Technical deep dives, industry insights, and practical guides on AI-powered anti-money laundering from our team of experts.
Technical articles, industry insights, and best practices from our team.
Discover how AI and machine learning are revolutionizing real-time risk assessment for financial institutions, enabling proactive fraud detection and prevention.
A deep dive into how Graph Neural Networks analyze transaction networks to detect sophisticated money laundering schemes that traditional systems miss.
Learn how behavioral profiling and continuous learning models can reduce false positive alerts by up to 85% while improving detection accuracy.
Comparing architectural approaches for transaction monitoring, with benchmarks on latency, throughput, and detection effectiveness.
A technical guide to extracting meaningful features from transaction data, including velocity metrics, network features, and behavioral deviations.
How to build interpretable ML models that satisfy regulatory requirements for model explainability, auditability, and transparency.
Comparative analysis of AI and traditional rule-based systems for detecting currency structuring and smurfing schemes.
Technical approaches to monitoring international wire transfers, currency exchange, and cross-jurisdiction money flows.
Best practices for validating ML models in production, including backtesting, A/B testing, and ongoing performance monitoring.
Implementing GDPR-compliant ML models using differential privacy, federated learning, and homomorphic encryption techniques.
Using LSTM and Transformer models to detect temporal patterns and anomalies in transaction sequences.
Best practices for building high-performance, scalable APIs that integrate AML detection into transaction processing pipelines.
Analyzing trade invoices and shipping data to detect over/under-invoicing, phantom shipping, and circular trading schemes.
In-depth technical documentation on our AI models, architecture, and methodology.
Comprehensive 40-page technical document covering our platform architecture, ML model design, feature engineering, deployment patterns, and integration strategies.
Detailed analysis of our ML methodology including model selection, training procedures, evaluation metrics, and comparative results vs. rule-based systems.
Navigate the regulatory landscape with AI-powered AML. Covers compliance requirements, audit trails, explainability, and regulatory approval processes.
Request access to our complete technical documentation and API reference guides.
Real-world implementation stories showing measurable results and best practices.
Case study: $5B regional commercial bank deployed nerous.ai to transform their AML operations. Details on implementation, change management, and measurable results.
How a leading crypto exchange integrated blockchain analytics with real-time ML models to achieve comprehensive coverage across 12 blockchains.
Digital-first bank used nerous.ai API to launch with full AML compliance in 4 weeks. Details on API integration, cost savings, and regulatory approval.
Find resources organized by key AML and AI topics.
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