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What are Applications and Importance of Data Science in Finance?

  • Bhumika Dutta
  • Jul 18, 2021
What are Applications and Importance of Data Science in Finance? title banner

Data Science


Every company and organisation deals with huge amounts of data that they receive on a daily basis. Data Science is a multidisciplinary subject that uses different processes, advanced algorithms, and other complex scientific methods to extract information  and provide insights from structured and unstructured data to any company or organisation. 


In 2008, the term “Data Scientist” was first used when companies first realised that they require professionals to handle huge amounts of data everyday. Data Scientists are able to discover relevant questions, acquire and organise data from different sources, find solutions from the available results, and use them for the betterment of the business.


Today, data science is used in many fields like Healthcare, Targeted Advertising, Gaming, Augmented reality, Finance etc. Before data science was even a well-known industry phrase, financial organisations understood that analytical, inquisitive and solution-oriented experts could argue for statistical analysis, forecasting, and risk analysis over huge volumes of consumer and financial information may be required. 


Finance is one of the most critical fields to handle in the field of Business, as it requires a lot of time, accuracy and effort. Financial data science is required for management, market analytics, risk analytics, fraud detection, customer management etc. There are increasing numbers of transactions presently underway over the Internet and stunning volumes of financial data are created every day.


In this article, some applications of Data Science in the field of finance are described. Let us elaborately discuss those applications below.


(Read also: Data Science in Risk Management)


Applications of Data Science in Finance:


  1. Risk Analytics


In every business, the authority needs to make some important and unpredictable decisions,  and hence it is very important to predict and analyse the risks beforehand. It is very important for large companies as one wrong decision can lead to huge loss. Using data science, these risks are analysed and strategic decisions are taken.


Problem solving abilities, mathematics and statistics are required for risk analytics. The data scientist studies all the available unstructured data like financial transactions and customer information, organises them, and identifies and prioritises the risks present in them. Risk Management is one of the most important applications of Data Science.



  1. Customer Data Management


Every business has a set of customer base, with which comes huge amounts of customer databases. Due to the large amount of online and offline transactions and social media, there are both structured and unstructured data that needs to be managed and stored. 


Data Science and Big data are used to process and analyze them. Big Data plays a major role in Business Intelligence and through various machine learning and AI tools like data mining, Natural Language Processing (NLP) and text analytics, it can provide insights of customer behavior and feedback. That way, the company can have a knowledge of market trends and financial markets.


(Must Read: Top 10 Applications of NLP)



  1. Financial Fraud Detection


A business has many financial transactions happening on a daily basis. This increases the chances of fraud. Earlier, with traditional fraud detecting softwares, many legal transactions were interrupted due to broken rules and fraudulent activities. Due to development of algorithms, today it is easier to detect these scams earlier and the chances of these mishaps have also been minimized.


Through Big Data Analysis and Real-time Analysis, data scientists learn the usual and unusual patterns in finance and trading data and alerts the company in case of any anomaly. Trading and Insurance companies use data science to decluster data and segregate them to learn and study different patterns. This helped the companies check and eradicate any suspicious behavior and saved them from big losses through these financial scams.


(Also read: Types of Clustering Algorithms in Machine Learning)



  1. Real-Time Analytics


Earlier, Data Analysis required time. Since there are huge amounts of data to be  processed, the data were put together in batches and analyzed one by one. This had a huge disadvantage as the predictions on the basis of historical data were not as accurate.


Due to advancement of dynamic data pipelines and other technologies in the field of data science, companies can access the real-time data with minimum delay in the process. This helps the company gain accurate insights and track all the finances, credit scores without delay.



  1. Trading


Trading has been very popular recently and many people trade their finances online. Everyday, huge amounts of data are streamed online through Algorithmic trading, which is one of the biggest emerging technologies in the market due to Big Data and Data Science.


All the data are accumulated and segregated into Data Models that describe all the information about the data. Analytical Engines predict future markets by processing different data models. Another use includes determining the identification of market players using predictive machine learning algorithms.



  1. Credit Allocation


Every person who uses the internet has a digital footprint, which is a trail of information left by the user when he/she visits any website or page. It is basically a massive dataset containing a wealth of relevant information. 


There are various Machine Learning Algorithms that can scan and reveal the previously unknown relationship between new elements and behaviour of the user. These are supported by Big Data and high and complex computational technology. The insights provided can help in Credit Allocation and exceed standard credit scoring algorithms in forecasting whether or not a client will repay a loan.



  1. Consumer Analytics


Customization according to the consumer is a key business for most of the financial organisations. It helps them gain trust and increase their customer base and popularity.


Companies may get insight into customer behaviour in real-time with the help of data scientists and real-time analytics to make better strategies regarding the profit and success of the company. Many financial organisations, such as insurance firms, are using data science to better understand their clients in order to decrease losses by eliminating below-zero consumers, boost cross-selling, and calculate a customer's lifetime worth.


Importance of Data Science in Finance


It is now established that Data Science has many applications in the field of Finance. Starting from Risk Analytics to Consumer Analytics, Data Science and Big data helps any organisation in their business and finance, minimizing the losses and maximizing the chances of profit and success by strategic planning.  Here are some of the major importances of Data Science:


  • It helps in segregation and processing of Structured and Unstructured Data. Unstructured data, as compared to structured data, is quite difficult to handle. But, data scientists make the work easier by using various MySQL tools and other Machine Learning tools.

  • It increases the interaction of the company with it’s consumer through processes like speech recognition and Natural Language Processing. The data provided back through these interactions help the financial institutions gather information about their customer requirements, which in turn might lead to profitable situations.

  • Through Data Science, companies are able to predict Credit scams, Insurance scams and other fraud activities that lead to huge losses, sometimes even bankruptcy and downfall of the company. Today, 3D passwords, PINT codes, OTPs and text message confirmations have made online transactions very secure and user-friendly.

  • Data Science algorithms are able to detect any anomaly happening by learning different patterns. This helps to catch frauds who are using private information to exploit the market and are taking advantage of innocent traders. Thus, data science has had a huge impact on catching and punishing illegal trading in the industry.

  • Data Science helps the company know details about all its consumers, their quantitative values and other information by separating and grouping them on the basis of different attributes like name, age etc. and then choose those who are worth keeping. This saves the company’s resources, time and financial savings.

  • Data science in Financial Management reduces human error to a greater extent. Finance is a burden to manage, but it is also one of the crucial parts, in any business. Any simple mistake could lead to disbalance and losses. With data science, Artificial Intelligence and other technological advancements, the entire process has become automated and accurate.

  • Data scientists play a critical role in removing the guesswork from life-or-death choices in high-stakes circumstances such as recessions and financial crises. Whereas other roles within a company may be in jeopardy during an economic crisis, data scientists often find a rise in demand for their services since financial institutions rely on analytics to assist them discover where to cut costs and enhance efficiencies.


(Recommended blog: Economic growth vs economic development)





With all these, we have come to the conclusion that Data Science plays a vital role in Business Intelligence and Finance Management. Financial institutions rely on data scientists on a daily basis to execute essential activities such as fraud detection, risk modelling, and discovering ways to improve the customer experience.


Data science also provides insights into how well a product sells or to whom it sells, allowing financial services businesses and banks to create consumer products, policies, and investment instruments that are expected to sell well in the future. There are a lot of roles for Data Scientists as all big and small financial companies are hiring them. 

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