Rules-based systems have several limitations and challenges, which have historically hindered their effectiveness in detecting and preventing financial crimes.  

For example, the rules and thresholds need to be manually created and updated, making the system less adaptable to evolving and emerging threats.  Similarly, rules-based systems also typically analyze individual transactions in isolation and may not provide a comprehensive view of customer and counterparty behavior or relationships. They may miss the connections between seemingly unrelated transactions or accounts, which can be crucial in identifying more complex fraud networks or money laundering schemes. 

In contrast, by combining all at once, Contextual Monitoring transforms the view of risk to a gain clearer understanding of customers, counterparties, their relationships, and behaviors in real time. Using advanced and network generation techniques, Contextual Monitoring focuses on holistic relationships rather than transaction risk in isolation. 

This added context helps to identify hidden risk and generates fewer, more accurate alerts. and operational costs and conduct more effective and efficient intelligence-driven risk processes without replacing existing systems. 

Turning data into intelligence is imperative for reducing manual processes, identifying critical connections and breaking down silos between teams. For shifting to an intelligence-led approach goes beyond