In a previous article we discussed what problems models and their assumptions introduced which could have precipitated the subprime crisis. Now let’s look at the analysts or the modelers themselves.

There is a pithy quote from someone (it could be the Black Swan guy) which goes something like "Dont cross a river because it is on average 4 foot deep".

In the highly readable account of the recent crisis, John Cassidy's book, "How Markets Fail" there is a very clear and illuminating diagram that helps to trace the genesis of sub-prime mortgages. We want to borrow his line of thinking and his chart to explain how the crisis really spun out of control starting with sub-prime loans and how detailed real time analytics with systems thinking would have helped. Here is the diagram, which he calls the Mortgage Chain.

This blog is about measurement of information content in data and how business analytics and risk professionals can adopt this simple and intuitive technique.

We recently encountered a fairly experienced risk manager who was very well-versed in quantitative methods, unlike a vast majority of "check-box" risk managers. His question was, what new information does entropy give him that his current sophisticated Bayesian models and analyses don’t? Business analytics professionals may have the same question.

The venerable World Economic Forum recently published their 5th annual "Global Risks" report. While clearly this is a very timely analysis, we see a few problems with their report.

Traditional risk analysis involves developing what are known as "ordinal scoring" scales. For example, this requires getting executives to answer questions like "what is the likelihood that the next major database upgrade will be delayed", "what is the impact of the hurricane season on the availability of medicines for our troops" etc.