Can Big Data Help Us Predict Financial Crises
Our understanding of the vulnerabilities in the financial system has always been deficient. It is precisely because of this that we had failed to foresee the global financial crisis of 2007-8. Even the recent upheaval which has gripped Greece’s economy got overlooked because of this shortcoming. And every time something catastrophic happens we react by introducing institutional reforms, which by every means is akin to bolting the stable after the horse has escaped. Today, in the age of big data, is it possible for us to know when another financial crisis is imminent.
Why Financial Crises are Difficult to Predict
Financial systems are notoriously voluminous and so extremely complex to handle. Sample this: the world’s financial institutions produce enough data to populate 100 World Trade Center towers every hour. To foretell a crisis, this data needs to be monitored and analyzed non-stop, so that the turning points can be picked up early. This, by any means, is a gargantuan task — one which defies logic, resource and available means. So gaining timely and trusted insights from the emanating data has always been difficult, making it impossible to anticipate financial crises.
Big Data Offers Hope
Financial crises can be predicted only if we can extract meaningful trends from emerging data fast. To make this happen, financial data need to be aggregated, encrypted and analyzed real-time. Big data’s potential in helping us with such predictions can best be understood when viewed in light of the role it plays in detecting financial fraud. By integrating customer data from multiple sources, this technology has made it possible to predict fraudulent activity much before they actually happen. At a macro level, big data can be equally efficient in spotting kinks, as its capacity to analyze data real time is 36000 times over than the 20 terabytes of data required by the Exchange Commission to monitor all US capital markets activity every month.
With big data analysis, financial regulators will have anytime access to what’s going on both inside individual banks as well as broader financial domains like capital markets. For instance, if a bank’s credit policy is working well, analyzing competitors’ counter-strategy can help them keep a tab on the emerging market trends. This will place them in a better position to spot systemic risks before they surface and take a proactive approach to nip it in the bud.
The problem, as it now stands, isn’t much with the possibility of making accurate predictions, but with getting access to all forms of financial data. Client confidentiality and legal bindings to sharing data are big obstacles to accessing and storing data. Once access gets easier, regulators will be able to make the most of big data to predict future crises.
However, it’s naïve to believe that big data could have helped us predict Greece’s epic crisis. And that’s because, for making predictions hit the mark it’s important to give analysts or regulators access to accurate data. In Greece’s case, the data on government debt levels and deficits had been misreported for years.