AI-Native AML: Technical Architecture & Implementation Guide

Executive Summary

This whitepaper provides a comprehensive technical overview of the nerous.ai AI-native anti-money laundering platform. True to our name—nerous, Finnish for genius, ingenuity, and brilliance—we detail our innovative system architecture, machine learning models, real-time processing pipeline, and production deployment considerations for financial institutions.

1. Platform Architecture

1.1 High-Level Overview

The nerous.ai platform is built on a microservices architecture designed for horizontal scalability, fault tolerance, and sub-100ms transaction analysis latency. The system processes over 100 million transactions per day with 99.99% uptime.

1.2 Data Flow

Transactions flow through the system in the following pipeline:

1. Ingestion: Transactions arrive via REST API or batch upload, validated and normalized
2. Feature Engineering: 500+ features extracted including velocity checks, network metrics, behavioral deviations
3. ML Inference: Ensemble of 12 specialized models analyzes transaction
4. Risk Scoring: Outputs unified risk score (0-100) with explainability
5. Case Generation: High-risk transactions automatically create cases for review
6. Feedback Loop: Analyst decisions feed back into continuous learning pipeline

2. Machine Learning Models

2.1 Graph Neural Networks

Our primary detection mechanism uses GraphSAGE architecture for analyzing transaction networks:

• Architecture: 5-layer GraphSAGE with attention mechanisms
• Embedding Dimension: 128 dimensions per entity
• Neighborhood Sampling: Up to 10 hops with adaptive sampling
• Training Data: 10B+ labeled transactions
• Update Frequency: Incremental learning every 24 hours

2.2 Anomaly Detection Models

Unsupervised models identify statistical outliers:

• Isolation Forest: Ensemble of 200 trees for fast anomaly scoring
• Autoencoders: 5-layer encoder/decoder for pattern reconstruction
• Local Outlier Factor: Density-based outlier detection

2.3 Temporal Pattern Recognition

LSTM and Transformer models analyze transaction sequences:

• LSTM: 3-layer bidirectional LSTM for sequence modeling
• Transformers: Attention-based models for long-range dependencies
• Sequence Length: Analyzes up to 180 days of transaction history

3. Feature Engineering

We extract 500+ features from each transaction, grouped into categories:

4. Performance Metrics

5. Integration Patterns

5.1 Real-Time API Integration

Synchronous REST API for transaction scoring at authorization time. Typical use case: payment processors needing instant risk decisions.

5.2 Batch Processing

Daily batch analysis of historical transactions. Typical use case: banks analyzing end-of-day transaction files.

5.3 Streaming Integration

Kafka consumer for continuous transaction streams. Typical use case: large enterprises with existing event-driven architectures.

6. Deployment Options

6.1 Cloud (SaaS)

Multi-tenant deployment on AWS/GCP with automatic scaling, managed updates, and 99.99% uptime SLA. Typical deployment time: 2-4 weeks.

6.2 On-Premise

Kubernetes-based deployment in customer data centers. Includes air-gapped deployment option for high-security environments. Typical deployment time: 8-12 weeks.

6.3 Hybrid

Sensitive data on-premise with ML model updates from cloud. Balances security requirements with operational efficiency.

7. Security & Compliance

• SOC 2 Type II certified
• GDPR compliant with data residency options
• AES-256 encryption at rest, TLS 1.3 in transit
• Role-based access control (RBAC) with SSO integration
• Complete audit logging for regulatory review
• Explainable AI with model decision justifications

8. Conclusion

The nerous.ai platform represents a new generation of AML technology built from the ground up with AI at its core. Our architecture delivers the performance, accuracy, and scalability required by modern financial institutions while maintaining the explainability and audit trails demanded by regulators.