Scientific Foundation
Engineering Trust.
AICIL is built on rigorous research into graph theory, outcome-based machine learning, and regulatory data structures. Our work is designed to create a more transparent global financial system.
Methodology
Core Research Areas
Graph Intelligence
Neo4j-powered correspondent banking network mapping across 180+ jurisdictions. Native graph queries for chain resolution and real-time path prediction.
Outcome Training
Models trained on downstream regulatory outcomes — whether correspondent banks accepted or rejected compliance packages — rather than human-labeled decisions.
Data Rigor
ISO 20022 compliance for every data object. Sanctions lists refreshed twice daily with 12-hour TTL. Stale data blocks transactions automatically.
Vector Embeddings
pgvector-powered semantic search across compliance precedents. Sub-100ms similarity queries enable real-time matching against historical regulatory decisions.
Multi-List Screening
Parallel fuzzy matching against OFAC SDN, EU Consolidated, UN Security Council, UK HMT, PEP databases, and adverse media.
Model Governance
Champion/Challenger deployment with 7-day minimum validation. SHAP values computed per prediction. Full audit trail with tamper-evident hash chains.
Publications
Research Papers
Pre-Emptive Clearance: A New Protocol for Correspondent Banking
An exploration of how pre-verified compliance data objects can eliminate settlement delays in high-risk corridors.
Explainable AI in Sanctions Screening: Moving Beyond Fuzzy Matching
Analysis of outcome-based model training and its impact on reducing false positive rates.
The $40B Friction: Quantifying the Cost of Cross-Border Compliance
A comprehensive study on the systemic costs of manual verification and reactive regulatory review.
OGEE: Outcome-Guided Expert Estimation for Compliance Intelligence
Formal specification of the OGEE framework — how contextual bandit formulation outperforms label-based training.
Graph-Based Correspondent Chain Prediction at Scale
How Neo4j graph traversal combined with ML ranking models predict correspondent banking chains across 50,000+ bank nodes.
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Our research team provides institutional partners with deep dives into specific corridor risk, regulatory trends, and compliance optimization strategies.
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