How has Data Analytics Changed the Finance Sector and What to Expect?

Gone are those days when the accountants used to hover around the calculations machines. By the way, things are doing now, duties and tasks of the modern-day accounting professionals may well be taken over by advanced technology. We can see how big data and data science is changing every industry, and finance is no exception. With many advanced technologies coming over the conventional methods of finance administration, we can expect such things to take over every sort of financial services and revolutionize the industry.
Big data and data analytics are largely consuming a major chunk of the financial duties in corporate sector, while defining some others by reinventing the conventional role of an accountant into a more advanced and modernized profession, who tend to rely more on analytics to better perform the financial operations, budgeting, financial projections, and management.
Almost all the finance-related duties deal with data of some sort, so this not only becomes more logical to implement analytics into finance, but it can also contain the mass amount of valuable data an organization produce daily and convert it into actionable insights for the betterment of the business.
Importance of data analytics in finance
The relevance of data analytics in finance and banking sectors have been long realized at a larger scale by most of the top performing financial institutions and banks across the globe. By using the advanced data analytics methods effectively and adapting to big data technologies, those establishments are reaping more benefits too.
For example, you can find how the top American banks are now using machine learning and analytics to comprehend the customer discounts the private bankers used to offer to their customers. Bankers are claiming that they are offering discounts only to the most valuable customers.
However, based on data analytics, you may find out that this is a different story in reality. So, one may be able to come into conclusion as to whether discount patterns are needed or not, and which approach should be correct. With this, the bank can instantly adapt to the change, which will ultimately lead to an increase in revenue as experienced by many.
Lately, in the United States, a major industrial survey which was conducted by covering 20 of the leading banks across EMEA region showed that there were many areas of improvement by adopting data analytics, which when worked upon started delivering great returns. As per the findings of that study, the major areas of improvement were:

  • Aligning priorities of data analytics to strategic vision making for the financial institutions.
  • Integrating the decision-making process with proven analytical practices
  • Developing more technology-based analytics assets at a larger scale and then investing more on roles which are critical to analytics.
  • Enabling a calculated user revolution where the data ownership is clearly defined alongside maintaining top-notch data.

Financial support organizations like also effectively make use of data analytics and big data methodologies in effectively managing individual and corporate debts.
To gain the competitive advantage of data analytics, the financial institutions need first to recognize the criticality of data management and science, and try to incorporate it in the decision making. The ultimate objective of this exercise is to reach up to the level of developing business strategies based on data-driven insights. For the beginners, it is ideal to start small by adopting easily doable steps and then try to integrate data analytics slowly into the operational models to stay ahead of the competition.
Some ways through which data plays vital in financial services
You can read further to understand the unique ways through which data analytics and big data play a vital role in transforming the global landscape of financial services.

  • Investments and trading

In real, now all the hustle and bustle at Wall Street trading is too little compared to what machine learning techniques do. Unlike human perspective, machine learning methodologies can have a wider-reaching view,  beyond analyzing just the buying and selling prices, but to analyze the best options for an investor at any given moment.
While doing this, a good data analytics tool can also take into account the political, social, and market trends alongside checking the most trending brands and social media buzzwords to take the most current decision on stock options. Moreover, machine learning methodologies help make these decision real-time based on live data analytics. The data is also compiled and evaluated to be presented in a human-understandable manner.

  • Reforming tax

In many cases, when there is a need for the collection and process of a massive volume of data in a precise manner, big data is not proving to be extremely efficient. You can find taxation as one such area where this can be applied at best. In fact, taxes are done each year for business or persons, and there is a huge volume of financial data getting generated from past to current. The analytical software can access this data over a longer period and process it by eliminating any human errors and do perfect taxation.
However, even when it helps to eradicate human errors, big data or data analytics methods will not eliminate humans from processing the data. However, the new role of taxation professionals will be more centered on big data and data analytics in the coming year. Taxation may be revolving more around the data collected, stored, managed, and well organized in order to ensure that the analytical potential of modern technology is fully leveraged accurately and responsibly.

  • Investigation and fraud detection

We can see that almost all transactions create a long trail of data. This huge volume of data can be stored and assessed through data analytics and machine learning methods to understand the user behavior and buying habits of the customer of a specific business. This will benefit the customers also as if your credit card is misused by someone, then the machine learning analytical methods can identify the transactions which are contradictory to your common habits and alert the bank and users immediately.
Credit cards also produce a huge volume of data which may end up in wrong hands too. Data analytics through machine learning will also help to police any such illegal activity and help stop the fraud at the first point itself. In a lot of cases, the users will get a customized notification on your mobile phone asking whether you initiated a payment or not to help you ensure financial security and privacy.