FinTechCryptoReview Prompt

AI Prompt for Reusable Synthetic Identity Asset Scans

Directs the Arbiter agent to sweep recent onboarding sessions for recycled synthetic identity assets: reused faces, document templates, and device fingerprints that fraud rings package into kits. Built for fraud and compliance teams running high-volume funnels.

How to use this prompt

1
Open the deepidv dashboard and address the prompt to Arbiter, or paste it into Claude, ChatGPT, or Gemini with your session export attached.
2
Set the lookback window you want swept; thirty days is a sensible default for funnels above ten thousand sessions a month.
3
Run the prompt and review the risk-ranked clusters it returns: each cluster groups sessions sharing a face, document template, or device signature.
4
Escalate confirmed clusters to blocking rules and feed the asset signatures back into your screening lists so repeat submissions fail at the gate.

The prompt

Arbiter, initiate an automated cross-examination of all account onboarding sessions from the past 72 hours. Check for indicators of reusable synthetic assets or automated deepfake selfie patterns matching the syndication tactics exposed in the AU10TIX Q1 2026 report.

INPUT, the user will paste:
- Session IDs from the past 72 hours and the disposition (approved, manual review, rejected)
- Biometric capture quality scores per session
- Device telemetry fingerprints per session
- Any prior known-fraud signatures the firm has captured

OUTPUT, return the following structured response:

1. REUSABLE-ASSET SIGNATURE MAP
For each session that matches an industrialized-asset pattern:
- Session ID and original disposition
- The asset signature that matches and the AU10TIX-report category
- Confidence band and the dominant signal that drove the match

2. SYNDICATION ANALYSIS
- Sessions sharing the same biometric vector across different identities
- Sessions sharing the same device telemetry fingerprint across different identities
- Time-clustered submissions consistent with automated-script execution

3. APPROVED-WITH-RISK INVENTORY
- Sessions that triggered high-risk flags but were approved automatically (the AU10TIX-reported failure mode)
- The specific gate logic that approved them
- The recommended gate-tightening adjustment

4. CONTAINMENT ACTIONS
- Accounts to freeze pending re-verification
- The re-verification path to require
- The records to retain for any downstream SAR filing

5. FORWARD MITIGATION
- New detection rules to deploy against the surfaced signatures
- Re-screening cadence for the past 30 days of approved traffic
- Recommended Luna prompts to follow up with for SAR drafting

Be specific. Quote session IDs where the analysis surfaces a match. Do not hedge on confirmed-asset calls.

Test it in Claude or another LLM

This prompt is built for the Arbiter agent inside deepidv, where it cross-examines the past 72 hours of onboarding sessions for reusable synthetic assets and automated deepfake selfie patterns tied to known syndication tactics. Here is how to dry-run the same workflow in any general LLM with synthetic session data before you point Arbiter at live sessions.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, and replace the direct address 'Arbiter, initiate an automated cross-examination...' with a role instruction such as 'Act as a fraud-detection analyst reviewing onboarding sessions for reusable synthetic identity assets.' Keep the five OUTPUT sections (signature map, syndication analysis, approved-with-risk inventory, containment, forward mitigation) intact.
2
Below the prompt, paste the synthetic sample data block from sampleInput so the LLM has fake session IDs, dispositions, biometric quality scores, device fingerprints, and a known-fraud signature to match against. This stands in for the live deepidv 72-hour session export.
3
Add a line instructing the model to quote the exact fake session IDs wherever it surfaces a match and to not hedge on a confirmed-asset call, mirroring the prompt's closing instruction.
4
Good output for this prompt is a signature map that names each matching session ID, the asset signature, and a confidence band; a syndication section that explicitly groups sessions sharing the same biometric vector or device fingerprint across different identities and flags time-clustered submissions; an approved-with-risk list calling out sessions auto-approved despite high-risk flags; and concrete freeze, re-verification, and SAR-retention actions. If it returns generic advice without naming the fake session IDs or the shared-vector groupings, sharpen the data block and re-run.
5
Once the output shape looks right, run the prompt live in the deepidv dashboard where Arbiter executes it against your real past-72-hour sessions, biometric quality scores, and device telemetry.

Synthetic sample data to paste alongside the prompt

Fake test data, safe to share with any LLM. Swap in your own once the output looks right.

SESSIONS, LAST 72H (synthetic, fake):
- SESS-TEST-0001 | approved | bio-quality 0.91 | device-fp DFP-AAA-000 | identity Jane Testcase
- SESS-TEST-0002 | approved | bio-quality 0.90 | device-fp DFP-AAA-000 | identity John Sampleton
- SESS-TEST-0003 | manual review | bio-quality 0.44 | device-fp DFP-BBB-111 | identity Pat Fakerton
- SESS-TEST-0004 | approved | bio-quality 0.89 | device-fp DFP-CCC-222 | submitted 03:01:14 (cluster of 12 within 90s)
KNOWN-FRAUD SIGNATURE (fake): SIG-TEST-REUSE-00 = identical sub-pixel selfie texture reused across distinct names

Pairs with on deepidv

Sources & further reading

FAQ

What is a reusable synthetic identity asset?

A face image, document template, or device profile that fraud rings package and resell so many operators can submit the same convincing identity. Catching the shared asset, rather than each submission, collapses the entire kit.

How often should I run a synthetic asset scan?

Weekly for high-volume funnels, and immediately after any spike in approvals from a new traffic source. Recycled assets cluster in time, so fresh scans catch kits while the ring is still active.

Run it with live verification data

These prompts work in any LLM. Inside the deepidv dashboard, Luna, Arbiter, and Arc run them against your real sessions, screening lists, and audit trails.

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