Why Financial Professionals Need to Have Knowledge of Big Data?

December 29, 2017
Why Financial Professionals Need to Have Knowledge of Big Data

The role of a financial professional has evolved leaps and bounds over the last decade due to the changing nature of the business. Initially, the key expectation from a finance manager was to check accounts to analyse variance between historical performance and forecasted performance. In doing so one could certainly answer the question about how vast the variance was, but even upon investigation could not get to the root cause of the variance. It was hence incomplete information that was available and any decision was taken would not be absolutely accurate or in the right direction, anyways this worked back then. In recent times, decisions are taken basis a multi-dimensional approach, results are compared with different variables like economic data, customer preference, markets, and only then can we say that a strategic and informed decision is made. And in this process, Big Data Analytics is an enabler.

This entire process is demonstrating that a new trend in the financial profession has initiated, Big Data Analytics skills are no longer an additional skill set that could help you get a job, but is soon turning to a must-have skill, which has become a pre-requisite for anyone pursuing their career in finance. Keeping up with the need, the Chartered Financial Analyst (CFA) is set to add questions on Artificial Intelligence, Automated Investment Services, and Mining Unconventional Sources of Data, as a part of their examination.

Three Primary Reasons why big data has become essential for financial services.IBM Machine Learning Banner

  1. Profitability – By getting insights from the vast volumes of data, helps the financial organisation to focus on target products and potential earning areas, this way they can design and launch their products for maximum returns.
  2. Customer Engagement – The huge volumes of big data can give specific insights into customer behaviour and preferences, towards certain products and services offered by the bank. This will help institutions build a better relationship with the customer and in turn increase customer satisfaction.
  3. Risk Mitigation – Good use of data analytics can assist banks to analyse the risk element that the financial institutions are always exposed to. They can then take the advantage of listing out potential defaulters, or risk agents and minimise the exposure to risk.

There are new areas that have developed in the financial processes where big data analytics applications can be or are used.

Procure to Pay Analytics, here data analytics ensures that updated data is maintained by the various departments, analytics also help them cross verify budgets and develop internal controls. Another process is Order to Cash, it refers to a collection of payment for goods sold and services rendered. Predictive Analytics is perhaps the most needed by professionals which help in forecasting future revenues, preparation of budgets, hedging, cash flow etc.., each activity can be tracked for probably high and low bills, which will help them manage cash flow more effectively. These are just a few besides, Risk assessment and Portfolio Stress Testing and other Risk Mitigating Controls that are possible due to the intervention of big data analytics.

The above-mentioned scenario is not processing what banks will do in the future; these are specific processes that are being followed by the financial institution of all statures. Due to the rapid growth in technology, there is an immense scope of analytics. Hence staying up-to-date with the new technology is mandatory, especially for those who intend to pursue a career in finance.
New aspirants should strongly consider taking up a certification or a course in Business Analytics and Data Science to upscale their prospects.

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