Many financial institutions still use outdated operating models, outdated thinking, and inaccessible assistance. With the advent of online banking and fintech applications over the last few years, financial services are now more readily available and user-friendly than ever before. NLP-driven tools, on the other hand, can open up considerably more tempting options and fill in the gaps in customer service in banking.
Sentiment analysis is becoming more important to financial institutions because it helps them better understand their customers’ behavioral and emotional responses to various service-related concerns and the overall bank image. Digital transformation in financial institutions can be accelerated through text mining automation powered by artificial intelligence and natural language processing (NLP).
In this piece, let’s discuss how sentiment analysis can help enhance banking and financial services. Let’s dive in.
What is Sentiment Analysis?
Natural language processing is the foundation of sentiment analysis (NLP). In recent years, as machine learning and computation have advanced, NLP has become an even more scalable branch of artificial intelligence. As a multidisciplinary field, it encompasses areas such as computational linguistics and computational neuroscience.
From chatbots to virtual assistants like Amazon Alexa, Google Assistant, and Siri, natural language processing (NLP) apps are omnipresent.
A natural language processing technique known as sentiment analysis can be used to determine the sentiment of data. Customers’ reviews, social media posts, help desk tickets, and other sorts of text are often subjected to sentiment analysis by businesses to better understand their demands and the reputation of their company.
It is possible to determine the polarity of information (positive vs. negative), feelings (angry, happy, sad, etc.), and intentions using sentiment analysis (e.g., interested and not interested).
Brand reputation management relies heavily on sentiment analysis. Customers’ attitudes, issues, and requirements can all be gleaned using this method. It provides for more accurate forecasts and better strategic judgments by allowing for data to be organized according to distinct moods.
Application of Sentiment Analysis Banking and Finance Industry
Here are some of the major applications of sentiment analysis tools in the banking industry.
Analyze customer interactions
Industry professionals frequently emphasize the necessity of appropriately gauging client opinions. Artificial Intelligence (AI) could have a significant impact on this industry because of the massive volumes of associated client data.
We’re sorry to hear you haven’t received a response yet, Adrie! Can you send us a DM with your email address so we can ensure you receive a prompt answer?
— ClassPass (@classpass) May 7, 2022
It is possible to employ a sentiment analysis tool to evaluate client feedback, polls, or social media comments about a financial institution. There are a lot of things that the software can accomplish with the enormous numbers, such as determining the general sentiment and picking out messages where the customer’s purpose has been determined to be good.
For banks to improve client acquisition and customer service, they need to comprehend how social media users discuss financial institutions such as banks. As an example, if a competitor’s marketing campaign was generating a lot of positive feedback, the bank may consider copying that strategy in their own marketing efforts.
Analyze market sentiment before investing in equity
Natural language processing and machine learning are now beginning to automate a few of the duties connected with trading and investing. Performing sentiment analysis is also a big part of it.
These artificial intelligence (AI) programs are fed research data gleaned from a wide range of sources, including news articles, social media posts, press releases, and more.
For example, banks’ equities investing sections can benefit from the use of sentiment analysis in their research efforts. In order to investigate a greater number of businesses, researchers might utilize the algorithm to assemble and prioritize the most relevant equity information from a variety of data sources.
Monitor the credit market effectively
To keep tabs on the public’s perception of credit, banks could use sentiment analysis. There is a lot of information regarding the performance of credit securities in press releases and other articles about the credit markets.
If major Dogecoin holders sell most of their coins, it will get my full support. Too much concentration is the only real issue imo.
— Elon Musk (@elonmusk) February 14, 2021
NLP tools and software such as BytesView can analyze this data from a sentimental standpoint. You can monitor sentiment data connected to certain bonds or construct lists of the corporations that are talked about most positively and adversely. Using artificial intelligence, it is possible to identify correlations between media coverage and the market performance of credit securities. Using this AI-enhanced analysis can drastically cut the time and costs required for in-depth financial research.
Another area where NLP-driven sentiment analysis might have a positive impact is compliance monitoring. Numerous records of compliance requirements are kept by banks and other financial institutions’ compliance departments, which, like trade data, should be updated on a regular basis to reflect new mandates. To avoid losing their licenses and reputation, banks may take the risk of doing so.
In most large banks and financial organizations, this information is regularly updated by in-house IT staff members. In order to search for regulatory website updates, they mostly use rule-based software that is now more than a decade old. To improve the process, AI-powered sentiment analysis software systems will swallow a lot of this information and categorize each update by relevance.
Improving banking products and services
Financial institutions are using social media monitoring to acquire a better knowledge of how their clients respond to their products and services. For example, you can use social media comments to fine-tune your rewards program. It will help you gain a better understanding of your customer’s perceptions of the bank on social media.
@AmericanExpress honestly the worst customer service I have ever witnessed – has anyone else tried to us an flight credit through there American Express travel service ?
— Neil Ormandy (@NeilOrmandy) May 8, 2022
Unstructured data is being generated at an exponential rate by today’s modern, tech-savvy customers. It’s tough, time-consuming, and exorbitant to sift through all these emails, texts, support tickets, and other correspondence. Banks can lessen the computational cost of evaluating large volumes of text by utilizing sopinion mining tools and gaining insights that can help them understand the demands of their customers.
How to perform sentiment analysis?
Well, you need an accurate sentiment analysis tool to get started. You have two choices here and they are as follows.
- Develop a sentiment analysis solution
- Subscribe to a SaaS-based tool
If you have the time, manpower, and money to build and train a sentiment analysis tool, you definitely should. But not every organization has the resources to build a custom tool that is accurate and precise from the ground up. Such organizations and businesses can get started with SaaS-based sentiment analysis tools.
Worried about the accuracy of the data analysis? These modern analysis tools provide you the option to develop a custom analysis model that you can train using data related to your industry or organization to increase accuracy. One such tool that you can use is the BytesView sentiment analysis API.
BytesView is a comprehensive text analysis tool that offers several unstructured text data analysis models such as sentiment analysis, entity extraction, topic labeling, and keyword extraction, among others. There are pre-trained models that you can get started with immediately. You can also use the BytesView API, just integrate it with your systems and train it with tagged data. The more data you train it with the more accurate it gets.
Key features of BytesView
- Dedicated plugins to integrate data
- API access to build custom solutions
- Various text analysis models for data analysis
- Build and train custom data analysis models
- Compile and analyze large volumes of unstructured text data
- Transform unstructured text into business intelligence
Additionally, BytesView can analyze text data in many languages and dialects.
You may gain a deeper understanding of your customers by using sentiment analysis. You can learn about your clients’ worries and utilize that information to create products and services that meet their demands.
Start using BytesView and look for game-changing insights that can help you expand your brand’s boundaries. Click here for a demo.
What is sentiment analysis?
As a form of NLP, sentiment analysis (also known as opinion mining) determines the emotional tone of a piece of writing. It’s a common method used by corporations to gather and categorize feedback on a product or service.
What is sentiment analysis used for?
It is possible to detect whether a given text has positive, negative, or neutral emotions by analyzing its sentiments. As a text analytics technique, it makes use of machine learning and natural language processing (NLP).
How does sentiment analysis work?
Many people use the phrase “sentiment analysis,” yet it’s widely misunderstood. Emotional tone analysis is basically a way of figuring out what people are feeling behind a string of words in order to better comprehend their online mentions.
Which algorithm is best for sentiment analysis?
The most recent, efficient, and extensively utilized approach to sentiment analysis is a hybrid approach for analyzing sentiments.
How does NLP work in sentiment analysis?
An analytical technique that employs statistics, natural language processing, and machine learning to decipher the emotional content of communications is called sentiment analysis. Messages from customers, interactions with contact centers, online reviews, social media posts, and other types of content can all be analyzed using sentiment analysis.