Status: Concept / Pre-prototype
Financial Dynamics · Personal Financial Norms · Adaptive Actions is a provider-agnostic Personal Financial Intelligence System built from consented financial data. It is a design proposal, not a production service or a performance assertion.
Generic financial prompts often overlook timing, obligations, data gaps, and the fact that two people with similar income can have very different financial room. Financial D/N/A proposes a shared profile and feature layer with separate task-specific models: describe changing financial conditions, learn dynamic personal behavioral ranges, and offer bounded actions with clear uncertainty.
The intended shift is Descriptive → Predictive, Generic → Personalized, and Insight → Decision—without treating an automated suggestion as a guarantee.
- Financial Dynamics: normalized, quality-aware signals about balances, cash flow, recurring obligations, and change over time.
- Personal Financial Norms: dynamic behavioral ranges, not moral or social norms, that form a revisable personal baseline.
- Adaptive Actions: task-specific, coverage-aware decisions such as Safe-to-Save and Contextual Transfer Review.
Every action, qualified insight, and no-action result carries a mandatory Financial Explanation envelope. The envelope is not an independent predictive task or model.
Potential prototype outcomes are clearer decision support, more legible uncertainty, and user-correctable assumptions. These are hypotheses to evaluate, not promised customer or business results.
flowchart TB
A([Consented financial data])
subgraph DNA[Financial D / N / A]
direction LR
D[Financial Dynamics<br/>What is changing?]
N[Personal Financial Norms<br/>What is normal for this person?]
AA[Adaptive Actions<br/>What is safe and useful now?]
D --> N --> AA
end
R([Action · Insight · No action])
X[Financial Explanation<br/>Evidence · gaps · confidence]
subgraph LEARNING[Governed learning]
direction LR
U[User correction] --> G[Governed evidence update] --> V[Next Personal<br/>Financial Norms version]
end
A --> D
AA --> R --> X
X -.-> U
See the architecture source and data-flow source.
Data Coverage describes task-relevant evidence completeness: High is sufficiently complete and current, Medium has material but bounded gaps, and Low has gaps that can reverse the decision. Model Confidence separately describes inference support: High is stable and well-supported, Medium is mixed or uncertainty-sensitive, and Low is weak or unstable. Numeric tier thresholds are task-specific policy parameters to calibrate, not universal production constants. See the complete action-gating matrix.
| Area | Purpose |
|---|---|
| System overview | Architecture, cold start, and decision boundaries |
| Data model | Canonical entities, provenance, and safeguards |
| Personal norms | Dynamic baselines and evidence blending |
| Adaptive actions | Action-specific behavior and explanations |
| Responsible intelligence | Controls, governance, and prototype limits |
| Synthetic scenarios | Documentation-level acceptance cases |
| Provider integration guide | Capability-based adapter contract |
- Read the system overview for the end-to-end proposal.
- Review the data model and provider integration guide before considering an implementation.
- Review the synthetic scenarios as documentation-level acceptance cases; they are not executable tests yet.
- Apply the responsible intelligence boundaries to every proposed experiment.
Start with offline synthetic data and turn the documented acceptance cases into an evaluation harness. Evaluate calibration of task-specific tier policies and ranges, whether explanations disclose assumptions and missing data, correct no-action behavior, user correction handling, and separation of anomalous behavior from fraud claims. Models should be tested per task; a shared feature layer does not imply a single universal model.
Financial D/N/A uses consented data only; it does not infer sensitive attributes by default, make lending or eligibility decisions, guarantee affordability, or identify a recipient as fraudulent from a personal anomaly. Data Coverage and Model Confidence are separate controls. Low Data Coverage never permits a personalized action; a safe, useful insight or a no-action result still carries Financial Explanation.
- Define canonical schemas and adapter validation.
- Build a synthetic-data evaluation harness and coverage simulator.
- Prototype Safe-to-Save with conservative ranges and corrections.
- Evaluate Contextual Transfer Review and the mandatory Financial Explanation envelope under governed, non-production conditions.
No license has been selected. All rights are reserved by default; this repository grants no open-source permissions.
This is conceptual documentation, not financial, legal, security, or compliance advice, and not a production-ready system.
System overview · Data model · Personal norms · Adaptive actions · Responsible intelligence · Scenarios · Integration guide