Why The Future of Finance Is Data Science

Why The Future of Finance Is Data Science

The entire process of working is going through fast changes with every advance in technology. Top financial advisors and leaders now see the future completely reliant on data science.

Automation is occurring in all industries, and while some jobs will become streamlined, that does not necessarily mean lowering the number of employees. With new technology, people need to reexamine software, information storage and even give up some responsibilities to Artificial Intelligence.

Statistics vs. Data Analytics

Statistics are a vital part of learning customer basis and seeing exactly what is occurring within the finance company and how it can be improved. There is a difference between analytics and statistics.

Vincent Granville, data scientist and data software pioneer explains this in the simplest forms, “An estimate that is slightly biased but robust, easy to compute, and easy to interpret, is better than one that is unbiased, difficult to compute, or not robust. That’s one of the differences between data science and statistics.”

Data science did evolve from a need for better statistics, and once big data arrived, the standard statistical models could not handle it. “Statisticians claim that their methods apply to big data. Data scientists claim that their methods do not apply to small data,” Vincent Granville also comments about the main differences.

New Roles In Finance

Finance is not left out in big data reform. There are specific roles that need attention to stay at industry standards.

A few of these roles are:

  • Statisticians
  • Data Scientists
  • Roboticists
  • Data Security Staff
  • Software Specialists

Technology needs updated, so data scientists can create new business models, and the finance industry can begin using real-time data. Finance is one of the fields that can really use data science to more easily handle

Going through the finance office’s equipment is a big step in reformatting the equipment, or even upgrading to partial cloud systems if you are unsure of completely moving the company to the cloud. Cloud computing reduces costs from bug fixes in on-site software, which can also improve productivity with information being accessible by all in the company, even if off-site.

Incorporating Data Science Software

Tech has moved away from not being understood by the masses, to a more simpler form advertising to all related companies. For a finance team to use advanced data science models, they will need to reformat the way their business runs, so with the blanket statistical work, there used to be little need to absorb those systems. Now different models are made specifically for finance

In an EY study, financial teams were asked what skills are critical for the future of finance. More than 50 percent of those included in the study said that predictive analytics was the biggest focus. This involves handling data at a larger scale and processing it to make use of the model.

The cloud is, perhaps, the most substantial change in storing data, and can provide cost-saving options to finance teams. Data Science is turning into a coveted profession where many companies expect at least some knowledge on the matter.

Employees can become threatened by automation, but even if one part of their job is done through new tech, that does not mean their role in the company is completely depleted. Statistical skills must, and probably do, exist with the company, so all of this viable data can be handled, and analyzed through several different models.

There are a large group of finance teams already working towards focused data science models.  What can your existing team do with these new tools? You may be surprised how new software or a switch to cloud computing can change how a finance employee approaches a job.

Artificial Intelligence and Finance

Data Science also related to Artificial Intelligence. You may wonder, “what does that have to do with finance?” It is pretty simple actually, AI can open the doors to smart technology, robotics, and the very basic act of automating specific jobs.

AI and blockchain are working towards providing real-time data analytics, which changes the relevancy of analyzed information-for the better. Robotics is focusing on transactional accounting work, and with the AI becoming more advanced, it is proposed by 2020, it will take over the job completely in financial offices.

George Soros, a billionaire financial tycoon, had the ability to predict the financial scene and become an undeniable finance leader; known for his high bet against British money in 1992. He quickly evolved the outlook on statistics and used the emergence of big data to make knowledgeable decisions in the financial scene.

“I occupy an exceptional position. My success in the financial markets has given me a greater degree of independence than most other people. This allows me to take a stand on controversial issues: in fact, it obliges me to do so.” said the billionaire financial entrepreneur. Soros has taken philanthropic causes to heart, seeing as 79% of the Lifetime net worth of George Soros has been donated.

Searching For Data-Minded Employees

New staff cannot be avoided when moving forward with incorporating data science into your finance office. The existing staff can become involved in figuring out new approaches to this new technology, but at least one data scientist, engineer or big data expert should be hired to drive the organization and drive of other’s less experienced.

The higher-ups of the financial world find this to be a matter of slowly bringing in professionals, but also implementing teaching to current employees. The financial world will always be a mix of the traditional ways of operation and the priority to adapt to industry standards, which is now focusing on data science options.

When predicting future talent needs, Dicks says that finance leaders should assess at a sub-function level, such as accounts payable, accounts receivable, tax and investor relations.

When in finance, accounts payable is a common term, but even with utilizing the system functions, the task is very repetitive. Areas like these use the help of robotics and machine learning models, so a computer can perform this task.

The conversation of adding technology to a finance atmosphere needs to be an honest one. Even if it means changes for the company, there are real advantages to incorporating data science sooner than later. Since data science is a broad area encompassing many different analysis methods, there are ways to make finance thrive through this new software and experienced personnel.

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