Data Sources Frequently Used in the Financial Sector

Value of Data Sources:

Data sources Business experts now recognize the value of raw text and are using text-mining techniques to predict future trends, control costs, and gain valuable insights. According to research, 80% of business data is in the form of text, such as online reviews, surveys, and call center transcripts. The question then arises, how do we analyze this text data to improve business performance?

Text mining sometimes referred to as text analytics transforms unstructured text data into a structured format for improved comprehension and decision-making.

In this blog, we will discuss the most commonly used sources of text data in the financial industry and how to analyze them using text mining.

Central goal:

Text mining’s main objective is the analysis of text documents by removing information from a variety of sources, including emails, reviews, and official documents.
This process uses statistical analysis and machine learning techniques to visualize and analyze structured data. The biggest challenge in text mining is organizing unstructured data into a structured format. In simpler terms, text mining aims to uncover patterns and knowledge from text documents.

As already stated, text mining converts unstructured text data into an organized form, and its value has grown for the financial industry. Among the most popular data sources for text mining include social media, emails, chats, forums, and news articles. Some researchers use spreadsheets and PDF documents as their data sources.

Internal data generated by the financial sector, including transactional data and customer information, is utilized to guide decision-making. Additionally, additional data sources are used, including websites and social media.

According to Turner, Schroeck, and Shockley (2013), 71% of financial companies use big data analytics to improve their operations. As a result, many financial companies utilize big data technology to store and analyze a wide range of textual data for business purposes. Most of this textual information was gathered via the internet and social media in recent years. Connect (2018) outlined the top ten techniques used in artificial intelligence for analyzing textual data in the financial industry. Nassirtoussi, Aghabozorgi, Wah, and Ngo (2014) discussed the use of text mining to predict stock market trends using data collected from social media and news articles.

The finance sector’s two main data sources for text mining:

There are two primary data sources for text mining in the financial sector. They are internal and external data sources, respectively. Transaction data, application data, and log data are examples of internal data sources. Website and social media data are regarded as external data sources. Furthermore, Zhang and Zhou (2004) made the argument that social media, particularly in the financial sector, is a valuable source for data collection.

Data sources Collection :

Collecting data through external sources is a popular method as they are freely accessible and easy to use. However, using internal data can come with limitations and restrictions, particularly when it comes to detecting fraud. Researchers in this field face challenges such as selecting the best models and features for fraud detection. Internal data sources are primarily useful in legal contexts. Studies have shown that using internal data, such as financial statements, emails, and reports, can enhance human resource management, internal auditing, and customer management.

Recently, the financial sector has been utilizing named entity recognition as the primary source for extracting client information such as names, account numbers, and social media accounts. However, as Ritter, Clark, and Etzioni have noted, there is often noise present in the data when using supervised techniques for mining. In addition to named entities, forums are a valuable source of textual data in the financial industry. A time series model is the most effective way to analyze this type of data.

Text data sources:

Data is abundant in the business domain, and it has become the dominant source of information. Often unstructured, meaning it is not organized in a specific format, such as a spreadsheet or database. Text mining is the process of extracting useful information from this unstructured data and converting it into a structured form. This can be done by using various techniques such as natural language processing, machine learning, and statistical analysis.

There have been many research studies that have shown the effectiveness of text mining in the financial sector. For example, financial organizations can use text mining to analyze data from social media and customer reviews. Other external sources to make informed decisions. Improve their business. Additionally, text mining can be used to analyze internal data sources such as emails. Corporate documents to gain insights into the company’s performance.

Text mining offers a number of advantages:

One of the key benefits of text mining is that it can help financial organizations answer important. Business questions such as “where,” “when,” “why,” and “how.” For example, by analyzing customer reviews, a company can understand why a particular product is not selling well and take steps to improve it. Similarly, by analyzing social media posts, a company can understand. What customers are saying about their brand and take steps to improve their reputation.

In recent times, most financial organizations have used external data sources to increase revenue and customize their businesses. A good example of this is companies analyzing how often their products are viewed on an e-commerce website. Then using the reviews of the product to improve its quality of the product. Thus, in general, external data sources are the most often used data sources for text mining in the financial sector.

Conclusion:

In conclusion, text mining is a powerful tool that can be used in the financial sector to extract valuable insights from unstructured data. It can help financial organizations make better decisions and improve their businesses. It is important for financial organizations to have a clear goal in mind when using text mining. To use a combination of internal and external data sources to gain a complete understanding of their business.