Application of Data Science in Finance Industry

Finance is one of the crucial sectors worldwide. One of the major concerns of the financial sector is preventing fraud. Fraud can be the loss of customers’ crucial data, money, invalid claims, fraudulent transactions, and many more. With the advancement..Read More

  • Updated on 28th May, 20
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Let us look at the agenda for this blog on Data Science in finance industry:

Why should we use Data Science in finance?

With the increase in digitization and online transactions, the risks of online fraud have increased rapidly. This is due to the customer data leakage as there might be glitches in online transactions. As customer data is an invaluable resource of this digital era, we need to protect it from fraudsters. This is where Machine Learning and Data Science in finance help, preventing customers as well as organizations from financial losses. Let us understand the uses of Data Science in finance with the help of a use case.

Let us suppose that a customer has an account in Citibank. The bank collects the data of the customer’s transactions, loans, debits, credits, online shopping, and many more. The software used by the bank keeps track of each and every activity of the customer’s account.

After collecting the data, it analyzes the transaction history, transaction amounts, the frequency of transactions, and many other factors. Also, the security software used by the bank records the time and location of the transactions through Data Science techniques. The system flags a transaction if any unusual activity is found such as transactions made from two different locations at the same time.

Moreover, the bank evaluates its potential customers by their account balance. If a huge amount is credited or debited from an account, then the system marks the transaction as suspicious and checks for its validation. Further, on the basis of customer data analysis, the bank decides to give loans, offers, and other benefits to the customers. This helps in making cordial relations with the potential customers. This also enhances its revenue generation.

All this is only possible with the involvement of Data Science in the finance industry. Now, we will go through some of the applications of Data Science in finance industry.

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Customer Data Management

As mentioned, data is one of the major resources in the field of business. Nowadays, most organizations utilize customer data to enhance their productivity. The data of customers helps financial institutions keep track of their transactions. In earlier days, organizations stored data using traditional methods. But, the rise of various technologies has given a new direction to the finance industry.

Data Science in Customer Data Management

Nowadays, technologies used by the finance industry are Big Data tools, Data Science, Artificial Intelligence, and Machine Learning. With the help of these technologies, data management has become a lot easier than in the earlier days. Data Science in finance is helping financial institutions manage and store their customers’ data efficiently. This data stored by these institutions can be structured or unstructured. However, the tools of Data Science are capable of processing, storing, and segmenting all forms of data. The stored data can be processed for preparing the financial reports of these organizations.

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Risk Analytics

For any company, there lie risks of security, competitors, financial loss, losing customers, or business failure due to some reasons. Especially for financial institutions, like banks and insurance companies, risk analytics becomes one of the essential business processes. Risk analytics helps in creating specific business strategies to maintain the efficiency of the business. Also, it helps in creating trustworthiness in the market as well as among customers.

Nowadays, the processes carried out for risk analytics use Data Science and Machine Learning. Data Science in finance analyzes market trends and customer data. Then, it tries to find potential threats with the help of Machine Learning tools. The software used for risk analytics help make strategies to prevent business failure. Moreover, by analyzing the risks, organizations can predict the ups and downs of business due to various global market activities.

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Financial Fraud Detection

In the digital era, there is a rapid increase in online transactions. Along with this comes several unethical activities all over. For instance, there are cases of false insurance claims that have led to great loss in the financial sector. To deal with such critical problems, financial institutions have started deploying various technologies. One of those technologies is Data Science. The tools of Data Science are integrated with the business software to keep track of customer transactions and their past history.

Financial Fraud Detection

With the help of Data Science in finance, the security systems of the financial institutions have become extremely secured and productive. Also, there is a decrease in credit card fraud in comparison to the financial history of the last 5 years. The algorithms of the software are designed in such a way that the software tries to learn from past data. Then, it uses its learning experience to predict the risks that may occur in the future. By this, Data Science helps the financial companies from degrading.

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Personalized Services

Personalized services are the key feature in today’s business world. The involvement of new technologies in customer services has made it possible to boost the overall growth of any business organization. These services use Data Science and Machine Learning for building applications that provide a personalized experience to customers. This has proved to be effective for financial institutions as well.

Personalized Services

Let’s consider the working of a bank. With the help of Data Science, the bank application analyzes customers’ data such as:

  • Frequent transactions
  • Shopping history
  • Debits/credits
  • Repaying of loans
  • Bank balance

There can be more hidden factors for evaluating customer data. With the help of this evaluation, the application provides a credit score for each customer. Based on the credit score, the bank identifies its profit-generating customers. Then, it provides offers, discounts, loans, and policies that can be beneficial for these customers.

Also, with Machine Learning and AI-based chatbots, customer services have improved a lot. In most of the areas of the financial industry such as insurance companies, loan providers, trading companies, banks, etc. use chatbots to provide high-quality customer service. Nowadays, these firms have also started using speech recognition technology to improve interactivity with customers. All these applications of Artificial Intelligence and Data Science in finance are helping businesses generate high profits.

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Automation of Risk Management

Every business involves some risks that may ruin business profits. The digital era has come up with a lot of advanced technologies that can save any business from unnecessary losses. One of the advanced technologies used by most of the financial organizations is automation. The automation of tasks and business processes is possible with the help of Data Science and Machine Learning. In the finance industry, they are used for the automation of risk management.

Risk management is a crucial business activity that helps in analyzing faults in a business. To prevent the faults and inconsistencies in the business, the software for security systems analyzes data and tries to detect unusual patterns using Data Science. Also, with the help of Machine Learning algorithms, the software learns and improves itself for the better prevention of faults in the future. In the case of a financial firm, if any risk is involved in a financial activity, then the software notifies and blocks the activity for security reasons.

With the help of the application of Data Science in finance, the automation of risk management has become much more efficient, and this has led the organizations to have great improvement in business management and growth.

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