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Finance Domain Cookbook

Complete Working Example

from director_ai.core import CoherenceScorer, GroundTruthStore

store = GroundTruthStore()  # empty — populate with your KB
store.add("savings APY", "Our savings account APY is 4.25% as of February 2026.")
store.add("FDIC", "FDIC insurance covers up to $250,000 per depositor per bank.")
store.add("wire transfer", "Wire transfers take 1-3 business days.")

scorer = CoherenceScorer(threshold=0.30, ground_truth_store=store)

# Correct → approved
approved, score = scorer.review("What is the savings APY?",
    "The current savings account APY is 4.25%.")
print(f"Correct: approved={approved}, score={score.score:.2f}")

# Wrong → rejected
approved, score = scorer.review("What is the FDIC limit?",
    "FDIC covers up to $500,000 per depositor.")
print(f"Wrong:   approved={approved}, score={score.score:.2f}")

Configuration

scorer = CoherenceScorer(
    threshold=0.30,   # Measured on FinanceBench: 0% FPR at t≤0.30
    soft_limit=0.35,
    use_nli=True,
    ground_truth_store=store,
    cache_size=4096,   # High cache for repeated product queries
    cache_ttl=3600,    # 1-hour cache for stable financial facts
)

Knowledge Base

store = VectorGroundTruthStore()
store.ingest([
    "Our savings account APY is 4.25% as of February 2026.",
    "Wire transfers take 1-3 business days.",
    "FDIC insurance covers up to $250,000 per depositor per bank.",
    "Minimum balance for premium checking is $5,000.",
])

Compliance Pattern

from director_ai.core.audit import AuditLogger
from director_ai.core.policy import Policy

# Audit all interactions for compliance
audit = AuditLogger(path="/var/log/director-ai/finance")

# Policy: block responses mentioning specific stock recommendations
policy = Policy(
    patterns=[{"name": "no_stock_advice", "regex": r"(buy|sell|short)\s+(stock|shares)", "action": "block"}],
    forbidden=["stock recommendation", "investment advice"],
)

Compliance Cost Avoidance

Risk Exposure Without Director-AI With Director-AI
Wrong product terms quoted to customer CFPB/FCA fine ($5K–$1M per violation) Caught mid-stream, never reaches customer
Hallucinated interest rate or fee Customer dispute + regulatory review KB-verified before display
Unauthorized investment advice SEC/FINRA action ($50K–$10M) Policy engine blocks + audit trail

At 5,000 customer interactions/day, a 0.1% hallucination rate means 5 wrong answers daily. Over a year, that's 1,825 potential compliance incidents. With Director-AI, the catch rate reduces this to < 20/year (assuming high catch rate with KB + NLI at threshold=0.30).

Key Considerations

  • Regulatory compliance: audit all rejections and approvals
  • Rate data freshness: financial data changes — set appropriate cache_ttl
  • Disclaimer on all outputs: regulatory requirement for financial advice
  • Multi-tenant isolation: use TenantRouter for different product lines