How is data science used in the finance domain?
Posted by nithyav2k16 on July 14th, 2019
The usage of Data Science has been spreading in a vast array of industries.It has evolved briskly in past few years and it has proven to be of pivotal importance in various industries like marketing, manufacturing and finance.
The aim of this article is to cover the question of how data science can be used in the financial domain. Before jumping directly on where data science is used in the finance domain and what exactly is its application, let's first discuss in brief what data science is all about.
What is Data Science?
Data science is basically the computation of data from sources that can be structured or unstructured to get useful insights from such data. The sources discussed here include a survey done online or in person to get the information for clients such as customer behavior, preferences, likes, and dislikes. This data is combined together to understand future trends and patterns. The field of data science can be incorporated into a study group of humans, such as those associated with social media, forecasting weather, machine learning, artificial intelligence, statistics, analytics, and so on. The data science, if applied correctly, will help the organization reach new heights, taking into consideration the proper implications of insights of data.
Applications of data science in Finance
The use of data science is not only restricted to getting insights from data but also using those insights in decision making. With the financial field, we all know how important it is to make smart decisions and in a very short period. This is only possible if we have the relevant information in hand. Something that is clear is that there is a huge risk involved in any decision whether it is a short or long term. But if the decision is backed by sound data and insights then it surely gives a peace of mind to all role players and also lessens some risks that may be involved.
Listed below are a few of the examples where the data science is applied in the financial domain:
Customer and Risk Management
An increase in customer management, keeping intact profits through risk management and continuing to be ahead in such a fast-evolving world of finance is all possible on a correct and wide implication of data science. The companies would be able to effectively carry off the customer data to the best of their advantage and also foretell the future trends in particular sectors. This, in turn, would make them be prepared for any contingency in the near future.
For any financial institution, security is one of the biggest concerns because the fraudsters are continuously in search of the ways to shoplift monies and other products. Take an example of the stock market, wherein odd trading trends are detected by machine learning algorithms. In such a case, this would be investigated and the staff would be alerted.
Earlier the processing of data was historical in nature. This created a problem for various financial industries that needed real-time data to get insights into the present circumstances. With the application of data science, it is now possible to record a transaction, credit scores and many other financial aspects without any further delay.
Empowering Personalized Services
Financial institutions implement a wide variety of techniques to get useful information about customer interactions from insights of data. This would ultimately lead to an increase in profit as the companies would be producing and supplying in the exact way that the end customer needs.
Algorithm trading was impacted when there was big data coming into the picture. Thus, it has given rise to the most important feature that is data science. The data analytical engine is aimed at making predictions for future markets by getting a proper understanding of devastating data sets.
Thus, we conclude that data science in the finance sector is not limited to only one but many roles as mentioned above. It is an important thing to note that the use of data science is widely used in risk management and analysis. It is also used for detecting fraud to find abnormal transactions. Attend a good training to master these data science skills.Also See: Data Science, Risk Management, Finance Domain, Machine Learning, Science, Data, Insights
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