AI Playbook for FCPA Third-Party Payment Risk
A US defense contractor is under DOJ inquiry regarding payments to a UAE-based distributor that has a documented relationship with a procurement official at a Gulf state ministry. The distributor received $3.4M in commissions over 4 years. The contractor is preparing a voluntary disclosure.
When to use this playbook
- Use this playbook when the decision looks like the situation above: A US defense contractor is under DOJ inquiry regarding payments to a UAE-based distributor that has a documented relationship with a procurement official at a Gulf state ministry.
- It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "FCPA Third-Party Payment Risk".
- Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.
What you'll need
- Commission payment records to the UAE distributor (4 years)
- Distributor contract and due diligence file
- Email thread (legal review cleared)
- Ministry procurement award timeline
- Internal approval records for each commission payment
Attachments: Documents (Documents)
The Prompt
You are a forensic accountant supporting an FCPA voluntary disclosure for a US defense contractor. I am attaching: Work only from the attached source files. If a conclusion is not supported, say so. Produce: 1. Map every commission payment to a corresponding contract award: did payments precede, accompany, or follow awards? Identify the timeline pattern. 2. Identify any payments that are not tied to a specific contract or that were made outside the contracted commission rate. 3. Flag communications in the email thread that reference the procurement official by name, title, or relationship. 4. Calculate the disgorgement amount DOJ is likely to seek under Pilot Program guidelines. 5. Tell me what the voluntary disclosure package needs to include and what mitigating factors we can document before filing. Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.
What to expect
- Payment-to-award timeline matrix
- Off-contract payment register
- Communication risk inventory
- Estimated disgorgement range
- Voluntary disclosure checklist
Review before you act
- Validate this output against source files before relying on it: Map every commission payment to a corresponding contract award: did payments precede, accompany, or follow awards? Identify the timeline pattern.
- Validate this output against source files before relying on it: Identify any payments that are not tied to a specific contract or that were made outside the contracted commission rate.
- Validate this output against source files before relying on it: Flag communications in the email thread that reference the procurement official by name, title, or relationship.
- Validate this output against source files before relying on it: Calculate the disgorgement amount DOJ is likely to seek under Pilot Program guidelines.
- Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
- Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
- Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.
Why compare models on this
For FCPA Third-Party Payment Risk, running the same attachments across independent models is useful because the hard part is classification and completeness, not fluency. The workflow is already designed to surface payment-to-award timeline matrix; off-contract payment register; communication risk inventory; estimated disgorgement range. Those are comparison artifacts — they only exist if more than one model runs. Models often split on qualitative materiality, intent versus error, and whether a newly formed counterparty is a red flag or a legitimate intermediary. Those splits are the review queue — not noise.
See governed multi-model AI on your own prompt
Compare GPT-5, Claude, and Gemini side by side, with human review and a decision record built in.

