Many forms of frauds, including money laundering, involve groups of collaborating individuals and entities with myriad complex web of transactions. The group dynamically evolves and adapts to regulations and enforcement, making it resilient from detection. The evidence of fraud may only be apparent when the collective behaviour of these groups is considered.
Datamuse network analytics provides an additional layer of intelligence for analyzing group behavior for fraud detection, using a combination of network analysis and supervised learning. It provides risk assessment scores to individuals and entities based on positions and roles in the network.
The network-based solution detects relationships and transactions patterns to reveal potential fraudulent financial operations. Once an individual or entity is identified for potential suspicious transactions, the network analysis helps in identifying the different channels of tangible and intangible exchanges, groups working in collusion, roles of different people and diffusion of fraud in the network.
It provides actionable analytics to preempt suspicious behaviors by extending the notion of ‘know your customer’ to a network setting.