Business Context
ApexPay is a fintech that issues prepaid debit cards and provides SMB lending across the US and EU, serving 18M active customers and processing $9B/month in card volume. ApexPay must continuously ensure its customer-facing policies (fee disclosures, adverse action notices, marketing claims, privacy notices) comply with evolving regulations (e.g., CFPB Reg E, ECOA/Reg B, GDPR, PSD2). Today, a legal ops team manually reviews every policy update and product launch document, causing 2–3 week launch delays and creating risk of missing a requirement that could trigger regulatory fines, consent orders, or forced product rollbacks.
You are asked to propose an NLP-driven multi-agent system that reviews regulatory documents and internal policy drafts to identify potential compliance errors, produce evidence-backed findings, and route high-risk items to counsel.
Data Characteristics
ApexPay has:
- Regulatory corpus: ~120k pages of statutes, regulatory guidance, supervisory highlights, and enforcement actions (PDF/HTML). Many are long (5–200 pages), with nested sections, footnotes, and cross-references.
- Internal documents: ~60k policy drafts and product requirement docs (PRDs) over 5 years.
- Annotation: 35k historically reviewed internal documents with attorney notes. Notes include issue types (e.g., “missing fee disclosure”), severity, and citations to regulation sections.
- Text length: internal docs median 900 words (p95 6,000). Regulatory sections median 250 words.
- Language: 85% English, 10% German, 5% French (EU policies).
- Label distribution (for issues): “No issue” ~70%, “Minor” ~20%, “Material” ~9%, “Critical” ~1%.
Success Criteria
- Critical issue recall ≥ 97% on a held-out, attorney-adjudicated test set.
- Evidence quality: every finding must include (a) a quote span from the internal doc and (b) a regulatory citation with the matching excerpt.
- Latency: < 30 seconds median per document (up to 20 pages) in an async workflow.
- Auditability: deterministic logs of prompts, retrieved passages, model versions, and final decision rationale.
Constraints
- Documents contain sensitive customer and partner information; system must run in a VPC with strict access controls.
- Must support multilingual review (EN/DE/FR) and preserve original citations.
- Hallucinations are unacceptable: the system must prefer abstain/escalate over unsupported claims.
Requirements (Deliverables)
- Propose a multi-agent architecture (roles, responsibilities, and handoffs) that reviews a document end-to-end.
- Define the information extraction schema (entities like regulation name, section, obligation, exception, thresholds, dates, jurisdictions) and how you will extract them.
- Describe how agents will use retrieval over the regulatory corpus (chunking, embeddings, reranking, citation grounding).
- Specify a modeling approach for: (a) issue detection, (b) severity classification, and (c) evidence/citation generation.
- Provide an evaluation plan addressing recall for critical issues, citation accuracy, and multilingual robustness.
- Outline production safeguards: prompt injection defenses, abstention logic, human-in-the-loop workflow, and monitoring for regulation drift.
Your answer should be concrete: include agent prompts/contracts, data flow, and how you would implement and evaluate the system in Python.
