How Big Data is Used to Better Understand Financial Riskby Fintechnews Singapore January 13, 2017
I should imagine you have read a lot about big data, even if you are not really sure what it is. The definition is simple, it’s a very large volume of data. The amount of data typically requires complex computational methods to analyse and reveal patterns which are of value to the organisation.
Therefore, the primary discussion centres on the methods to store and analyse this data and the various use cases. One such use case is to underpin financial risk management systems. Let us take a big step back and consider why this would be an important use case – and we only need to rewind time by a decade to the major economic downturn caused by the catastrophic failures of banking institutions.
This was in a time when most people believed in ‘too big to fail’ and as a result risk management did not have a seat at the table. The results were disastrous, and the failure for the banks themselves to predict their own demise has led to the FCA having a far more pervasive role in risk management.
Risk management relies on an understanding of the financial system to the extent that this is an area of research in itself, with some researchers calling for regulators and investors to consider the financial system similar to any other ecosystem.
In this sense the banks, regulators and customers are actors within this dynamic ecosystem with their respective strategies evolving over time. But what does this really mean? There is huge value in understanding the characteristics of a customer base, in this case the borrowers.
For example, what they spend and when, how often they miss payments, and so on. This data is powerful for a bank to assess their ongoing liquidity position, but the volume of data to derive useful insight is huge, and this is where big data comes in. This is a granular example – but I am sure you can appreciate how this data can be build up to the point where systemic risk management is possible.
Furthermore, the more data you have, the better your ability to build useful models where scenario analysis can be run to determine risk profiles of certain events. Different types of shocks can be hypothetically applied to the model, revealing critically needed understanding of the impact on the system as a whole.
The natural next step is to then work with the banks and other financial institutions to put in place business continuity strategies and perhaps to consider diversification when risk in certain areas of business becomes unmanageable.
The volume of available data presents a big opportunity, particularly from a risk management perspective. However is does present a challenge in terms of how to manage that data and analyse it to produce valuable business input. This will continue to be a key question that the banks, regulators and risk management software providers have to answer.
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