Investment management firms use predictive analytics to optimize processes and to identify early warnings of process anomalies. Predictive analytics use historical data to predict future outcomes based on current data, and use machine learning to create models that help identify patterns and behaviors that predict risky situations. This research explores how Operations teams can leverage predictive analytics and models to provide much earlier warnings to predict and prevent operational loss, and presents some options on how to get started.
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