Just as the internet caused a paradigm shift in the way that consumers access banking – 58% of consumers globally now prefer to bank online or via a mobile app – another fast emerging technology breakthrough promises a similarly profound shake up across the financial services market. Artificial intelligence (AI) is expected to add up to US$4.4 trillion in value to the global economy – per year. And of all the industries set to benefit, banking has one of the greatest opportunities. Through AI, banks are gaining the capabilities to digitalize and automate workflows, opening up exciting ways to improve both their own employee experience by making space for more fulfilling roles, and creating more meaningful and seamless customer experiences. This is a win-win for both businesses and consumers.
As the capabilities and applications of AI grow, banking leaders are waking up to its potential and beginning to explore how they can harness the technology to tackle one of their biggest challenges: delivering timely and personalized support to their customers. While banks widely perceive customer experience as a key differentiator and source of competitive advantage, over half say they are trying to offer a more bespoke service yet their efforts end up appearing generic.
One of the key branches of AI making a real difference in this area is natural language processing (NLP) and machine learning (ML). Thanks to this technology, banks gain the capability to extract information from all manner of different sources, including social media posts, web reviews, emails, and customer satisfaction surveys, to better understand their customers and wider market trends. Tapping into this vast data pool and applying AI and NLP empowers banks to listen to their customers, anticipate their evolving needs, and constantly raising expectations, and move quickly to address pain points. Banks that successfully transform unstructured data into clear information can use it to create successful personalized customer journeys, gain trust, and foster long-term loyalty.
The huge drive towards digitalization opens up exciting opportunities for banks to capture what their customers are saying, and use that information to deliver even better services.
Taherah Kuhl, Vice President of Business Services at Dassault Systèmes
Applications of NLP, ML, and semantic analysis technology
Here are three ways banks are applying NLP, ML, and semantic analysis technology to get more from their data:
1️⃣ Customer sentiment analysis
Today, consumers expect a personalized banking experience that is tailored to their specific needs and recognizes their current situation. Through NLP and ML solutions like Proxem Studio from Dassault Systèmes, financial institutions can deliver on this by analyzing all manner of data sources to understand customer sentiment in their native language (+30 languages can be analyzed with this tool). From here, they can offer personal advice and services, targeted product recommendations and promptly address queries and issues.
Before, it would have been impossible for banks to manually read and analyze the vast amount of customer feedback they receive, whether that’s from satisfaction surveys or reviews on social media. It’s meant that they’ve never really had a true understanding of customer sentiment at scale. AI changes that, providing a 360-degree view of all those sources in different languages, which they can use to identify trends and determine how well they’re meeting customer satisfaction key performance indicators.
François-Régis Chaumartin, Vice President of Semantic Data Science at NETVIBES, Dassault Systèmes
2️⃣ Automating tasks
AI is increasingly being used to automate routine tasks and free up customer service representatives and account managers so they can spend their time tackling more complex issues and responding faster to customer requests.
Account managers can spend up to half of their time replying to incoming queries, whether that may be product advice, information requests or complaints. In more than 70 % of these cases, the response is relatively straightforward – such as stating the balance on a bank account or giving information about a new product. AI can automatically understand incoming requests, extract key information and help to compose a suitable response, saving up to one hour per day.
François-Régis Chaumartin, Vice President of Semantic Data Science at NETVIBES, Dassault Systèmes
3️⃣ Anticipating and identifying unseen risks
Financial crime and fraud remain one of the biggest challenges for banks and financial institutions. According to one report, by 2027 fraud will cost the global industry US$40.62 billion. It’s important, then, that companies take steps to proactively detect and prevent fraud and remain compliant with the fast-evolving regulations landscape. Using AI, they can do this by tracking data threats, identifying potential security breaches and disruptions before they occur, and monitoring regulatory compliance needs.
One of the key regulations impacting the financial services sector more recently is around operational resilience – meaning the ability to absorb and adapt to shocks and disruptions in the market. As part of this, regulators expect banks to have a holistic view of their enterprise and understand all possible interdependencies across the value chain that might affect their services and products. If there’s a failure that might affect the market or customers suddenly face an issue accessing their account, it’s fundamental for banks to be aware of what’s happening to minimize disruption.
Taherah Kuhl, Vice President of Business Services at Dassault Systèmes
AI and intelligent customer analytics in action
AI and NLP tools aren’t new to the financial services industry. Banks have used them for many years to get more from their data. However, Proxem Studio offers an entirely new proposition by providing users with a completely personalized and responsive semantic analyzer tool that includes:
- Integrated deep learning and machine learning features
- The ability to quickly set up and virtually model banking and insurance operational systems
- The option to deep dive into any given subject within context, making accurate connections and relationships between concepts, market news etc.
Harnessing these combined capabilities within a single tool helps banks reveal surprising results and insights, and come up with solutions to issues they might not otherwise detect:
Ramping up customer confidentiality measures: Through Proxem Studio’s NLP insights, one bank detected an issue unique to its branches in Paris. Here, the population is high and branches tend to be small, so it was easy for customers to overhear confidential information when queuing. Being able to understand exactly what was happening allowed the bank to put in place more stringent client confidentiality measures.
Catering to local preferences: Belgium has three official languages: French, Dutch, and German. Proxem Studio revealed that high net-worth individuals in Brussels, which expect a high quality of service, wanted to be able to speak to advisors in their native language.
Over the last decade, we’ve been capturing the voice of customers to help companies automatically analyze tens of millions of customer feedbacks and extract the information they need to improve their offering and gain a competitive advantage. For over a year now, we have been investing heavily in LLMs (large language models), the foundation of generative AI. These models enable us to go even further in terms of in-depth natural language understanding, and intelligent text generation: it’s never been so easy to dialogue with the machine!
François-Régis Chaumartin, Vice President of Semantic Data Science at NETVIBES, Dassault Systèmes
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Taherah KUHL, Vice President Business Services Industry, Dassault Systèmes
Taherah has worked at Dassault Systèmes for the past 7 years. Focused on the Financial Services & Logistics industries globally, Taherah is responsible for driving the industry strategy and vision. LinkedIn profile
François-Régis CHAUMARTIN, Vice President of Semantic Data Science at NETVIBES, Dassault Systèmes
François-Régis is the founder of Proxem, a start-up specialized in semantic analysis, acquired by Dassault Systèmes in 2020. He is the author of Le Traitement Automatique des Langues (published by Dunod). LinkedIn profile
Global Industry Marketing Leader, Business Services at Dassault Systèmes