Financial institutions face increasingly complex financial crime threats, dramatically higher operating costs, and greater regulatory expectations. This combination of factors is driving a need for contextual analytics, which can help transform financial crime programs by helping them become more effective and efficient and, ultimately, provide law enforcement with better information.
The benefits of contextual analytics
Contextual analytics applies advanced analytic techniques including machine learning and artificial intelligence to both internal and external data to bring context to events happening within financial institutions. That context makes true financial crime traces readily discernable.
Contextual analytics uses a data-driven systematic study of the internal and external circumstances that surround the focal entity of an event, such as a transaction monitoring (TM) event. Contextual analytics takes advantage of big data, machine learning, contextual scoring, predictive modeling, network scenarios, analysis and visualization, entity resolution, and natural language processing to provide more insights into the activity occurring in a financial institution’s network.
Currently, transaction monitoring systems (TMS) generate an excessive amount of noise, or alerts that are explainable and do not warrant human investigation. By treating all activity generated by a TMS as an event and continuously reevaluating these events as part of a triage process rather than moving directly to alert and, therefore, investigation, financial institutions can shift to a truly risk-based event assessment process.