Traditional Risk systems can be a ‘black box’ that take in required inputs and generate necessary analytics with little or no ability to understand how the results are calculated.
Understanding the details of models used in the generation of analytics can be challenging – specifically if this is needed by the user soon after running analytics for deeper results analysis. Getting such details from existing risk systems may involve request for details from the support team first and then putting this together in a form that is easier to review – a process that can take anywhere from a few hours to more than a day and may not be acceptable.
Understanding the model parameters, model details and all data inputs used to generate the analytics in real-time from will not only help in calibrating the models dynamically but also provide a faster way to communicate the details of ‘how’ of analytics to the portfolio manager/clients.
Of course it goes without saying that such transparency will allow managers to ‘better’ position the portfolio for the future that meets the risk/return needs in the investment policy.