Most of the information is present in text format such as government documents, business records, social media streams, legal acts, medical archives, clinical trials, and emails, to name a few. Owing to the rise in digital texts, there is an increased demand for text analytics.
So, it is essential to opt for a smarter option to read and understand text data. As the written word undergoes various transformations, ranging from the clay tablet's preserved inscriptions to the documentation stored in cloud systems, the information present in textual sources comprises the tools and capabilities for extraction and interpretation.
What is text analytics?
Text analytics solutions to transforming unstructured text data into structured formats. Text analytics breaks various phrases and sentences into components. It follows the evaluation of the role of every part through complicated machine learning algorithms and software rules.
Several years back, text analytics was made of simple and easy tasks. In the past few years, techniques such as deep learning and artificial intelligence technologies such as natural language understanding (NLU) have improved text analytics' efficiency.
More than 80 percent of the company's data is available in the text form of web pages, reports, call center notes, and emails. Text plays an integral role in getting a better understanding of their customers’ behavior.
It is beneficial to business organizations to understand their potential audience. It is useful in determining the customer's purchasing patterns and demands with the analysis of data, which is generated from different resources.
Text analytics solutions are regarded as the processes that involve text mining to evaluate the same insights. Such text mining techniques make use of various text mining applications and tools for the execution.
Use cases of text analytics in the finance industry
Fraud detection
Banks and firms offering financial services make the best use of text analytics to distinguish legitimate and real business transactions from fraudulent interactions. With the application of machine learning and text analytics, they will define normal activities, following potential customers' history.
It also helps differentiate it from the unusual behavior, which indicates fraud. The analysis systems also recommend specific immediate actions, including blocking irregular transactions, which are beneficial in stopping and preventing fraud. It is also beneficial in bringing an improvement in profitability.
Customer segmentation
At times, banks need to take certain steps to transform into the customer-centric business from product-centric ones. The best way to achieve a specific transformation is to understand the targeted audience's requirements through segmentation.
Text analytics tool is worth mentioning in this aspect as it provides the prerequisite opportunities for grouping the audience into unique segments, as defined in the sets of data. It includes daily transactions, customer demographics, interactions with the telephone, and online customer service systems. In addition to this, it is also inclusive of external data, which adds value to the business. Marketing campaigns and promotions are then targeted to the potential audience, as per the segment.
Regulatory and compliance requirements
Business organizations offering financial services act as a hefty regulatory framework that needs significant reporting and monitoring levels. In this context, the Dodd-Frank Act, which was created after the financial crisis in 2008, needs deal monitoring and documentation of every trade's details. Such data is beneficial for trade surveillance, which helps determine all sorts of abnormal trading patterns.
Risk management
Every business organization must undertake risk management. It is a must-have of every finance organization. For regulatory schemes, the business organization must manage different risks associated with market liquidity through stress testing.
It is also a prerequisite for financial organizations to handle customers' risks through different customer portfolio analysis. It is possible to manage the risks associated with algorithmic trading to aid back testing strategies against historical data. Once there is a surpass in the risk threshold, text analytics also supports real-time alerting.
Personalized marketing
Personalized marketing is another use case of the financial industry, which targets potential audience by understanding their individual purchase habits. Though the merchant records' text analytics tool supports it, it is also necessary for business organizations to offer financial services to incorporate unstructured data from the audience's social media profiles.
It will be beneficial to them to develop a complete understanding of the customer's requirements via the sentiment analysis. After understanding such requirements, data analytics must craft a credit risk assessment to understand if they should perform the transaction.
Text analytics is beneficial in the text analytics sector as they play an integral role in crafting the right data from the trend's unstructured text, uncovering patterns, and insights.
This specific technology is gaining a prerequisite understanding in the past few years, owing to its capabilities of predicting potential audience's behavior. This technology is beneficial across various business organizations such as market research, management of brand reputation, customer support and service, and competitive intelligence.
Summary
The finance industry has reaped a lot of benefits from text analytics. As the above use cases signify, you will understand how text analytics is beneficial in assuring a smooth and successful running of the business. It is also helpful in enhancing the revenue of the business.
