How Data Science is Transforming Financial Systems

Business + Management Financial Services Guest contributorposted by ODSC Community April 8, 2020 ODSC Community

Data science has continued to develop at a rapid and given birth to a whole new age of machine learning. Financial...

Data science has continued to develop at a rapid and given birth to a whole new age of machine learning. Financial tasks have started to see their roles reprised within the business world as more companies move forward with this innovative technology. Businesses are reshaping their strategies to incorporate these new technologies. This post is going to look at some of the creative ways that data science has transformed financial systems across all industries.

[Related Article: What Finance Companies are Excelling in AI?]

Data Science is Driven by Customer Behavior and Expectations

There has been a massive shift in the mindset of customers around the world. We’ve seen them start to raise their expectations. Banks are expected to provide online services to reduce personal interaction for convenience. But this benefits banks as well, so it’s win-win since they can collect more data about their customers through online transactions.

Customers also expect their customer-centric systems to be available around the clock. But for financial institutions to deliver this level of experience, they must have access to data.

Businesses Have Access to Automated Risk Management

Risk management is essential for companies of all kinds. Every business has to take risks, but it’s the ones who get a handle on risk management that are successful. This process has changed quite a bit over the years and transformed the entire landscape of the financial sector.

What makes this even more substantial is that risk management through machine learning is still in its earliest stages of development, and it’s already proving to be a potent tool. Imagine how much potential will be unlocked once it’s developed even further.

Look at this example. Financial institutions generate a large amount of data through transactions. They can use sales to gauge consumer behavior, market anomalies, and economic trends. Then analytical data systems can be put in place to organize this data into useful information that the institution uses to analyze risks. Credit card companies, for example, have systems in place to detect breaks in patterns and alert to fraudulent activity.

Predictive Analytics is At the Core of Financial Systems

This falls back to the previous example of spotting patterns in certain types of transactions but takes it a step further. We can now use data to predict future sales and find patterns in spending habits. Predictive analytics goes above and beyond, merely looking at transactions, though. It dives into social media, news trends, and a variety of other data sources to find directions early on. Seeing these trends shows businesses how they can intervene to get their piece of the pie.

Using financial institutions as an example, they have such large amounts of money at stake that they must be able to detect anomalies in commercial patterns. So they use business analytics to predict potential fraud and take the necessary steps to prevent it. This is automatically done with every transaction to protect consumers.

Creating More Efficient Ways to Manage Customer Data

Customer data is the most valuable resource for all businesses, but that is especially true in the financial sector. There is just so much data coming in from a lot of different sources that its management has become a constant resource strain. Data science and business analytics make this process much more streamlined, moving away from the struggles associated with trying to process it manually.

The best method of extracting useful data is through machine learning tools like data mining and text analysis. What this does is help businesses manage such a large volume of data without having to sort through it manually. For financial systems, this can mean the analysis of market trends and economic developments through historical data.

Financial Management

We’re living in a well-connected materialistic world, so managing finances has become quite the challenge. As we set our eyes on the new decade, we’re already starting to see artificial intelligence as the next step to financial management. Financial systems can collect data based on your online footprint and then automatically compile a graph that details your spending habits. Companies are using this sort of technology to track consumer spending patterns right now, so it’s not surprising that it’s being used as a financial management tool. We’ll start to see this incorporated on a much larger scale over the next several years.

Algorithmic Trading

Data science has also changed the way we look at trading. One of the most significant impacts of data analytics is through a practice known as algorithmic trading. Every moment is precious in trading, so faster decisions can lead to higher gains. Data science is used to develop systems that analyze traditional and non-traditional data, allowing businesses to make faster decisions. Since data loses its value over time, being the first to explain, it provides a significant advantage.

With that said, we can also use business analytics to combine predictive analytics with real-time information to develop statistical models. In the past, businesses could hire an expert just for this one job, but now we’re seeing it being swapped to artificial intelligence.

Challenges with Data Science in Financial Systems

We’ve seen a lot of the groundbreaking transformations that data science is having on financial systems, but there are also some significant challenges. The biggest problem is keeping all of these massive clusters of data security and address the ever-rising concerns over privacy. This criticism falls directly onto data analysis.

The challenge is to develop systems that consumers trust enough so that they freely provide you with information. As new laws are passed, we’re starting to see consumers gaining more power over their data, so this is going to continue to be the greatest challenge.

[Related Article: Generative Adversarial Networks for Finance]

Final Thoughts

Data science has become a game-changer across the financial industry, and businesses can reap the same benefits. There is such a vast amount of data in the world that machine learning and AI tools are the only ways to keep it in check. Otherwise, it is going to overwhelm most businesses.

Gracie Myers is a content writer at Research Optimus . She enjoys writing on various topics mainly associated with Research. Her famous articles are on the topic of Business Research, Market Research, Business Analytics and many more.