NLP Driven Sentiment Analysis in Financial Services: 4 Use Cases
NLP-driven sentiment analysis can enable banks to shape a truly customer-first experience.
Antiquated thinking, outmoded operating models, and inaccessible support still have a place in many financial companies. In recent years, digital transformation has started entering the financial industry with online banking solutions and fintech applications, making financial services more accessible and convenient for customers. However, banking can open much more appealing opportunities and fill the gaps in customer service by using NLP-driven sentiment analysis.
For financial companies, sentiment analysis acquires greater significance as it allows understanding the customer behavioral and emotional response to various service-related issues and the bank image in general. Powered by artificial intelligence (AI) and natural language processing (NLP), sentiment analysis and text mining automation can accelerate the digital transformation of financial institutions.
What is sentiment analysis?
Sentiment analysis has its roots in natural language processing (NLP). NLP is the field of artificial intelligence that, with the rise of ML and computational abilities, has become even more scalable. It is an interdisciplinary field that combines neuroscience, linguistics, math, and computer science under one roof.
According to TechCrunch, NLP is one of the most progressive fields that has made the most advancements in the last 3 years.
NLP-based apps are everywhere, from chatbots to voice assistants like Amazon Alexa, Google Assistant, and Siri.
Sentiment analysis is an area of natural language processing used to detect the sentiment of data. Companies frequently apply sentiment analysis to different types of texts, like customer reviews, social media conversations, support tickets, etc., to research real customer needs and assess brand reputation. Sentiment analysis is leveraged to identify the polarity of information (positive vs. negative), emotion (anger, happiness, sadness, etc.), and intention (e.g., interested and not interested).
Sentiment analysis is an important aspect of brand reputation management. It enables companies to understand the overall customer attitude, their challenges, and their needs. It allows structuring data by different sentiments and making more accurate predictions and better strategic decisions.
How can banks leverage sentiment analysis?
Sentiment analysis has found its ground in the financial industry. Banks and financial companies can leverage AI-powered sentiment analysis systems to gauge customer opinions about their financial products and the overall brand image. This approach might require a certain level of data competence that financial companies will need to reach before starting sentiment analysis projects in earnest. Sentiment analysis is a very promising NLP field and will definitely have a place in the financial services of the future. Here we will consider the four most popular applications of sentiment analysis in financial services.
Research of customer emotional responses
Financial companies can use AI-powered tools to process and analyze vast amounts of such data to determine different sentiments of customer conversations about the banks in their posts or comments on social media, transcripts of conversations from chatbots, customer reviews, or survey responses. The segmentation of customer sentiment data allows financial companies to investigate the people’s attitude towards products, services, and experiences with a bank and improve those areas accordingly. Also, companies can apply sentiment analysis to research how a target audience responds to their competitors’ campaigns and consider developing a similar strategy.
More effective credit market monitoring
Financial companies can leverage sentiment analysis to track credit sentiments from the media. Articles, press releases, and news about the credit markets have a lot of data about the performance of securities provided by organizations. Special NLP tools and software can process this information from the sentiment perspective. It allows banks to monitor the sentiment data related to specific bonds or create lists of the organizations that are talked about most positively and negatively. AI analytics can detect correlations between the media updates and the performance of credit securities in the market. This AI-enabled analysis can significantly reduce the time and costs needed for in-depth financial research.
Advanced research for equity investing
Sentiment analysis has already been used in trading and stock investing for the years. However, with the fast evolution of NLP and machine learning, many tasks related to trading and investing can be automated. For instance, AI-based research of the brand mentions in social media and news websites can significantly input the investment strategy and decision-making process.
For example, sentiment analysis can be leveraged to integrate the investigation process involved with equity investing divisions in banks. Experts might use the software to research more companies since the AI algorithm can compile and prioritize the most appropriate equity data for every organization from different sources.
Compliance monitoring in banks
Compliance monitoring is another area in which NLP-driven sentiment analysis can bring multiple benefits. Like financial trading data, compliance divisions in banks and some financial companies store many records of compliance regulations and should regularly update their processes to correspond to these mandates. Otherwise, banks might take the risk of losing their licenses and credibility. Therefore, most large banks and financial institutions have their own IT teams responsible for updating this information. The software that they use to scour the regulatory websites for updates is mostly rule-based and outdated. AI-powered sentiment analysis software systems are poised to improve the process by ingesting huge amounts of this information and categorizing every update by relevance.
Conclusion
NLP-based sentiment analysis is a new technique that can empower financial companies to process and structure large volumes of customer data with maximum benefits for both banks and customers. It’s poised to help traditional financial companies and neo-banks to improve current customer experiences, reduce friction in financial services and create better financial products.
