As the technological transformation of the financial services sector continues, banks are taking advantage of all the ways that AI and machine learning can improve their operations. Over the recent years, one specific sub-sector of artificial intelligence has gained momentum compared to others–natural language processing (NLP), particularly the use of NLP in customer service.
Nearly half of all businesses are already using NLP to gain meaning from text and enhance human-machine interactions, with that percentage only expected to grow over the coming decade.
With increasingly available data, both structured and unstructured, the use of NLP in in customer service for banks and financial institutions will continue to grow and evolve, offering more accurate analysis of customer text data for real-time insights and action.
What is Natural Language Processing
NLP is a subset of AI that uses machine learning, deep learning and other techniques to extract insights from speech, text documents and unstructured data and to structure or quantify unstructured data.
One of the fastest-growing sub-sectors of AI in recent years, NLP is being used for machine translation, to power voice assistants, question-answering systems and chatbots as well as for text summarization and sentiment analysis.
Across the finance world, NLP extends AI to tackle tasks beyond those that involve numbers – quantitative or structured data and mathematical modelling.
For example, NLP in banks can automate business processes in customer support departments, improve compliance processes, and even help perform credit risk assessments.
Business teams across departments can use NLP to classify documents, extract entities, and themes, assess relevance and score sentiment more quickly and efficiently than human efforts alone.
Customer Service NLP for Banks
Natural Language Processing is giving firms an edge over the competition by helping businesses enhance their overall customer experience. Before AI-based customer service solutions became available, bank customers with problems or concerns would have to call customer service agents, stay on hold, and make their case to the available representative.
With chatbots, personalized recommendations, and automated responses, the customer experience has vastly improved and customers can now use NLP-powered support solutions to get the help they need as quickly and accurately as possible.
Speech recognition and sentiment analysis capabilities can promptly detect a customer’s inquiry or issue, determine whether to ask further questions, and provide a relevant and personalized solution.
5 Use Cases of NLP in Customer Service
Between shorter wait times and more personalized solutions, NLP in banks can elevate the customer experience while also allowing staff to focus on other tasks that aren’t as routine or repetitive.
While early versions of AI and rigid rule-based algorithms could not adequately handle the nuances of customer service tasks, NLP facilitates these human-machine interactions with more ease.
1. Analyzing Consumer Data
At its core, understanding and serving customer needs are the foundation of any business. To remain competitive, firms need to thoroughly research customer needs and evaluate customer feedback.
This process requires the data analysis of mountains of consumer data – structured and unstructured data – from both proprietary and third-party sources to assess behavioural patterns, past interactions, and historical documents. The insights revealed enable financial institutions to provide personalized and more precise solutions.
With more data on consumers than ever before, bank customer service departments can gain a clear and comprehensive picture of their clients. Still, it would be impossible for the in-house workforce to manually perform this type of analysis for every customer they interact with each day. NLP makes it feasible to assess both structured and unstructured consumer data quickly and efficiently.
Natural language processing helps banks analyze and track data they don’t have the time or resources to do manually in order to predict future needs, quickly provide relevant resources and solutions, and in the end better customer service support.
When a customer interacts with chatbots or voice assistants to express a particular issue or concern about their service, NLP can break down their history with the bank, any past inquiries or problems, and analyze relevant documents to better understand the client from the start of the interaction.
2. Interpreting Consumer Sentiment with Text Summarization NLP
Human emotion is likely one of the most complex and nuanced subjects, but today’s NLP models can examine text or speech to determine intent or sentiment accurately through sentiment analysis, a subset of NLP. With this technology, the bank customer service agent can quickly and accurately determine the emotion and intent of customers from their voice, chat or support ticket.
So whether the interaction appears to be positive or negative, and the degree of urgency expressed, NLP can help customer support teams and product managers analyze the data, serve their customer needs better and offer better solutions. In other words, NLP can detect the initial problem customers are facing, prompt relevant dialogue that leads to a solution, and determine if customers are satisfied with the end result.
And while there may be some hesitancy around human-machine customer communications in customer service, consumers are more likely to leave these interactions positively if they feel that their issues were correctly and swiftly addressed by chatbots or voice assistants.
3. Risk Assessment
For banks, assessing client risk is essential to their operations and continued success. Historically, risk management has been a tedious and time-consuming task requiring a lot of manual labour. For example, identifying a consumer’s creditworthiness often takes days of research and analysis.
Now banks can automate and streamline risk management processes with AI so that staff can focus on other value-add tasks and better protect the firm and its customers from risky clients or transactions. More specifically, NLP can efficiently analyze all types of text data and information from various sources to get a clear picture of a client’s risk profile and predict potential outcomes from this information.
AI can consider more than just traditional quantitative financial data by extracting insights from a text – historical documents, social media posts, loan applications, employment history, and more – to provide meaningful and actionable information about how risky a current or potential customer may be. Plus, when customers interact with intelligent chatbots or voice assistants, they’ll only receive relevant recommendations based on their risk profile since that’s readily available to bank customer service teams using NLP.
By making relevant and accurate recommendations based on a client’s creditworthiness and risk profile, the bank is better positioned to service qualified customers and meet compliance requirements. This enhancement promotes greater satisfaction among actual clients and puts both the customer and the bank at less risk.
The implications of NLP integration into the bank customer service department are monumental and have the potential to revolutionize an area that historically shied away from technological advancements.
The use of NLP in customer service in banks enhances human-machine interactions at every touchpoint and can help banks become more efficient, accurate, and effective in providing superior customer experiences.
4. Personalized Services
Across the financial services industry, firms are seeing increased demand among clients for more personalized services tailored to their specific needs. While historically, a one-size-fits-all or tiered offering approach was the standard in the industry, this isn’t the case anymore.
To improve overall customer satisfaction, financial services firms are taking advantage of the abundance of customer data available and applying that to their own service offerings and customer analytics.
Today, with more customer data available than ever before, financial institutions can access information about clients based on their online activity. By deploying NLP in customer service, banks can take advantage of customer data and extract meaningful insights from it to target customers with personalized and custom offerings. While this would have been impossible before, firms no longer need to rely on staff manually combing through a customer’s online interactions to gain insights into what they may need. Instead, firms can use NLP and customer analytics to analyze large amounts of text and unstructured data in a short amount of time.
This keeps firms up-to-date with their clients and ensures that they’re being offered personalized services that are relevant to them.
5. Customer Feedback Analysis
Financial services firms can improve their service offerings by responding to customer feedback in a timely manner, though manually combing through reviews and visiting comparison websites isn’t operationally feasible.
Keeping customer satisfaction rates high and responding to valid critiques are key to staying relevant in the field today, though it can be a tedious and time-consuming task when done by hand.
With NLP, firms can more easily analyze customer feedback and sentiment about their business, all without having to read or filter through each individual review. NLP allows the entire process to be optimized.
Since natural language processing can extract insight from text data, this makes it the perfect tool for firms to keep track of customer feedback and determine whether it’s positive or negative and to what degree. This allows organizations to monitor reviews in real time and flag the most important or time-sensitive comments, provide timely feedback, and ignore filler or irrelevant reviews.
Improving Customer Service and Experiences
Overall, NLP can help financial services firms improve the customer experience and how they gather customer analytics. NLP enhances human-machine interactions and can be applied to improve the customer journey for all.
Powering chatbots and allowing for quicker response times, NLP can easily conduct sentiment analysis to determine what type of assistance the customer needs. With NLP, when a customer contacts the institution with an issue or concern, their problems can be addressed in real-time and resolved with a personalized solution. In the past, they would have to call and wait for an available representative who would then scan through their history with the firm and gather relevant data about their account before they could work on a resolution.
While customer service departments haven’t traditionally been early adopters of technology, more financial institutions are beginning to see the power of NLP and customer analytics. Through NLP, they can provide faster and more personalized customer service, aggregate customer feedback, and free up employees for more impactful tasks.
As AI continues to revolutionize the financial services industry, firms will begin to see how powerful NLP can be within their organizations.
With a wide range of possible applications, NLP can help firms gain better customer analytics, enhance market and competitor research, perform customer review analysis quickly and efficiently, and make better business decisions.
Looking ahead, the use cases of NLP within the financial services industry will continue to expand as the technology evolves and becomes more sophisticated.
How No-Code NLP Empowers Better and Faster Customer Analytics
The financial services space will continue to be impacted by the rapid pace of technological advancements, and firms will find new and effective ways to implement AI into their operations. For any financial service firm, implementing AI into their business may seem like a daunting and expensive task.
No-Code AI and products like the Accern NoCodeNLP Platform make it easy for both data teams and non-technical business users to more quickly and cost-effectively take advantage of these innovations. They can automate more business processes, implement more efficient workflows and improve the overall customer experience by routinely delivering relevant and timely responses without sacrificing human, high-touch interactions when needed.