Fraud Risk Architecture · Bengaluru, India → Global

Stop AI agents from making decisions that would fail your fraud checks.

Zarelva reviews every AI and automated decision like a senior fraud analyst — flagging fraud risk, false positives, and liability before it impacts customers or regulators.

Catch agent-driven fraud before money moves
Reduce false positives with explainable, defensible decisions
Get audit-ready justification for every high-risk action

For: Fraud & Risk Teams · Fintech · Payments · Lending · AI-driven decision systems

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Payments & Fraud

Detect risky payment approvals and agent-driven overrides before they lead to fraud loss — with an explainable decision your fraud team can act on immediately.

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Lending & Credit

Explain AI credit decisions the way a human credit committee would — with risk factors, liability notes, and audit-ready justification for every approval or rejection.

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Agent Hierarchies

Map how risk propagates when one AI agent delegates to another — identify privilege over-extension and unchecked authority chains before they become compliance gaps.

The Gap

Attestation proves what happened.
Zarelva shows whether it was a safe decision.

Tools like Attestix cryptographically prove that an agent acted with a verified identity, scoped delegation, and a tamper-proof audit trail. That is the evidence layer — the ledger.

But a ledger does not interpret itself. An AI agent can have a fully attested identity and still approve a transaction that your fraud policy would block. Someone has to decide if the decision was right — not just that it happened.

Zarelva is that layer: it evaluates each AI decision for fraud risk, compliance impact, and business liability — in language risk teams, boards, and regulators understand.

Attestix answers

Did the agent act?
Was identity verified? (W3C DIDs)
Was the credential valid? (UCAN scopes)
Tamper-proof audit trail on blockchain

Zarelva answers

Should it have acted, given your fraud policy?
Is this decision pattern suspicious?
Who is liable if it turns out to be wrong?
Does this meet EU AI Act Article 9 & 12?

Together

A complete compliance posture for Annex III fraud and credit AI — from identity proof to decision quality. The only stack that covers both.

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Your AI decisions will be audited — whether you're ready or not

Your systems already make decisions on payments, credit, and access. The first fraud incident or regulatory review won't ask: "Was this logged?" It will ask: "Was this decision reasonable and defensible?" Zarelva answers that before it becomes a problem.

EU AI Act Annex III enforcement: August 2, 2026 — 4 months away.

How to use this demo

Run one real decision through the engine

Pick a single AI or automated decision from your environment and run it through the tools below. You will see the same style of judgment Zarelva applies in client work — fraud exposure, false positive risk, and where liability actually sits.

Who this helps

Fraud & risk teams at fintech, payments, and lending companies.
Marketplace trust & safety teams reviewing onboarding, listings, and payouts.
AI / agent platform teams where LLM agents make or recommend high‑impact decisions.

What you need before you start

One concrete decision event (e.g., “agent auto‑approved a ₹40,000 payout in 3 minutes”).
Basic context: product / journey step, country, channel (web / app / API / agent).
Key signals the system used: KYC outcome, device/IP basics, account age, velocity, amount.
Your current policy expectation (approve / hold / decline / escalate).

How to run each tool

Decision Risk Reviewer
Describe one agent action (Agent ID, action type, target, context). Choose environment & industry, then click Run risk review. Use this for single high‑impact decisions.
Fraud Pattern Detector
Paste a short audit trail (JSON / CSV / plain text), set time window and sensitivity, then run analysis. Use this when you suspect pattern‑level abuse or overrides.
Agent Authority Risk Mapper
Describe your delegation chain in plain English or UCAN/DID JSON, select industry and regulatory scope, then map risk propagation. Use this to see where authority is over‑extended.

Live Risk Engine

What your risk team actually sees

This is how a single AI decision is reviewed, explained, and made audit-ready. Try it on a real scenario below — results in under 10 seconds.

Used by teams reviewing payments, lending, and AI-driven decisions.

Try this:

1 · Load "Privilege escalation"

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2 · Run analysis

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3 · See risk score + fraud impact + audit record

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Live Risk Engine — Not a Prototype

Every decision below is evaluated by a live AI risk engine trained on real fraud operations — the same judgment a senior fraud analyst would apply. Results are scored, logged, and traceable. Input your own scenario or load one of the examples.

Tool 01

Decision Risk Reviewer

Paste any AI agent action. Get a risk verdict your fraud team can act on — with fraud impact, false positive risk, and an audit-ready justification record.

What you get

Risk score · Fraud loss estimate · FP risk · Production outcome simulation · Audit record

Payments agent Data access Privilege escalation ★ Demo: UPI Fraud Approval ★ Demo: AI Override Abuse ★ Demo: Silent Fraud Pattern

Agent ID

Action type

Target resource

Agent-provided context

Environment

Industry

Tool 02

Fraud Pattern Detector

Paste an agent audit log. Zarelva reads it like a fraud investigator — identifying velocity abuse, override patterns, and compliance gaps your team needs to act on.

What you get

Pattern classification · Fraud loss estimate · Timeline analysis · EU AI Act gaps · Remediation steps

Velocity abuse Flag override pattern Normal operations

Paste audit log (JSON, CSV, plain text — any format)

Time window

Sensitivity

Tool 03

Agent Authority Risk Mapper

Describe your agent hierarchy. Zarelva maps how unchecked authority flows from one agent to the next — surfacing over-extension, liability gaps, and the controls you're missing.

What you get

Node risk scores · Privilege propagation map · Critical violations · Compliance gaps · Priority fixes

Payment approval chain Fraud review hierarchy

Delegation hierarchy (plain English or UCAN/DID JSON)

Industry

Regulatory scope

Why This System Is Credible

Built by a fraud risk practitioner, not a product team

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6+ years of live fraud operations

Hands-on experience across Flipkart, Google, Amazon AWS, and G2 Risk Solutions — working real fraud cases, not hypothetical models. Every risk signal in this engine comes from patterns seen in production.

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EU AI Act Annex III aligned by design

Fraud detection, credit scoring, insurance, and hiring AI are high-risk by default. This system is built with explicit Article 9 (risk management) and Article 12 (logging) hooks — not retrofitted for compliance.

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MSME registered · India

Zarelva is a registered Indian MSME (UDYAM-KR-03-0675917), based in Bengaluru. A real business entity with real accountability — not an anonymous SaaS tool.

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The complementary layer to Attestix

This is not a competing tool. It sits above attestation infrastructure. Where Attestix closes the "proof of action" gap, Zarelva closes the "proof of judgment" gap. Both are needed for Annex III compliance.

Engagements

Turn attested logs into defendable decisions