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Big Data in Finance


Big Data in Finance

In today’s world of finance, the word massive data is very often used by experts. So in this article, we will see what massive data is, its importance and its uses in the field of Finance.



Massive data represents a large amount of diverse information that is collected at an increasing volume and speed. It is stored in computer databases and can be structured or unstructured. In addition, for the analysis of these data, the use of software specifically designed to manage large amounts of information is required.

This data can be obtained from various sources including social networks, websites, business transactions, smart devices and industrial equipment…


Importance of massive data

Thanks to massive data, the financial companies have acquired the ability to process and analyze larger volumes of data to help them in their decision-making mainly. Among other things, this allows for much more accurate analysis, customization of products for different types of customers and many other potentials.


Use of massive data in Finance

In the field of Finance, massive data are used for different services in the sector. The following lines highlight real uses of massive data in the financial sector.


1. Overview of stock markets in real time

The use of massive data has completely revolutionized the functioning of stock markets and more precisely the way investors around the world make decisions. The use of massive data coupled with methods such as machine learning allows computers to use powerful computer algorithms and make accurate market predictions in order to make decisions. These analyses take into account political and social trends that may affect the stock market. In addition, it allows transactions to be made much faster than humans and at higher frequencies. Thus, the use of massive data and algorithms allows analysts to make smarter decisions by reducing the risk of errors due to behavioral biases and influences, as well as obtaining optimized information to maximize portfolio returns.


2. Data analysis in financial models

Massive data analysis offers many possibilities for improving prediction models. This allows better approximation of rates of return and investment results in order to obtain much more accurate predictions and reduce risk. In addition, it makes it possible to automate financial reporting, to help financial departments be more productive, and to allow users to access and analyze information more easily.


3. Customer analysis

For financial institutions, activities are carried out around the customers and oriented towards their satisfaction. Thus, customer analysis is one of the best methods to provide quality customer services. The use of massive data allows institutions to anticipate future customer behavior in order to generate products or services that will increase customer satisfaction and to seize market opportunities. As a result, massive data allows them to analyze customers and implement strategies to reach customers in a much more efficient way. 


4. Risk management and fraud detection

Financial institutions’ risk management and fraud detection decisions are based on massive data analysis. By including economic, business capital, and customer segmentation factors, risks such as bad payers or bad investments can be identified through data analysis. Thus, financial groups such as banks or insurance companies use the data they hold to prevent and reduce the risk of fraud.

Thus, through the different topics discussed, one realizes the importance of massive data and the impact that their use has brought to the finance sector. This sector will continue to develop thanks to new technologies.



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