Today’s highly quantitative risk management industry is the product of the simultaneous advances in computing power and finance theory we have seen since the 1960s. Exponential increases in computation speeds have allowed academics and practitioners to create a wide range of mathematical models able to process vast amounts of historical data and develop numerous projections of the future. Although they are undoubtedly helpful when used appropriately, the resulting tools (now ubiquitous across the industry) have led to an over-reliance on numerical estimates of risk. The language of risk is dominated by the terms “volatility” and “value at risk,” creating an unintended blind spot in relation to risks or trends that are inherently difficult to measure or quantify.
The following quote from Lord Kelvin (inscribed on the wall of the social sciences building at the University of Chicago) could quite easily be the slogan of today’s risk management industry:
“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind.”