Transaction Compliance Nexus

1 System Overview

The Transaction Compliance Nexus (TCN) is a pre-clearing compliance layer that intercepts every transaction before it reaches the clearing house. Unlike advisory fraud tools that run in parallel, TCN is an inline gate that autonomously decides: ACCEPT, REVIEW, or BLOCK—in under 50 milliseconds.

Core Innovation: Interpretable Vector Space

Each transaction is encoded into an 80-dimensional semantic vector where every dimension has explicit meaning. This enables:

2 The 80-Dimensional Risk Vector Space

Each transaction generates two 40-dimensional vectors. Values range from 0.0 (low risk) to 1.0 (high risk). Together, they form an 80D combined vector that captures complete transaction context.

2.1 Inherent Risk (IR) — Entity Profile (40 Dimensions)

IR captures who is transacting: account history, behavioral patterns, and risk classification.

2.2 Control Risk (CR) — Transaction Context (40 Dimensions)

CR captures what is happening: transaction characteristics, velocity patterns, and technical signals.

2.3 The Perfect Vector

The Perfect Vector represents an ideal low-risk transaction from a fully verified, long-standing customer with consistent behavior. It serves as the reference point for all similarity calculations.

3 Cosine Similarity: Measuring Risk Deviation

Cosine similarity measures the angular distance between two vectors, independent of magnitude. This is crucial because we care about the pattern of risk distribution across dimensions, not absolute values.

3.1 Interpretation

3.2 Why Cosine Similarity?

4 Audit Risk (AR) Calculation

The Audit Risk score combines IR and CR similarities into a single risk metric, inspired by traditional audit methodology (AR = IR × CR). We convert similarity (higher = safer) to risk (higher = riskier).

4.1 Weight Rationale

4.2 Non-Linear Scaling

The exponent 0.8 applies a power function that amplifies mid-range values, making the system more sensitive to moderate risk signals that might otherwise fall below thresholds.

4.3 Decision Thresholds

5 3D Visualization

To visualize 80-dimensional data, we project to 3D using domain-aware aggregation. Unlike generic PCA, our projection preserves semantic meaning by grouping related dimensions.

5.1 Axis Mapping

5.2 Visual Interpretation

The visualization displays wireframe spheres centered on the Perfect Vector representing decision zones: green (ACCEPT r=0.4), yellow (REVIEW r=0.8), red (BLOCK r=1.3).

6 Agentic AI: Autonomous Compliance

By 2027, Gartner predicts 50% of enterprises will deploy AI agents for autonomous decision-making. TCN is architected from day one for this agentic future—systems that perceive, reason, decide, and learn without human intervention.

6.1 The Four Pillars of Agentic AI

6.2 Why 80D Fixed Dimensions: A Deliberate Architectural Choice

Our 80D semantic vector space is not a limitation—it's a deliberate design decision that enables autonomous AI at payment speed:

At 10,000 TPS: Vector comparison uses ~5ms total, leaving 995ms for ensemble logic, logging, and I/O. The bottleneck is never the vector math.

6.3 Structured World Model for Agent Reasoning

Unlike opaque embeddings from black-box neural networks, our 80D vectors provide a structured world model that AI agents can navigate and explain:

6.4 Multi-Agent Ensemble Architecture

TCN operates as a multi-agent system where specialized agents collaborate—mirroring how DeepMind and OpenAI structure complex decisions:

6.5 Continuous Learning Without Retraining

6.6 Explainability Output

{
  "top_risk_factors": [
    { "dimension": "CR:mule_account_indicator", "contribution": 0.89 },
    { "dimension": "CR:cross_border_indicator", "contribution": 0.76 },
    { "dimension": "CR:amount_deviation_score", "contribution": 0.71 },
    { "dimension": "IR:pep_status_score", "contribution": 0.65 }
  ]
}

6.7 Regulatory Compliance Built-In

6.8 Competitive Advantage

6.9 Roadmap

A Appendix: Inherent Risk (IR) Dimensions

IR captures entity-level, historical, and behavioral risk factors. All 40 dimensions with their semantic meaning and ideal "Perfect Vector" values:

A.1 Account Profile (IR 0-9) — Used in X-Axis

A.2 Transaction History (IR 10-19)

A.3 Behavioral Profile (IR 20-29) — Used in X-Axis

A.4 Risk Classification (IR 30-39) — Used in Z-Axis