AI optimization loop
The AI optimization loop is not a structure to maximize the outcome of a single judgment. NoahAI is financial AI infrastructure designed so that, through the cycle of judgment → record → verify → feedback, judgment criteria themselves become more refined over time.
Each judgment is recorded per user, but outcomes are analyzed as anonymized patterns and reflected in overall policy improvement. As users and operational data grow, every user benefits from a more stable judgment environment.
Record
Judgment: structure decision support from market data
AI structures decision support from market data. Context and outcome of every judgment are recorded in a standardized format and stored for traceability.
Outcome
Outcome: record and explain results of judgment
Results of judgment are recorded and explained. The focus is not only on performance metrics or risk events but on clear explanation and record of outcomes.
Explain
Log: record judgment and outcome in explainable, standardized form
Judgment and outcome are fully recorded in explainable, standardized form. Under XAI policy, every decision process is transparent and categorized for traceability.
Policy
Replay: analyze logs and extract success/failure patterns
Recorded logs are analyzed to extract success/failure patterns. "Why was this decision good or bad?" is reviewed systematically; improvement is derived from pattern-level learning. This step is the core of reinforcement learning: individual outcomes are not reused directly—only success/failure patterns are used as reward signals.
Risk
Policy adjustment: auto-adjust decision policy and parameters from patterns
Decision policy and parameters are auto-adjusted from extracted patterns. Pattern learning by market regime (up/down/sideways) and judgment context by asset type are kept separate; one asset's outcome does not directly affect other judgment domains.
Feedback
Feedback: detect risk signals and strengthen guardrails
Risk signals are detected early; conservative control (guardrails) is strengthened when needed. "Minimize accidents" is prioritized over short-term returns; anonymized pattern-level learning detects risk signals faster. This process assumes organizing and explaining judgment; execution is connected only optionally per user or policy. Feedback does not copy individual execution results. Instead, the relationship between risk signals, judgment errors, and market conditions is learned as collective patterns and reflected only at the policy level, so one user's performance does not directly affect others.
XAI
Explainable AI: explain every decision and maintain verifiable structure
Every decision's reasoning is kept explainable and audit logs are maintained. This step is essential for trust and transparency; local storage allows external verification.
Why the 'loop' matters in financial AI
Financial judgment depends on context: assets, liabilities, goals, living expenses, risk tolerance. Trust is built through repeated verification and feedback, not single outcomes. This structure can extend to voice phishing and fraud detection, protection of digitally vulnerable users, and other financial safety areas.
Operational view
The AI optimization loop is not for making more decisions; it is for reducing the chance of failure and gradually improving judgment criteria.
The 7-step loop operates as follows:
- Continuous cycle: The 7 steps repeat; for every decision, AI organizes judgment, explains it, and performs policy adjustment.
- Pattern-level learning: Learning is by success/failure patterns, not raw past performance, enabling regime-specific pattern learning.
- Per-asset-type learning: Judgment context is separated by asset type; one asset's outcome does not directly affect other judgment domains.
- Data-driven: All improvement is based on recorded data and outcomes; stability and reproducibility are validated in production.
- Safety first: The Risk step prevents failure through conservative control and detects risk signals early.
- Transparency: The XAI step keeps every decision's reasoning traceable; local storage allows external verification.
- Collective improvement: Individual outcomes are protected; only collective patterns are used for policy improvement, so judgment quality improves cumulatively over time.
Reinforcement learning reward design
The reward functions below are examples of internal judgment-quality evaluation logic used in production; they do not guarantee returns or promise investment performance.
NoahAI's reinforcement learning system uses the following reward functions:
Profit trade reward
- α: Reward scaling (default 1.0)
- profit_rate: Actual return (0.0–1.0)
- confidence_score: AI confidence (0.0–1.0)
- risk_penalty: Risk penalty (0.0–0.5)
Loss trade reward
- β: Loss scaling (default 1.2)
- loss_rate: Actual loss (negative)
- consecutive_loss_penalty: Consecutive loss penalty (0.0–0.3)
Risk management reward
- γ: Risk management reward coefficient (default 0.5)
- early_exit_bonus: Early exit bonus (0.0–0.2)
- late_exit_penalty: Late exit penalty (0.0–0.3)
RL reward and policy adjustment logic are handled automatically by the internal engine; every judgment is recorded in reproducible log form. Implementation details are in the system architecture document.
How RL and collective learning connect
NoahAI's reinforcement learning does not maximize per-account returns. It rewards 'judgment quality' itself: appropriateness of judgment, risk response, explainability, accident avoidance.
Each user's outcome is collected only as anonymized patterns; as these accumulate, policy criteria become more conservative and refined. Through this structure NoahAI aims not for 'more users, more risk' but for 'more users, lower probability of failure.'
Related technical docs
Architecture, record, and proof linked to the AI optimization loop are described in the documents below.