Could Analytics Have Prevented The Banking Crisis?

Should they have been used more effectively?


American economist Edgar Fieldler once famously said that if you ask five economists the same question, you'll get five different answers - six if one went to Harvard. Nowhere is this more evident than when people discuss the causes of the 2008 financial crisis. However, if you’d asked them if there was a crisis coming at the start of 2007, as the four horseman of the financial apocalypse were appearing on the horizon, you’d probably have received a far more uniform ‘no’.

One of the most baffling elements of the 2008 financial crisis is just how few saw it coming. Those who warned of its likelihood were ignored, and even derided, with people like current Governor of the Reserve Bank of India, Raghuram Rajah, notoriously labelled a Luddite by Larry Summers for daring to warn of the risks from financial sector managers being encouraged to ‘take risks that generate severe adverse consequences with small probability but, in return, offer generous compensation the rest of the time.’ Something which is now widely accepted. Others, such as Meredith Whitney of Meredith Whitney Advisory Group, also called early how bad things would get when the housing bubble popped.

Economic theories, while so often considered a science by practitioners, are all based on political and ethical assumptions. Predictive analytics is a tool that has really come into its own post-recession, driven by the rise of Big Data. It is, arguably, free from the bias that economists have, and could have been used to reinforce the soothsayers’ warnings that the prosperity of the early and mid noughties was built on foundations made of sand.

There are a number of things that predictive analytics could have seen that might have helped governments and organizations to - if not alter the course of the ship - at least brace for impact.

One of the worst affected groups during the crisis was small investors. At the time, quantitative financial analysis was available only to high frequency traders, but a substantial drop in the cost of the technology in recent years has made it available to investors too, giving them far greater insights into where they should put their money and reducing the chances they will suffer massive losses.

In terms of putting an end to sub-prime mortgages - one of the main drivers of the crash - it is not clear how much predictive analytics could have prevented them. Theoretically, it should make it clearer which borrowers are a risk, though lenders at the time had a pretty good idea of who was a risk and lent to them anyway because it made them money. In future, regulators may be able to use predictive analytics to leverage insights that could help see when excessive sub-prime lending is occurring, and by who, so that they can try and minimize the damage.

Predictive analytics is an extremely useful tool. However, it is unlikely, given the amount of cash being made by those about to bring down the economy, as well as the level of ideological conviction held by people who could have prevented it like Larry Summers and Alan Greenspan, that it would have produced warnings that would have been heeded. At the moment, Wall Street and other such businesses are looking to predictive analytics in ways that would have been useful if they’d been around then, looking for trends and deviations that could imply a future drop in the markets. If the warnings of the past are heeded, and predictive analytics are taken notice of, they might just be able to stop another Lehmann Brothers fiasco.


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