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Big Data uses in Financial Institutions

Big data is capable of enhancing the quality of risk management models because of the increasing availability and diversity of statistics. Now, big data can be used to simulate a variety of scenarios in order to realise all of the potential risks, leading to faster reactions to developments within the market. Big data also allows financial institutions to identify fraud quicker and with more precision by comparing internal and external data.

 

Beyond the risk management applications, big data can be applied to other areas in the world of finance. For example, big data provides far more ability to customise products through the use of location data, click streams, and social media analysis. This will enable financial institutions to better retain their client base, as well as attract new clientele. It is also worth noting that implementing the use of algorithms and big data to anticipate market exchanges has great potential.

UOB using Big Data for Risk assessments

Financial institutions are already in the process of implementing big data measures. One example of this can be see in Singapore’s UOB bank. It recently tested a risk management system that used big data to streamline the calculation of total-bank risk, reducing calculation time from 18 hours to mere minutes. In the future, stress tests can be carried out in real time, allowing quicker reactions to new risks.

Another example of the successful implementation of big data for a financial institution is Morgan Stanley. The bank launched a big data program to better their analysis of their portfolio’s size and results. With technology that enables pattern recognition, this analysis is set to improve risk management significantly.

Kreditech, a German business that offers credit scores for individuals, has also implemented a successful big data project. They use location data, social media analysis, online purchasing behavior, and web analysis for risk management. Similarly, American company Kabbage uses big data to analyze the risks of providing loans to corporate customers, using external data from social media and online delivery services. Finally, Paymint, a U.S. company that specializes in compliance, combats credit card fraud using big data analysis to recognize fraud patterns. Their big data software analyzes millions of transactions each month to manage risk.

How to successfully use big data

To successfully use big data for risk management, it’s prudent to implement a structured evolutionary approach in order to accommodate the broad scope of big data. Go slow. First, internal data should be collected and used. After that, you will have a clearer idea of data sources that will benefit you, so you can go to external sources, provided the benefit outweighs the cost. More important than amount of data is an integrated process of analysis. Taking small steps towards implementing big data programs allows you to identify any weaknesses or areas of risk.

The biggest obstacle to using big data is data protection, according to a recent study from the Fraunhofer Institute. Other obstacles include lack of knowledge, budgetary restrictions, lack of access to expertise, and prioritising other programs first. There are also smaller technical problems, such as immature technology, that are a factor, but they are much easier to fix. The importance of big data in risk management will be fully realized once these hurdles are addressed by a skilled data analytics company.

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