The financial services industry is a complex ecosystem, with many different functions, players, and data. The traditional model of financial services is rapidly evolving. Traditional banking, insurance, and investment firms are now being challenged by new entrants like fintech startups, who are disrupting the financial services industry by using innovative technology and data-driven strategies to create new value for customers.
The Financial Services industry is evolving at a rapid pace, and as such, there is a need for data-driven strategies to creating a competitive advantage. Traditional financial services firms are now turning to text analysis to gain a competitive edge.
Day-to-day operations in finance entail producing and consuming large amounts of unstructured text data from various sources. However, the manual approaches to data processing have over time been reduced in use and importance.
Because of this text analysis, the demand has increased significantly in recent years. The field of text mining is constantly evolving alongside artificial intelligence. The analysis of large numbers of financial data is both a requirement and an advantage for companies, governments, and the general public. Nowadays people predict and manage risks by text analysis, by making decisions based on factual data and keep their customers happy and overcome their competitors.
Applications of Text analysis in Finance
- To detect fraud, it includes an examination of all financial and investment reports, as well as a sustainability assessment.
- Text analysis aids in stock market forecasting and prediction. This allows those involved to base their decisions on facts rather than pure speculation.
Financial managers use text analysis applications such as money laundering and risk management.
Challenges for Financial Text Analysis
- Analysis can never achieve full accuracy due to the involvement of confidential data
- Text analysis models lack a well-defined understanding of financial jargon.
- Financial data is highly unstructured and redundant in nature.
- There are no dynamic text analysis models designed specifically for financial operations.
Text analysis Models for finance Topic labeling Analyzing text data to identify emerging topics in order to identify rising and falling financial market trends.
Sentiment Analysis Analyze feedback from your customers extracted from multiple sources and identify the sentiments of the market towards a brand market reputation. This helps in the prediction of stock market trends.
Feature Extraction Banking transactions necessitate a significant amount of textual data processing. Feature extraction is a technique for identifying and structuring documents from a variety of sources.
Entity Extraction Recognize entities from unstructured text and documents. You can use it to extract valuable financial insights from text data or to keep track of your competitors.
Semantic Similarities Comparing all financial products and solutions to see how similar they are. Identify similar data and use the tool to avoid financial report duplication.
BytesView’s Advanced Text Analytics Solution can help you recover and analyze large volumes of unstructured text data from multiple sources. Transform extensive volumes of text data and turn it into business intelligence. Predict and manage risk, make data-driven decisions and keep your customers happy and overcome your competitors.