Artificial Intelligence (AI) and Machine Learning (ML) have made a notable impact on many industries in the last decade. Leveraging advances in computing power has enabled industries such as retail, e-commerce, education and healthcare to improve effectiveness and efficiency in reducing customer churn, improving learning and development and bettering the understanding of customers’ needs. Despite this, organisations within the financial services sector have been relatively slow to adopt machine learning techniques into their businesses


According to the CFA in 2019, 75% of investors were not using any machine learning techniques in their investment process. This will undoubtedly change in the next decade. Speech analytics, powered by Natural Language Processing (NLP), a type of machine learning, will streamline process efficiency by replacing outdated Quality Assurance (QA) methods and better decision makers’ effectiveness by providing alternative sources of information. These benefits and many others will be appreciated by investors, consumer facing agents and customers themselves. 


So, what are these benefits and where are they likely to be seen? Here are 10 ways in which NLP will change financial services

  1. Market sentiment 
  2. Transcription of corporate calls
  3. Portfolio selection
  4. Workforce management 
  5. Quality assurance
  6. Improved conversion rates 
  7. Sentiment analysis 
  8. Protecting vulnerable customers
  9. Supporting compliance processes
  10. Improving CX 




NLP has many potential applications in the investment process. Sentiment analysis, specifically market sentiment (the overall attitude of investors towards specific securities and markets),  could be a fundamental analysis tool for stock price forecasting in the future. Applying sentiment analysis in an omni-channel setting would enable investors to extract economically meaningful information from social media and news platforms, financial reports and many other sources. 


NLP softwares, like Aveni Detect, can accurately transcribe spoken words into text in real time. Manual transcription and evaluation of calls takes time and is particularly labour intensive. However, NLP powered transcription fast tracks the structuring of unstructured data, improving efficiencies and investor performance by providing a greater breadth of information to analyse. 


A misconception associated with financial data is that it is all numerical. In fact, according to IDC, 80% of worldwide data will be unstructured by 2025. As a result, NLP will be incredibly useful in structuring complex data in order to derive meaning and value. Instead of having to comb through thousands of pages of information about a potential investment, NLP solutions will present the most valuable information to investors so that they can make the most informed decision with the least time spent. 




NLP will allow managers to analyse their sales teams in an omni-channel setting to manage them as effectively as possible. Insights into best practices and customer preferences that have been previously unseen will enable management to transfer customers to the right agents at the right time. Moreover, NLP will help at the front end of workforce management in the recruiting process. NLP provides a data driven approach to CV screening, which saves time per candidate and allows for more informed hiring decisions. 


Real time transcription and analysis of calls will mean that outdated post-call listening processes for QA will become a thing of the past. QA teams will no longer have to trawl through calls listening to hundreds of hours of recordings to find snippets with complaints, expressions of dissatisfaction or insights into best sales practices. NLP will highlight the most important parts of the call that require a supervisor’s attention, boosting efficiencies. Whatsmore it will increase coverage for Quality Assurance allowing firms to monitor and assess up to 100% of calls.

Real time suggestive AI, driven by NLP, will help improve sales conversion rates. As agents converse with customers in real time, suggestions about topics to bring up, questions to ask and products to sell will pop up on the agent’s screen helping to improve sales conversion rates and customer experience. 


Similar to that mentioned above, sentiment analysis through video, phone and text between the agent and the customer will provide meaningful insights into best practices, suitable training programmes and customer experience. NLP will give supervisors previously unseen insights into the voice of the customer as it provides coverage across 100% of communication, giving learning and development programmes a data-driven foundation that address genuine weaknesses. 




NLP solutions, like Aveni Detect, can capture information about the customer and determine whether they are vulnerable through the language they use and the sentiment behind it. If a customer is considered vulnerable by the software it is flagged to the agent and they then know to proceed accordingly. Advances in NLP will help to protect vulnerable customers in the future. 


Much of the data being handled in the financial services sector is private and as a result compliance processes are a must. NLP solutions help to enforce a rigorous approach to compliance, limiting the chances of fraud and malicious attacks.  By labelling data from interactions (language, sentiment and other information), analysing it using bespoke fraud dictionaries, comparing it to previous interactions and evaluating the outcomes, potentially fraudulent activities can be flagged and investigated further, keeping customers’ data in the right hands. 


Of course, if sales and marketing see benefits from the deployment of NLP across the financial services sector, customers are likely to see them too. Improving the customers’ experience is a win-win for customers and agents, reducing churn, improving sales lead times and ensuring the fair and consistent treatment of customers. A great example of this is Amazon. They have used NLP to drive better customer engagement through their product Alexa. Voice assistants are being used to process orders for products, perform actions such as play music or simply start a phone conversation with a contact. The fundamentals of this technology is currently being implemented, but in the next few years we will see the AI software go even further and help assistants with more complex tasks. This adds true value to the customer journey as there is better customer support, as well as helps the customer to save time doing certain tasks, making their everyday lives more enjoyable. 


If implemented in the right way, NLP has the potential to lead to more personalised, better monitored customer experience, which will not just benefit the customer but the company too.  Better customer experiences lead to happier customers, greater loyalty and increased rates of referral which of course mean greater profitability.


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