How is Data Science Used in Finance?
The goal of data science is to take data and through various methods, processes, systems, and algorithms, extract relevant information. Since finance is all about interpreting data, it’s not surprising that data science has transformed the finance industry. Powerful tools such as machine learning technology and Artificial Intelligence (AI) are being put to use throughout the finance industry, but there are a few areas where the use of data science in finance is most notable.
Finance is all about balancing risk and reward and data science allows companies to do this more effectively and efficiently. The economic security and stability of people all over the world are dependent upon the finance industry. This means the industry has a responsibility to mitigate as much risk as possible in areas ranging from security to decision making.
One way in which data science is helping mitigate risk is through machine learning technology. This technology relies on algorithms to detect anomalous behavior. For example, certain algorithms can detect credit card fraud by looking for anomalies in a customer’s spending or review trading activity to find suspicious trades. The effectiveness of these algorithms at detecting fraudulent activity is incredibly high.
Another example of data science in financial risk management is companies using machine learning to evaluate the creditworthiness of customers. By looking at a customer’s spending habits and looking for specific patterns, data scientists can “teach” machines to evaluate potential customers even if the customer has a little credit history. Though still in its infancy, this technology shows a lot of potentials.
Data Management and Analysis
The amount of data out there is vast and continues to grow. From transactions on the market to social media activity, firms need to have the ability to gather massive amounts of data. But they need the ability to analyze the data and gather useful insights, otherwise, the information is useless. This is where predictive analytics comes in. As the name implies, predictive analytics not only gathers and analyzes massive amounts of data, but it then makes predictions based on the data. Predictive analytics is especially important in finance since so much of finance is about predicting when certain events, such as moves in the stock market, will occur.
The ability of data science to gather, maintain, and analyze data and then make predictions is transforming finance, but the ability to analyze data in real-time has an especially unique impact. Real-time analysis means not just analyzing massive amounts of data but looking for patterns and figuring out the best way to react at the moment. Two areas where real-time analysis is changing finance is fraud detection and trading.
Fraud detection is incredibly important for companies in the financial sector, but historically has been quite difficult and occurs after the fraud has occurred, but advances in data science are changing that. For example, when an unusual purchase is made with a customer’s credit card, the purchase can be blocked until the customer confirms the purchase. Since these algorithms are self-learning, the more they’re used, the more effective they become.
Real-time analysis has also been used to assist in trading. With trading, every second matter. Therefore, the faster you can analyze data the better position you’re likely to be in. Algorithmic trading can analyze thousands of different data sources all at once to make predictions about the market. Algorithmic trading allows traders to make better trades based on more informed decisions.
Real-time data analysis has also transformed the level of personalization that companies can provide and allows them to better understand their customers. This is essential in this day and age, where customers have come to expect a more personalized approach. The amount of data required to better understand a customer is massive and complex. Data analytics can sort through this data to provide highly personalized advice based on the combination of where customers live, how they spend their money, social trends, demographic trends, and much more. One company putting this technology to use is Wells Fargo, which uses AI-driven chatbots to assist with basic concerns customers may have.
Since data science has allowed more data to be reviewed with fewer resources, it has opened up financial opportunities to more people. It doesn’t require looking far to see this trend – Robo advising has only been around for about a decade, but it’s already managed to transform the industry by providing personalized advice to almost anyone.
This is Only the Beginning
Data science is changing the face of finance, but its full impact has yet to be seen. There’s a shortage of people knowledgeable about the latest updates in data science and the required research and development is still prohibitively expensive for many firms. As these barriers are eliminated, data science will likely be able to transform finance in ways beyond our current imagining.
Learn more about the role of financial machines in finance.
Related Course: Data Science for Finance Professional Certificate
This Professional Certificate course will teach you how to extract valuable insights from financial data with the powerful Python programming language. You will learn how to wrangle data from many different data sources as well as the fundamentals of machine learning. By the end of the course, you will have developed highly relevant and sought after analytical skills and the tools to develop your own financial modeling or algorithmic trading strategy using Machine Learning.
About The New York Institute of Finance
The New York Institute of Finance (NYIF) is a global leader in professional training for financial services and related industries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The New York Institute of Finance has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes. Founded by the New York Stock Exchange in 1922, NYIF has trained over 250,000 professionals online and in-class, in over 120 countries.
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