged, and why.
| Agent | Amount | KYA | Status |
|---|---|---|---|
| agt_procure_01 | $2,450.00 | 0.92 | Approved |
| agt_travel_03 | $847.50 | 0.87 | Approved |
| agt_mktg_02 | $15,200.00 | 0.41 | Declined |
| agt_ops_07 | $320.00 | 0.95 | Approved |
| agt_finance_01 | $5,100.00 | 0.78 | Step-up |
| agt_saas_04 | $199.00 | 0.91 | Approved |
Generate SAR narratives and STR reports in FinCEN BSA (US) and Panama UAF Law 23 (LatAm) format. Automated compliance calendar with regulatory deadline tracking. Every report includes the complete decision context.
| Report | Type | Due | Status |
|---|---|---|---|
| SAR-2026-0019 | SAR | Jun 15 | In Progress |
| QMR-Q2-2026 | QMR | Jun 30 | Not Started |
| SAR-2026-0018 | SAR | Jun 02 | Filed |
| STR-2026-0007 | STR | May 28 | Filed |
| PCI-DSS Annual | Audit | Jul 31 | Preparing |
Track trust levels, behavioral risk zone, transaction history, decline rates, and behavioral baselines for every AI agent operating under your program. Promote, throttle, or suspend agents with full audit trail.
| Agent | Trust | Zone | Txns | Drift |
|---|---|---|---|---|
| agt_procure_01 | TRUSTED | GREEN | 1,247 | 0.08 |
| agt_travel_03 | ESTABLISHED | GREEN | 892 | 0.12 |
| agt_mktg_02 | ACTIVE | YELLOW | 341 | 0.38 |
| agt_ops_07 | TRUSTED | GREEN | 2,103 | 0.05 |
| agt_finance_01 | ESTABLISHED | GREEN | 567 | 0.11 |
| agt_saas_04 | VERIFIED | GREEN | 89 | 0.14 |
| agt_hr_02 | REGISTERED | CRITICAL | 12 | 0.82 |
Ask questions in natural language and get instant answers grounded in your transaction data. Ivy understands agents, mandates, trust scores, and compliance context — making every team member as effective as your best analyst.
Traditional fraud models assume human behavior patterns. Agent fraud looks different — prompt injection, credential replay, behavioral drift, and coordinated multi-agent attacks. Our fraud analytics engine is trained on agent-specific signals, not retrofitted from card-present heuristics.
| Alert | Type | Agent | Severity |
|---|---|---|---|
| FRD-0412 | Intent Mismatch | agt_mktg_02 | Critical |
| FRD-0411 | Drift Attack | agt_hr_02 | Critical |
| FRD-0410 | Velocity Spike | agt_saas_04 | Medium |
| FRD-0409 | MCC Anomaly | agt_ops_07 | Medium |
Define, test, and deploy risk rules without code changes. Every rule goes through a dual-approval workflow — propose, review, approve — with full version history. Gap analysis continuously identifies where your rule coverage falls short against emerging agent fraud patterns.
| Rule | Condition | Action | Status |
|---|---|---|---|
| Max single txn | amount > $10,000 | Step-up | Active |
| Velocity 1h | > 20 txns / hour | Decline | Active |
| MCC blocklist | MCC in [6051, 7995] | Decline | Active |
| Low-trust block | KYA < 0.35 | Decline + alert | Active |
| Geo mismatch | agent_country ≠ merchant_country && amount > $5K | Step-up | Pending |
When a fraud alert fires or a suspicious pattern emerges, Case Management gives your compliance team a structured workflow to investigate, document, escalate, and resolve — without leaving the dashboard. Every case links back to the specific transactions, agents, and risk signals that triggered it.
| Case | Subject | Priority | Assignee | Status |
|---|---|---|---|---|
| CSE-0087 | Intent mismatch — agt_mktg_02 | P1 | J. Williams | Escalated |
| CSE-0086 | Behavioral drift — agt_hr_02 | P1 | Compliance | In Review |
| CSE-0085 | Velocity anomaly — agt_saas_04 | P2 | Risk Team | In Review |
| CSE-0084 | Budget overrun — agt_travel_03 | P3 | Unassigned | Open |
| CSE-0083 | SAR filing required — agt_finance_01 | P2 | J. Williams | In Review |
Every module shares the same data layer, the same agent identity graph, and the same behavioral models. Integration is built-in, not bolted-on.
One REST API for authorization, agent management, compliance export, and trust scoring. No middleware. No message queues between services.
All seven decision gates execute in under 25ms. Redis-cached trust scores, in-memory velocity counters, and async post-decision hooks keep the critical path fast.
Program managers see only their agents. Principals control their own API keys, rate limits, and risk thresholds. Tenant isolation is enforced at every layer.
The Decision Trust Protocol works on existing Mastercard and Visa payment rails. No new network required. No merchant-side integration needed.
Designed to help issuers meet their Mastercard principal-member obligations, FinCEN BSA requirements, and Panama Law 23. Not an afterthought — compliance is in the authorization path.
Every scoring model, behavioral metric, and trust threshold is grounded in published research. The EDQS, Decision Trust Protocol, and ABT papers document the methodology.
Whether you're an issuer exploring agent authorization, a program manager building agent-powered financial products, or a compliance team preparing for agentic transactions — we'd like to talk.