> For the complete documentation index, see [llms.txt](https://quackai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://quackai.gitbook.io/docs/intelligence-and-analytics-layer/predictive-compliance-scoring.md).

# Predictive Compliance Scoring

Predictive Compliance Scoring is the analytical layer that quantifies trust before violations occur.\
It uses machine-learning models to forecast potential breaches, evaluate transaction safety, and pre-approve actions within risk tolerance limits.

This allows the network to intervene proactively rather than reactively.

#### Scoring Model Structure

| Input Category       | Data Used                             | Output Score                 |
| -------------------- | ------------------------------------- | ---------------------------- |
| Identity Signals     | KYC records, wallet history patterns  | Identity risk rating         |
| Transaction Behavior | Frequency, value, time patterns       | Behavioral consistency index |
| Policy Adherence     | Number of past violations or warnings | Compliance trend score       |
| Counterparty Risk    | Relation graph and jurisdiction data  | Counterparty exposure rating |
| Agent Reputation     | Execution accuracy and audit history  | Agent trust index            |

#### Scoring Workflow

1. Data Aggregation – Combine user, agent, and asset data.
2. Normalization – Convert metrics into comparable scales.
3. Model Processing – Run predictive models for each data type.
4. Composite Score Generation – Weighted average of sub-scores.
5. Policy Integration – Results feed directly into execution and governance logic.

#### Score Tiers

| Tier                | Interpretation                           | Automatic Outcome             |
| ------------------- | ---------------------------------------- | ----------------------------- |
| 90-100 Safe         | Fully compliant and low risk             | Fast-lane approval            |
| 70-89 Monitor       | Acceptable risk profile with minor flags | Extra review by Policy Engine |
| 50-69 Caution       | Irregular behavior detected              | Limited participation rights  |
| Below 50 Restricted | High risk or pending verification        | Requires manual approval      |

#### Use Cases

| Application             | Purpose                                           |
| ----------------------- | ------------------------------------------------- |
| Pre-Trade Verification  | Assess risk before token swaps or RWA redemptions |
| Treasury Disbursement   | Validate recipient trust before funds release     |
| Cross-Chain Transfers   | Check destination policy compatibility            |
| Agent Onboarding        | Evaluate new agents for network eligibility       |
| Institutional Reporting | Provide proof-based compliance certificates       |

#### Benefits

* Predicts policy violations before they occur.
* Automates risk segmentation for governance and finance.
* Enhances confidence for regulators and auditors.
* Provides data inputs for AI Twins and Facilitator selection.

Predictive Compliance Scoring completes the Intelligence & Analytics Layer by turning every data point into foresight. It ensures that the Quack AI network not only governs and executes intelligently but also anticipates risk before it materializes.