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Previously, we discussed the number of risks a business can face in its lifetime.

Big Data holds the potential to bring about a digital revolution which will transform existing businesses to a great extent. World’s data is exploding in volume, velocity, and variety, faster than ever before.  By 2020 our accumulated digital data will grow from 4.4 zettabytes to around 44 zettabytes.


‘Time is critical in the new world of risk management. If you can react to a risk faster, you have a competitive advantage’, Jason Hill. In this fast-changing world, businesses are always searching for methods to gain a competitive advantage over their competitors. Many utilize big data in many fields but risk managers are yet to exploit it to their benefit. It can improve the predictive power of risk models making them more stable and reliable.


Enabling real-time analysis, big data makes risk monitoring more efficient. Asset managers, banks, and insurance companies can use data analysis techniques and statistical tools to detect potential risks and reduce reaction time. They can make an informed decision after considering a wide range of risk variables.

Big data can be extensively used in frauds and money laundering management. Extraction of data from all possible sources, quick interpretation and processing of it allows for early detection of fraudulent activities. This helps mitigate the damages caused by it.

The growing number of scandals today necessitates the termination of traditional approaches of anti-money laundering. In the absence of automated algorithms scanning through a plethora of documents and reports becomes a laborious task. Also, statistical analysis, data mining and advanced visualization of raw data can uncover suspicious transaction patterns in no time.

Big Data Analytics

Counter party credit risk evaluation involves very complex calculations. Counterparty risk refers to the risk of default or that the other party will not meet its end of the bargain in a contract. Large banks typically run 1000-5000 Monte Carlo simulations for this purpose. However, for complete accuracy, they are to perform 100,000 simulations. Traditional technologies fail to cope with such hefty processes at a high-speed. Banks with updated Monte Carlo abilities enjoy a comparative advantage.

Big Data analysis makes it possible for analysts to work with an entire population rather than a selected sample. Moreover, the sampling process is subject to errors and is therefore not very reliable. An added advantage of Big Data is that it significantly reduces the cost of risk management, with automation, more precise predictive systems and lower risk of failure.


As observed by the Economist Intelligence Unit during a survey, Big Data has been most successful in the prevention of credit card fraud followed by prevention of defaults and compilation and analysis of liquidity requirements. As markets are becoming more and more interlinked the threat of systematic risk has increased. Thus, Big Data analysis has become an essential tool for risk management.

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