As of 2012, about 2.5 exabytes of data are created daily, a number that doubles approximately every 40 months (Harvard Business Review). That is enough data to fill 50 billion file cabinets of text…each day.
While not all of the data analyzed will provide significant value to the risk management professional, some of it – the proverbial “needles in the haystack” – provides key indicators of risk that prove exceptionally useful in assessing or mitigating risk. Integrating the use of big data enables better identification of correlations, and allows users to analyze the data to identify and potentially predict the possibility of future risk being introduced, i.e. within an acquiring bank’s merchant portfolio.
The challenge that risk professionals now face is in understanding what to do with those predictions and correlations identified. The difficulty lies not only in the vast amounts of big data collected, but also in the increasing complexity of the risk environment in global business relationships.
Prescriptive analytics combines business rules with mathematical and computational sciences, along with big data, to make predictions that are used to enable targeted business decisions.