11.7 Future Extensions – Kor.ai Bayesian Risk Engine - ravkorsurv/kor-ai-core GitHub Wiki

11.7 Future Extensions – Kor.ai Bayesian Risk Engine

This section outlines potential enhancements to Kor.ai’s Bayesian abuse detection platform across model evolution, architecture, and analyst tooling. These extensions build on the foundational principles of Bayesian Networks and support system-wide scale, flexibility, and regulatory robustness.

🌐 Model Expansion

Additional Risk Typologies

  • Wash Trading: Detect cyclical trades between linked accounts or entities
  • Collusion: Model multi-party abuse networks (via shared identifiers or comms)
  • Layering: Sequence of deceptive quote placements followed by trades
  • Quote Stuffing: Detection of high-frequency cancel patterns

Multi-Model Composition

  • Merge BN outputs from different typologies (e.g., Insider + Spoofing) into:

    • Composite AbuseLikelihoodNode
    • Typology-specific scorecards for dashboards

Shared Node Pools

  • Use global nodes (e.g., AccessToInfo, IntentToManipulate) across all models
  • Support future risk types without duplicating logic

⏱️ Dynamic Time Models

Dynamic Bayesian Networks (DBNs)

  • Model sequential events in abuse patterns:

    • Spoofing: Order placed → price moves → order cancelled
    • Front-running: RFQ received → proprietary trade placed → client fill

Event-Based Time Slices

  • Track transition states across:

    • T-1 (intent)
    • T0 (execution)
    • T+1 (price impact)

Technical Additions

  • Define timeIndex for nodes
  • Extend payloads to support time-series evidence (e.g., Q3_T0 = true)

⚖️ Risk Decision Graphs

Add Utility Nodes

  • Extend BN to include STOR Filing decision as a downstream node

  • Input parameters:

    • Risk Score
    • Case Age
    • Analyst Disposition
    • Cost of False Positive

Decision Optimization

  • Recommend:

    • Close
    • Escalate to L2
    • File STOR

  • Use expected utility to drive compliance actions

🚀 Inference Engine Evolution

pgmpy Fallback Support

  • Full replication of Agena inference in open-source pgmpy

  • CLI + backend services to:

    • Load BN models
    • Validate CPTs
    • Run local scoring

Dual Engine Capability

  • Use Agena for production

  • pgmpy for:

    • Simulation
    • Research
    • Offline fallback

📊 Model Learning and Feedback

Parameter Learning

  • Use reviewed cases to adjust CPT probabilities

  • Bayesian parameter update via:

    • Expectation Maximization (EM)
    • Scoring-based approaches

Structural Learning (Phase 3+)

  • Use alert history to learn:

    • New node connections
    • Correlations across typologies

📊 Analyst Workflow Enhancements

LLM-Powered Explanations

  • Use GPT-style prompts to auto-generate:

    • “This alert was scored 92% due to...” narratives
    • “Suggested review outcome: escalate”

Analyst Notes Summarization

  • Auto-tag cases using disposition notes
  • Train feedback loops on natural language annotations

🥇 Integration with Case Systems

Alert Lifecycle Hooks

  • Push alert events into:

    • Jira
    • ServiceNow
    • In-house case platforms

Export Format

  • Standard JSON with:

    • Alert ID
    • Risk Score
    • Contributors
    • Evidence Map
    • Analyst Comments

✨ Final Remarks

These extensions position Kor.ai’s platform to:

  • Scale across asset classes and abuse types
  • Adapt based on feedback and regulatory demands
  • Offer cutting-edge explainability and automation for compliance analysts

All enhancements are staged by phase, driven by business adoption and user feedback cycles.