Evaluator Context · Feature and Context Engine
FEATURES — Context Evidence
A trust score is not based on a raw transaction alone. The feature stage derives context evidence such as transaction velocity, corridor familiarity, device trust, relationship age, authentication strength and metadata completeness. These indicators form the evidentiary basis used by the AI trust engine.
FEATURES — Context Evidence
Expanded evaluator context
Professional interpretation
Purpose in the demonstrator
This stage translates a normalized transaction into analytical evidence. It is the bridge between interoperable transaction ingestion and AI assessment.
Operational meaning
Behavioural evidenceVelocity, recurrence and corridor familiarity indicators are derived.
Context evidenceCustomer, counterparty, device and channel context are summarized.
Quality evidenceCompleteness and reliability of available metadata affect confidence.
Inputs and outputs
| Element | Meaning in this demonstrator |
|---|---|
| Normalized transaction | Common transaction structure produced by the previous stage. |
| Feature vector | Derived indicators used by the model, such as velocity, corridor and device context. |
| Evidence indicators | Named contextual facts that support explainability and governance. |
| Output | Structured feature and context package for AI assessment. |
Evaluator questions this page answers
- What factual evidence does the AI actually consume?
- Why do some scenarios reduce confidence or increase risk?
- How is weak metadata reflected before the model scores?
- Which contextual signals matter most in the demonstrator?
How this stage fits in the end-to-end chain
1. INPUTCanonical transaction available.
2. FEATURESContext evidence and feature vector derived.
3. AI TRUSTModel scores the evidence package.
4. EXPLAINApplied evidence becomes reason codes and narrative interpretation.
Governance and interpretation notes
This page is an evaluator-facing contextual explanation of the demonstrator stage. It explains the business role, the technical contribution, the governance boundary and the expected interpretation of the displayed output. It is designed to make the demonstration credible, understandable and professionally reviewable without exposing raw internal JSON on-screen.
Feature & Context Derivation
This visual explains how analytical evidence is produced from the normalized transaction before AI scoring begins.
Velocity, device context, corridor behaviour and metadata quality are transformed into the evidence package consumed by the AI model.