Examples of Data Science in Finance

Let’s look at a news headline from Nasdaq:

“The Rise Of Financial Data Scientist”

Key Takeaways:

• The finance industry is the one that expects the highest ROI for investing in Big Data, after the logistics industry.
• It is incredibly difficult to find senior financial data scientists, so the large scale banks are recruiting candidates who have acquired specialized skills through Data Science programs and bootcamps.
• Financial Data Scientists have the unique ability to utilize technology so as to reduce costs and enhance profits, fuelling the demand for them.
• As stated by a study conducted by EY on the future of finance, 57% of them noted that it is crucial to brush up skills in predictive and prescriptive analytics.

There are many reasons that fuel the demand for data scientists across the globe.

For the second year in a row, the Data Scientists job has been at the top of the list of 50 best jobs, as reported by Glassdoor.

The salary of Data Scientists can go as high as 19Lpa.

That’s huge! Isn’t it?

Moreover, Harvard Business Review refers to Data Scientist as “the sexiest job of the 21st century.”

Additionally, data scientists are required in all the sectors from manufacturing to finance, healthcare to IT regardless of your geographical location or current domain.

Another reason for choosing a career in Data Science is that it has plenty of sub-roles such as Data Architect, Database Administrator, Data Engineer, Data- and Analytics Manager. And a good thing about these roles is that they are in high demand across the globe.

Applications of Data Science in Finance

There are many financial companies that train machine learning models to use a large amount of data obtained from different sources like customers, rivals, markets, etc, in order to improve ROIs and enhance cost-efficiency and sustainability. It allows companies to forecast significant parameters such as customer retention schemas, stock market moves, etc.

There are a lot of ways in which Data Science is changing the Finance Sector. Financial management is no longer a tedious task for companies, and the credit goes to data science.

From fraud detection to risk analysis to algorithmic trading, data science is changing the way the finance sector functions. Let us read some of the ways in which Data Science is being used in the Finance Industry.

1.    Risk Analysis

The most crucial part of the finance industry is Risk Analysis. It is impossible to do strategic planning and maintain dignity without going through risk analysis. Risk analysis reports can guide customers regarding investment in the market.

Data science has changed the approach to risk analysis resulting in a transformation in the Finance industry. So, you should understand the concepts of statistics, problem-solving, and math. Some of the sources that generate risks are customers, competitors, shareholders, markets, and others. It is required to analyze the huge amounts of data coming from customer interactions, financial transactions, etc, and train the algorithms to optimize their risk score so as to reduce their risks, thereby enhancing cost-effectiveness and sustainability.

For implementing machine algorithms and data science in the finance sector, companies need to prepare themselves by enhancing the analytical skills of the finance team, automating core financial processes, and making strategic technology investments.

2.    Fraud Detection

Fraud is always anticipated where there is a finance-related task. It is the primary responsibility of a finance company to guarantee security to its customers/stakeholders. This is the reason why fraud detection and management is the most crucial challenge for the finance team. This is the point where a skilled and trained data scientist comes into action. As a data scientist, you have to design such a perfect algorithm that can and prevent anomalies in the work process or user behavior. For example, when there are unusual cash withdrawals or financial purchases that are not frequent, there may be some chances of fraud.

There are machine learning algorithms that can analyze unusual patterns in trading data and hence catch investment fraud. There are clustering algorithms to find insurance-related fraud and other frauds.

3.    Consumer Analytics

In the end, consumers are a critical part of the finance sector. By using predictive modeling and consumer data, you can observe consumer behavior. There are machine learning algorithms that allow you to perform customer sentiment analysis to obtain insights from client behavior, their feedback, and their social media activities. The results can be used to enhance profit and improve personalization. There is a massive amount of consumer data to be handled and to accomplish this task you need a trained and experienced data scientist to perform a precise breakdown.

4.    Real-Time Analytics

Real-time analytics has brought about a transformation in financial processes by analyzing huge amounts of data which is obtained from different sources. This lets you quickly identify any changes thereby finding the best possible solution to the problem.

The real-time analysis helps a company to know the state of the market, their risks on investments, and the stakeholders. Eventually, it helps a company in making better decisions.

5.    Algorithmic Trading

Algorithmic trading has a significant impact on real-time analytics. It is important to have the most recent information regarding traditional and non-traditional data so that the finance company can make real-time decisions. Having the fastest methods of analyzing real-time data makes your company be at a competitive edge.

By initiating algorithmic trading strategies, Data Science and AI have brought a revolution in the finance sector. With techniques such as NoSQL, Hadoop, and Spark, and the most precise algorithms, you can change the way your company responds to the market changes.

6.    Customer Data Management

The finance sector has customers at its core. You need to take care of your customers by knowing their requirements and working on fulfilling those. Customer data is available in both structured and unstructured forms. Structured data can be obtained from official forms such as details form and feedback forms and is easy to analyze and handle. Unstructured data creates chaos as it is in different formats which are obtained from social media posts, online feedbacks, and emails. You require an experienced data scientist to handle this kind of unstructured data by utilizing tools such as Hadoop, Spark, etc.

7.    Personalized Services

A company becomes the favorite of its customers when they get personalized services from your side. The financial sector also does the same, gives some personalized services and proper attention to each of its customers. To give personalized services by using the data generated from customers and analyzing it.

The most common example of personalized services is chatbots.

It is customer satisfaction that can improve the efficiency of your company thereby improving sales.

Conclusion

Besides the applications of Data Science in the Finance sector, other implementations include social media interaction, process automation, improved level of security, and many other tasks. It can be observed that Data Science is bringing revolutionary changes in the finance industry.

To make a career in this domain, you should take up an online training course in order to get certified and career guidance as well. There are some training providers that provide you the choice in mode of learning and learning hours too. They conduct doubt sessions to ensure that you are well prepared and guide you along your career path.

So, get yourself enrolled now!

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