The Rising Importance of Data Science in the Financial Industry

Written by Sandra Rosa Prince


Details and information regarding every purchase we make, of any product or service, are stored as data in every business. We are today, living in an era where data is of paramount importance and provides a very important resource in understanding useful information. Without data, how do you know who your customers are? Without data, how do you know if customers like your services or if your marketing efforts are efficacious? Without data, how do you know how much money you are earning or expending? Data is crucial in understanding your customers, the market and its trends and direction. Simply put, data helps you see, understand and improve performance. And therefore, a more profitable analysis of such data leads to better decisions which boost profit for financial institutions. But to take full advantage of data and analytics, you need to know how to make the most use of your data, by deriving relevant facts, insights and ideas from it. This is what data science accomplishes. Data Science gives a scientific, innovative, and investigative review of big data. At its heart, data science is a field of study that aims to use a scientific approach to extract meaning and insights from raw data.


The earliest application of Data Science was in finance. Data Science emerged as a solution to rescue the institutions from losses. It enabled them to categorize the customers based on past expenditures, current credits, and other essential variables to analyse the probability of risk and default. In financial firms, Data management systems perform critical roles: like bringing and organizing data from various sources, linking it together and making it available across the organization. The use of Big Data and its analysis in accounting and finance is the most within these four areas: Financial distress modelling which refers to the prediction of risks of financial distress or similar situations, Financial fraud modelling which is detecting fraudulent or unauthorized behaviour or activity, Stock market prediction that means to predict the future value of a stock on an exchange, Quantitative Modelling that is the analyzing and interpretation of data sets to identify trends in the market, and Auditing referring to the assessment of financial operations in an organization.

"Data is the oil of the 21st century and analytics is the combustion engine."

This quote by Peter Sondergaard clearly tells the importance of analyzing raw data and using it to understand your business and expand your profits. Instead of converting thermal energy to mechanical energy, data science acts as a combustion engine by converting raw data to useful and highly valuable information. 

 HDFC Bank, India's leading private sector and the world's 10th largest bank that offers many personal banking services and net banking facilities was one of the first organizations to take steps toward data management and analytics. It started with Big Data analytics in 2004, intending to boost its revenue and understand its clients and markets better than its competitors. Data science and analytics have been integral in helping HDFC Bank segregate its consumers and offer customized or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in selling relevant offers to its consumers. Apart from regular fraud prevention, it assists in keeping track of customers' credit scores and has also been the reason for the speedy loan approvals offered by the bank.


The world of finance is transforming at an unprecedented rate. Data science has entirely modified the face of traditional finance management. Data science is essential to almost every field, but it finds an integral position in the finance sector as it can be applied to many financial problems like risk management and many others. With the increasing need for Data Science in all the industries and increased amount of data, the significance of Data Science has increased because it can analyze such a large quantity of data to get insights. Numerous new and existing financial institutions are now moving toward a data-driven business model due to the amount of insight and direction that can be extracted from consumer data. As the finance sector continues to digitize, there will be more and more raw data to sift through and interpret which will consequently lead to a significant need for data scientists and analysts. Firms that use data science algorithms in finance have better chances to achieve customer loyalty, prevent losses, and remain ahead of the game.

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