PTI and Risk Engines
Risk engines (decision engines, credit policy platforms, rules orchestrators) evaluate institution-specific policies against applicant data — bureau scores, internal behavior, product rules, and regulatory constraints. PTI feeds risk engines with portable trust intelligence; it does not replace the institution's authority to approve or decline.
1. What risk engines are
Risk engines are policy execution platforms that combine data sources, models, and business rules into a decision outcome. Common forms:
- Credit decision engines — scorecards, cutoffs, reason codes
- Business rules management (BRM) — Drools, FICO Blaze, custom rule DSLs
- ML model serving — real-time feature vectors and model endpoints
- Workflow orchestration — refer, manual review, stipulation paths
- Champion/challenger — A/B policy testing and monitoring
Risk engines answer: Given our policy and this applicant's data, what action do we take?
2. What problem risk engines solve
| Problem | Risk engine response |
|---|---|
| Inconsistent manual underwriting | Codified policy rules |
| Regulatory reason code requirements | Structured decline explanations |
| Product-specific cutoffs | Tiered pricing and limits |
| Model governance | Versioned scorecards and audit |
Risk engines are institution-owned decision systems. They require quality inputs — but typically integrate each data source through bespoke connectors with no standard trust intelligence envelope across vendors and contexts.
3. What PTI adds
Risk engines
- Institution policy execution
- Final approve / decline / refer
- Product-specific scorecards
PTI adds
- Standard trust input — JSON envelope with drivers and confidence bands
- Context-scoped features — request
lendingonly, not global mashup - Provenance metadata — feature sources for model documentation
- Coverage gaps — explicit thin-data flags for policy branching
PTI implements the Trust Intelligence Engine conformance class — producing inputs suitable for risk engine feature pipelines. The institution retains decision accountability; PTI supplies governed external intelligence.
4. How they compose together
Integration pattern:
- Origination workflow triggers parallel data fetches — bureau, internal, PTI trust lookup (
contexts: ["lending", "risk_compliance"]). - PTI response maps to feature variables —
pti_lending_confidence_pct,pti_driver_repayment_weight,pti_coverage_gap_count. - Risk engine applies institution scorecard — PTI features are one input block, not the decision itself.
- Decline reason codes combine bureau reasons with PTI driver labels where policy requires.
Institutions SHOULD treat PTI as adjacent data subject to fair lending and model risk management — documenting feature usage in model cards.
5. When to use each
| Scenario | Risk engine | PTI |
|---|---|---|
| Final loan approve/decline | Required | Input only |
| Portable cross-MFI behavioral features | Engine cannot source alone | Required |
| Internal overdraft limit rules | Required | Optional |
| Multi-context tenant + employment check | Engine needs features | PTI multi-context lookup |
| Regulatory decision ownership | Institution | PTI never decides |
Rule of thumb: PTI is upstream intelligence; the risk engine is downstream policy.
6. Related PTI spec/RFC links
- RFC-004 — Trust Lookup API
- Explainability guide (
explain_score.v1) - RFC-012 — Trust Evidence
- Reference API Specification
- Compliance guide