How important is Data Science in Finance?

It is estimated that data-driven transactions in 2020 are worth $1.2 trillion. This is about four times the value just six years ago. Many companies now rely on big data to make essential business decisions. These data are generated in large volumes and then broken down and analyzed. Important information about them is extracted to make organizational decisions. This field of study is called Data Science.

Data Science or also called Data Analytics can be structured in three main fields, descriptive analytics, predictive analytics and prescriptive analytics. The first basically, as its name suggests, describes the behavior of some past situation or result, in other words it explains why something happened. The second field is in charge of predicting some specific event, based on historical data behaviors. And finally, prescriptive analytics, seeks to establish a recipe to obtain the best possible outcome in an environment of great uncertainty.

This science field deals with the extraction of knowledge from big data, analyzing the data, and presenting information generated from the data to help organizations make crucial decisions. It is applied in almost all business industries. Data science can be used in marketing to create a targeted advertisement, in governance to allocate resources, in technology for artificial intelligence, and development of computer software. There is no sector where this new scientific knowledge has not found application. Among the leading sectors is finance.

Applications of Data Science in Finance

The financial sector is one of the most critical sectors in every society. It involves a massive volume of data, a large number of participants, and a lot of gains and risks. The sector is also susceptible and must be well developed for the economy to thrive. Dealing with the complex nature of the industry used to be challenging, but with the help of data science, many things have been simplified. What are the applications of data science in finance?

●      Financial Forecast

In forecasting, companies extract data, monitor the trend of the data, and predict the future behavior of the data. Financial bodies can use data collected over some years about business performance to predict the future performance of the business. Such performances could include profit and loss, revenue generation, production, sales, and more. Data Science has greatly made forecasting more accurate. With a better method of collecting and analyzing data, it is easier to observe the trend and make an accurate prediction.

●      Financial Reporting

Financial reporting is a critical aspect of finance. It involves making financial information available to the users and stakeholders. Such information includes the financial statement of a company or an account owner. Financial reporting also includes financial information given to the press by the business organization, financial information on the company’s website, and the minutes of stakeholders’ meetings.

Financial reporting typically involves a large volume of data. Therefore, if a proper technique is not applied, processing and communicating them may be cumbersome. Fortunately, data scientists have developed many techniques to make financial reporting easier and more efficient.

●      Risk Analytics

Every business transaction involves risk, and in the financial sector, it is twice as much. Every institution assesses the risk involved in a business before going for it. This is called Risk Analytics. Banks and other corporate lenders usually determine the creditworthiness of an individual or organization before providing credits. Data scientists are generally employed in finance to use information obtained from different sources to predict the credit behavior of their customers. This prediction will help the financial house decide if it is safe enough to provide a loan for the customer.

●      Fraud Detection

No sector deals with fraud as much as the financial sectors. However, with data science, a financial institution can prevent fraud as much as possible. Among the most common frauds facing the industry is the credit card scam. Now, there are tools that can detect abnormality in spending patterns in real-time. It is now easier to see if the pattern suddenly changes by analyzing data from the card owner. The credit card issuer gets an alert in real-time and will tackle the issue immediately. Not only credit card fraud can be minimized by data science, but almost all kinds of fraud in the industry.

●      Marketing

Finance, like any other sector, uses machine learning algorithms to understand the nature of its customers. With that, they can send out targeted advertisements and offers. From the information obtained from websites, it is possible today to know individuals' likes, preferences, and interests. Knowing this will help the marketing department of a financial institution channel its resources in the right direction.

●      Customer Relation

Like sending out targeted marketing, data obtained about a customer can be used to provide personalized treatment and services to the customer. This will improve customer experience, build loyalty and increase the customer's bond to the institution.

●      Investment

Financial sectors constantly invest in different businesses. Before putting money in any business, you need some specific information. Data scientists are now helping financial houses to get the necessary information to make business decisions. This will include risk analytics, profitability, competition, change in trends, among others.

Using artificial intelligence to make investment decisions will eliminate human errors and biases. The software can also process data faster than humans. Hence making an investment decision with the aid of Data Science is both convenient and efficient.

●      Algorithmic Trading

Algorithmic trading is an automatic trading process in which a financial institution lets software automatically make trading decisions like buying and selling of shares. Algorithmic trading involves complex formulas based on the available data to assist the computer system to make the right decisions. Data Science has played a significant role in simplifying the complexity of algorithm trading and enabling businesses to make accurate forecasting of future behavior of stocks and other investments.

Conclusion

Finance is one of the most critical sectors in the world. Finance management used to require a lot of effort and time, but not anymore. Using Data Science, now one can quickly analyze finance and make better decisions to better manage their finances. The same can be extended to other services such as credit risk analysis, fraud detection, pricing optimization, trading, and other parts of finance as highlighted above.

Data science uses scientific tools to extract knowledge from big data and analyze the data such that the information generated from it can help organizations make critical business decisions. Data Science is applied in almost all fields, and its application is expected to continue increasing. In finance, Data Science is used for risk analytics, forecasting, investment decision making, marketing, personalized customers relation, fraud detection, algorithmic trading, and many more. Now, with the massive amount of big data, we can connect it with machine learning algorithms.

Data Scientists have developed many tools which make the financial industry more secure, profitable, and efficient. Recently, many companies incorporate this technology to deliver their services and operate more effectively.

The knowledge has significantly reduced the challenges faced in finance due to its complexity and sensitivity. It is believed that in a few years, it will even get better.