3 Ways NLP Helps Banks Improve Customer Service
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 recent years, one specific sub-sector of artificial intelligence has caught momentum compared to others–natural language processing (NLP).
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 banks will continue to grow and evolve, offering more accurate analysis of text data for real-time insights.
Understanding NLP
NLP is at the crossover of AI and machine learning, which extracts insights from text and unstructured data while automating and facilitating non-quantitative. NLP uses speech-recognition methods to break speech or text into individual elements, like sentences or words, which are then compared against a previously analyzed address to provide context, classify, and dissect the meaning or intent behind the words.
As the fastest-growing sub-sector of AI in recent years, NLP is quickly becoming one of the most beneficial technological tools for all industries—and especially finance.
Across the finance world, NLP allows AI to tackle tasks beyond those that involve concrete numbers and mathematical modeling. For example, NLP in banks can enhance customer support departments, improve compliance processes, and even help perform credit risk assessments. Financial teams of all kinds can use NLP to classify documents, entities, and themes and analyze the sentiment and relevance of text more quickly and efficiently than through human efforts alone.
Customer Service NLP for Banks
NLP is giving firms an upper edge over the competition by helping businesses enhance the overall consumer experience. Through NLP chatbots, personalized recommendations, and quick responses, customer service has become vastly improved.
Before customer service AI’s became available, bank customers with problems or concerns would have to call the customer service line, stay on hold, and make their case to the available representative. In contrast, customers can now use NLP-powered support solutions—like chatbots and voice assistants—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.
3 Ways NLP Helps Banks Improve Customer Services
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 specific AI segments and rigid rule-based algorithms could not adequately handle the nuance of customer service tasks, NLP effectively facilities these human-machine interactions with ease.
1. Analyzing Consumer Data
At its core, understanding and serving customers' needs is the foundation of any business. To remain competitive, firms need to thoroughly research customer needs and evaluate customer feedback. This process requires the analysis of mountains of consumer data from both proprietary and third-party sources to assess behavior patterns, past interactions, and historical documents. The insights revealed enable financial institutions to provide personalized and 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 their in-house workforce to perform this type of analysis for every customer they interact with each day. Thus, NLP makes it feasible to assess both structured and unstructured consumer data quickly and efficiently.
NLP helps banks analyze and track data they don't have the time or resources to do themselves, which can help predict future needs, quickly provide relevant resources and solutions, and improve the customer service experience.
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 firm, any past inquiries or problems, and analyze relevant documents to better understand the client from the onset of the interaction. Not only can this lead to more personalized solutions, but it also brings about shorter wait times and reduced strain on human employees.
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 sub-set of NLP.
With this technology, bank customer service departments can quickly and accurately determine the emotion and intent of customers from their voice or messages. So whether their concern appears to be positive or negative, and to what degree of urgency, NLP can help finance teams analyze the data and create more custom solutions. This allows banks to serve their customers' needs better and provide more accurate solutions.
In other words, NLP and sentiment analysis 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 communication in customer service, consumers are more likely to leave these interactions positively if they feel that their issues were correctly addressed by chatbots or voice assistants.
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 because of the extensive manual labor. For example, identifying a consumer's creditworthiness would have taken days of manual research. 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 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 financial data by extracting insights from 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 departments 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 all 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 bank customer service departments are monumental and have the potential to revolutionize an area that historically shied away from technological advancements. The use of NLP in banks enhances human-machine interactions at every touchpoint and can help banks become more efficient, accurate, and effective with their customer service systems.
How No-Code AI Can Simplify NLP for Banks
It is no secret that AI will continue to transform the financial services sector, especially related to banks and their customer support systems. While AI and machine learning are complex technologies, a no-code AI solution, like Accern, allows all banks and financial professionals to integrate NLP into their operations to reap the benefits of greater efficiency and an improved customer experience at lower costs.
Banks don't need to hire software engineers or data scientists to code NLP models; instead, a no-code solution and ready-made use cases can help banks quickly integrate NLP into their business. To see how your bank can implement NLP techniques for customer service, request a free demo with Accern today.
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