Mayur Rele Describes How Data Science is Revolutionizing the Finance Industry
According to Mayur Rele, as the world advanced into the era of big data, the need for its storage also grew. Storage became the primary concern and challenge for the enterprise industries until 2010. The main focus then was to build solutions and frameworks to store data. Now when Hadoop and other frameworks successfully solved the problem of storage, the focus has shifted to the processing of this data.
Data Science is the secret sauce here. All the ideas one sees in Hollywood sci-fi movies can be turned into reality by Data Science. It is the future of Artificial Intelligence; therefore, it is essential to understand what Data Science is and how it can add value to businesses.
Data Science is a blend of various algorithms, tools, and machine learning principles to discover hidden patterns from the raw data. Data Science is primarily used to make predictions and decisions, making use of predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning says Mayur Rele.
Finance has always been about data, and matter-of-factly, finance and data science go collectively. Finance has been using it long before the term data science was devised. Just like how banks have been automating risk analytics, finance industries have also used data science for this task.
Finance industries understand data as a fundamental fuel and commodity. It transforms raw data into a meaningful product and makes use of it to draw insights for better functioning of the industry. Finance is the hub of data, and financial institutions were among the pioneers and earliest users of data analytics. Data Science widely used in areas like customer management, risk analytics, algorithmic trading, and fraud detection.
Why data science is used in Finance
Financial industries need to automate risk analytics to implement strategic decisions for the company. With the use of machine learning, they monitor, prioritize, and identify the risks — these machine learning algorithms model sustainability and improve cost efficiency through training on the enormously available customer data, says Mayur Rele.
Similarly, financial institutions make use of machine learning for predictive analytics. It allows the companies to predict their stock market moves and customer lifetime value.