FinTechTask Prompt

Task Prompt to Grade Mobile Driver's License Issuers for mDL Acceptance

This Arc task prompt stands up issuer-posture-weighted mDL acceptance before volume arrives, because every issuer's provisioning ceremony sets the assurance ceiling for the credentials you accept, and the US install base is headed from 21.7 million toward 143 million. Arc, the deepidv credential gateway, builds an issuer posture table per issuing state and wallet platform covering provisioning, in-person versus remote enrollment, liveness requirements, document checks, revocation infrastructure, trust-list participation, and incident history, with every unknown marked explicitly because opacity is a posture. It then sets acceptance weighting per issuer tier and action class, defines binding tiers that decide which action classes (onboarding, payouts, recovery, age-only checks) need a liveness-anchored portrait match on top of cryptographic validation, builds the freshness machinery that measures the lag between issuer revocation and your refusal and alerts when any issuer exceeds policy, and writes the per-acceptance evidence schema exportable for the examinations that follow CIP credential adoption. Built for bank and fintech compliance and fraud leads who want the first provisioning scandal to land as a weight change, not a crisis.

How to use this prompt

1
Open Arc in the deepidv dashboard and paste the full prompt, or run it in Claude, ChatGPT, or Gemini if you are drafting the posture table outside the platform.
2
Below the prompt, add an INPUT section listing the issuing states and wallet platforms your customers present, the action classes you gate, and your current trust-list and revocation handling.
3
Run the prompt and read the issuer posture table first: every unknown is marked explicitly, since an issuer that does not document its provisioning ceremony is graded on that opacity.
4
Hand the acceptance weighting and binding tiers to onboarding and fraud engineering, and route the freshness alerts and evidence schema to compliance with an owner per issuer.
5
Re-grade the table quarterly and on every issuer incident, so the first provisioning scandal lands as a weight change rather than a crisis.

The prompt

Arc, stand up issuer-posture-weighted mDL acceptance before volume arrives: 21 states issue today, the install base is headed from 21.7 million toward 143 million, and every issuer's provisioning ceremony sets the assurance ceiling for credentials we accept.

Produce:
1. Issuer posture table: per issuing state (and wallet platform), what is documented about provisioning, in-person versus remote enrollment, liveness requirements, document checks, plus revocation infrastructure, trust-list participation, and any incident history. Mark unknowns explicitly: opacity is a posture.
2. Acceptance weighting: how a valid presentation scores as evidence per issuer tier and per action class, with weak or opaque issuers earning the present-person bind at lower risk thresholds.
3. Binding tiers: the action classes (onboarding, payouts, recovery, age-only checks) and the liveness-anchored portrait match each requires on top of cryptographic validation.
4. Freshness machinery: trust-list update cadence, revocation checks at presentation, and the measured lag between issuer revocation and our refusal, with an alert when any issuer's lag exceeds policy.
5. Evidence schema: per acceptance, the record naming issuer, trust-list version, checks run, binding method, and outcome, exportable for the examinations that follow CIP credential adoption.

Re-grade the table quarterly and on every issuer incident, and treat the first provisioning scandal as a weight change, not a crisis.

Test it in Claude or another LLM

This prompt is built for Arc inside deepidv, where Arc validates live mDL presentations against current trust lists and revocation status. You can dry-run the posture table in any general LLM first with synthetic issuer data before wiring it to real acceptance policy.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, replacing the opening 'Arc,' with a role instruction such as 'Act as a credential acceptance architect grading mobile driver's license issuers.' Keep the five numbered outputs as written.
2
Under the INPUT section, paste the synthetic sample block below so the model has issuers, wallet platforms, and action classes to grade.
3
Add one framing line: 'This is synthetic test data. Where an issuer's provisioning or revocation practice is not stated, grade it as unknown rather than assuming it, since opacity is a posture.'
4
Check the output shape: an issuer posture table, acceptance weighting per issuer tier and action class, binding tiers, freshness machinery with a lag alert, and a per-acceptance evidence schema. If any issuer is graded on a practice the input does not describe, tighten the framing and re-run.
5
Once the shape is right, run it live in the deepidv dashboard where Arc validates real presentations against current trust lists and revocation status.

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.

ISSUERS (synthetic, fake):
- State A: in-person enrollment only, liveness documented, on the trust list, revocation status list published, no incidents on record
- State B: remote enrollment by document scan and selfie, liveness undocumented, on the trust list, no published revocation mechanism
- State C: provisioning practices unpublished, trust-list status unknown, one reported provisioning incident
WALLET PLATFORMS (fake): Wallet X, Wallet Y
ACTION CLASSES (fake): onboarding, payouts, account recovery, age-only checks
CURRENT POLICY (fake): every cryptographically valid mDL accepted at equal weight, no portrait match on any action
OPEN ITEM (fake): revocation-to-refusal lag has never been measured for any issuer

Pairs with on deepidv

Sources & further reading

FAQ

What is an mDL issuer posture table?

It is a maintained grading of each mobile driver's license issuer, per issuing state and wallet platform, covering how credentials are provisioned, whether enrollment is in person or remote, which liveness and document checks run, how revocation works, trust-list participation, and incident history. Unknowns are graded explicitly, because an issuer that does not document its provisioning is itself a risk signal.

What does this prompt produce?

An issuer posture table, acceptance weighting per issuer tier and action class, binding tiers that set which action classes require a liveness-anchored portrait match on top of cryptographic validation, freshness machinery that measures the lag between issuer revocation and your refusal, and a per-acceptance evidence schema exportable for examinations. The table is re-graded quarterly and on every issuer incident.

Why does a valid mDL still need a portrait match?

Cryptographic validation proves the issuer signed the credential and that it has not been altered, not that the person presenting it is the person it was issued to. Stolen devices and coerced approvals present perfectly valid credentials, and a wallet provisioned against a stolen identity validates cleanly every time it is shown, so high-stakes actions and weak or opaque issuers need a liveness-anchored portrait match on top of the signature check.

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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