Imagine watching all the money movements in a bank as if they were a big social network: people and companies are dots, and payments are lines between them. This system uses AI to spot unusual and suspicious patterns in that network—like circles of accounts passing money around in strange ways—so compliance teams can catch money laundering much faster and with fewer false alarms.
Traditional anti–money laundering (AML) systems throw off huge numbers of false alerts, miss complex laundering schemes spread across many accounts, and are hard to adapt to new criminal tactics. This approach uses deep learning plus graph (network) analysis to identify sophisticated laundering patterns in transaction data more accurately and efficiently than fixed rule-based systems.
Combination of institution-specific transaction history, labeled suspicious-activity data, and proprietary graph features/topologies tuned to that bank’s customer base and channels, which together are hard for competitors to copy.
Classical-ML (Scikit/XGBoost)
Vector Search
High (Custom Models/Infra)
Graph construction and traversal on large-scale transactional networks; training and serving deep models over high-dimensional, evolving graph features in near real time.
Early Majority
Uses deep learning combined with graph (network) analytics on transactional relationships, going beyond traditional rule-based or simple supervised AML systems by explicitly modeling multi-hop, many-entity laundering patterns and potentially uncovering novel typologies.