Voice user interfaces are filled to the brim with human language. Human languages are beautiful; they are geographically diverse and filled to the brim with uncertainties. Some languages have words that derive their semantic meaning from their context; for others, meaning derives from the tone in which the speaker says it or the syntactical placement of the word in the sentence. In linguistics, there are five main categories to human language that help linguists understand how each language harmonizes into a unified whole: syntax, semantics, pragmatics, morphology and phonology. Because of how ambiguous human languages are and how uniquely innate they are, it may be evident that they would not translate smoothly into a computer. While partly true, it is an inevitably complex subject. Computational linguistics is the academic study of computing natural language. On the theoretical and artificially intelligent level, this is referred to as machine language learning. All of the above is used in voice user interfaces.

Machine language learning is a component of deep learning. Deep learning is a core part of artificial intelligence and is critical to an artificially intelligent system’s performance – whether text-based, voice-based, or neither. Machine language learning possesses many layers, with its main one being natural language processing – also commonly referred to as NLP. Like deep learning at the core of artificial intelligence, natural language processing is at the core of machine language learning. It is an essential component of the fundamentals of any computational linguistic algorithm. Natural language processing often goes together with natural language understanding (NLU), and together, they make up the most successful chatbots and other text and voice-based artificial intelligence systems.

The use of computation to store text-based data is not a new concept. Any simple computer programming language will have a text-based database. It thus makes sense to want to apply language analysis, language translation, conversational design, and language databasing to computation. From a computational perspective, computational linguists can draw from their interdisciplinary background and apply it to artificial intelligence in the form of conversational artificial intelligence within chatbots, virtual assistants, and even virtual reality.

Voice User Interfaces

Voice user interfaces such as those seen from Google (Google Home) or those as technologically iconic as Amazon’s Alexa have become commonplace within many people’s day-to-day lives. For example, FitBit Sense now allows one to talk to Amazon’s Alexa right from the watch itself with the press of a button. As visual interfaces begin to intertwine themselves with voice user interfaces, it is critical that we not only understand why they work but how this will impact our lives as humans who already use language to connect on many levels with other people. How this phenomenon will translate into chatbots is an entirely different narrative.

person reaching out to a robot
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As mentioned previously, machine language learning is at the heart of computational linguistics. Within artificial intelligence, cognitive scientists aim to create artificial intelligence that is ‘strong’ – ‘strong’ in that it does not rely on a user for its input. ‘Strong’ in that it can use past inputs and outputs to create new algorithms. Essentially, computational linguists and cognitive scientists want artificially intelligent systems and voice user interfaces that can learn and speak without the help of a human. They want a piece of artificial intelligence that can evolve with time, independent of any individual behind the scenes working on simulating the learning experience of the chatbot’s experiences with the user. We want the artificial intelligence system to be phenomenally conscious, not just simulate it.

Strong artificial intelligence systems such as those mentioned previously are indeed the future. With self-driving cars on the way and mobile GPS apps with voice user interfaces that are almost entirely voice-based, the push for complex voice user interfaces and conversational artificial intelligence will continue to grow. With the success of social media and social communication apps, it only makes sense that cognitive scientists and computational linguists would want to have chatbots and voice user interfaces that mirror human-to-human communication and interaction. Humans are social creatures, and we crave social interaction. Whether or not an artificially intelligent robot can satisfy that craving is up to our ability to make conversational AI and voice user interface systems naturalized.

READ MORE: Machine Learning 101: The Revolutionary Side of Artificial Intelligence

Unfortunately, an area with such high potential results in high expectations. Much like the expectation of strong artificial intelligence to understand us and evolve and learn from their experiences with the user, this would ultimately translate into a piece of conversational artificial intelligence or voice user interface that is more human-like than its predecessor. The study of cognitive science is, after all, to naturalize and formalize the human mind into a machine.

Why Personalization Requires Strong Conversational Artificial Intelligence

With the expectations of users and those creating the robot to be more human-like and therefore more personalized, it becomes apparent that conversational artificial intelligence branches out into a much more complex area of technology and technological development. To make voice user interfaces that are as engaging and realistic as a human at the other end of a telephone would be, we would need it to function as a human. It would need to be personalized to each and every user, not your stereotypical cookie-cutter chatbot. A significant problem with the rise of conversational AI is its lack of personalization and user-friendliness. At this point in time, it is still too robotic, and therefore, unfriendly.

Presently, chatbots and other voice user interfaces are believed by the average user to not handle complex inquiries and tasks. Synthesizing language and natural language processing to allow artificial intelligence systems to process and understand natural language is one thing. Providing personalized implementations of conversational AI to the user that tailors to them and their experiences in a human-like manner is an entirely different story – one that must technologically evolve as the digital era continues.

While hard to imagine, this is crucial to keep in mind whilst thinking about the future of artificial intelligence, chatbots, virtual assistants, mobile apps, and other viable pieces of technology and their future in marketing. Going beyond artificial intelligence that manually simulates learning from a human and actually learns from the user, their prompts and experiences are the next steps in creating artificial intelligence eco-systems that would have consumers be open to the idea that chatbots can, indeed, handle complex tasks in an empathetic manner. Communication automation is the goal, yes, but we must consider that having communication automation be personalized to the individual is the ultimate goal regarding a voice user interface’s strength as an artificially intelligent system.

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This is not to say that we have not already reached artificial intelligence that is strong and, thus, formalized and personalized. We are far from completing such a task with all the natural human languages that exist – especially for voice user interfaces. Unfortunately for machines, human language is not as innate to machines as they are to humans.

The Applications of Conversational AI

Fortunately, natural language processing and conversational artificial intelligence as a scientific discipline of computer science have uses that go beyond user experiences on mobile apps or other hyper-personalized experiences with technology. Because machine intelligence weaves itself into computational linguistics and all its subfields, it is important to note its uses beyond futuristic speech-based interaction with humans. 

Statistical natural language processing is a massive component of this aspect of conversational artificial intelligence. It involves complex computer algorithms combined with machine language learning, deep learning, and neural networks to analyze and categorize voice and/or text data. There are many aspects of technology used in – presumably, types of technology you are familiar with and have encountered. It is an important aspect of voice user interfaces, specifically.

For example, many websites want to check that you are not a robot. What is so ironic about this is that it is a robot making sure you are not a robot. They do this by asking you questions that are typically beyond most standardly automated robot capabilities. To put it more simply, they ask you questions that are more ‘human’ – questions that often require critical thinking, common sense, and a lack of repetition, all of which are core components to the ‘humanity’ of humans. Said subjects are also very complex and have yet to be fully formalized within pieces of artificial intelligence machinery.

Therefore, it is easy to see why conversational artificial intelligence combines with natural language processing and understanding to form a subcategory of artificial intelligence that does not revolve around a user. Instead, it relies upon its own unique machine language learning algorithms to help with spam detection (especially within the financial sector), thorough machine translation, social media rule-analysis and even summarizations of voices or texts.

Artificial Intelligence (or AI) is already being widely deployed across industries, sometimes in ways you wouldn’t even have imagined. The financial sector, which is heavily regulated, may not seem like an industry that would be poised to harness cutting-edge technology. But with the right planning, regulatory experts, and smart selection of use cases, AI can be put to work for the good of the employees and customers. 

Where AI in Banking and Finance Gets Put to Work

AI in banking and finance can also lead to huge cost savings, within different business units or channels. There are three main channels where banks can use artificial intelligence. These include the front office (for tasks such as conversational banking), the middle office (for fraud detection and risk management), and the back office (for processes like underwriting). It is estimated that in the next 10 to 15 years, AI-powered applications will create $1 trillion in savings for the industry.

  1. Conversational Banking

The front office is client-facing, and incorporating AI generally means including messaging applications as part of the client experience. Having chatbots within the suite of customer service tools allows for clients to receive answers faster, while interacting with a human-like interface. Conversational banking will continue to improve as more institutions harness AI to elevate their customer and employee experiences. AI in banking and finance can:

  • Support customers by answering frequently asked questions quickly
  • Increase employee satisfaction by eliminating repetitive tasks
  • Boost sales by allowing AI to better serve and sell to clients
  • Help to improve marketing efforts and services by collecting data
  1. Fraud Detection and Risk Management

Within the middle office, AI is perfectly placed to identify any out-of-the-ordinary behaviour within financial accounts. Machine learning and AI are based on data analysis and pattern recognition, with each getting better and better the longer they are utilized. Unfortunately, fraud is an increasingly costly problem. But, AI used cleverly in risk management can perform data analysis in milliseconds, which makes it the most efficient way to detect fraud quickly. AI can:

  • Increase efficiency in identifying fraud and freezing suspect accounts
  • Improve customer service by providing account access and unlocking options in automated and easy-to-access applications
  • Reduce intensive human labour in fraud detection, allowing for employees to focus on developing the software and systems that improve security, rather than searching through transactions to identify fraudulent actions
  1. Underwriting

Underwriting is when your income, assets, and debt are verified as you attempt to apply for a loan. In the back office, AI can be put to good use for underwriting tasks that include data entry, data transfer, automated financial plan creation, and collection of loan application statuses. So much of the processes today are various data collection systems and oversight processes. In a bid to save time and allow the software to do some heavy lifting, AI can:

  • Process underwriting submissions
  • Make risk decisions based on past data
  • Give coverage recommendations 

Check Out Some of These Progressive Financial AI Chatbot Examples

Some of the largest banks in the United States and Canada have embraced AI in the form of a chatbot that interacts with clients. These examples of conversational banking have received rave reviews and are used by millions of users. Conversational AI is at the heart of each of these chatbots, used to ensure that the customer experience service that feels human-like, supportive and customized.

  1. Bank of America Chatbot: Erica

In 2016, Bank of America had a vision for a digital assistant that would help clients online, where they wanted to seek service. Not long after, Erica was born. This chatbot is considered the first widely available virtual assistant in finance. 

Erica can send notifications to customers, give money-saving tips, provide credit reports, and dole out balance information. When you call into your bank, these are all options you have, either in an automated response or when speaking to a representative. But 43% of online bank users would rather use a chatbot than speak to a representative. Erica provides answers much faster than a human ever could.

To say Erica was a hit is an understatement – within two months she already had 1 million users. As with most conversational AI chatbots, the more Erica interacts with customers, the smarter she gets. The initial investment that Bank of America made in the chatbot is paying off now, without needing to reprogram or upgrade the system. She is able to learn what customers need and how best to converse in order to provide the most up-to-date help.

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  1. RBC NOMI Digital Banking Assistant

RBC’s NOMI has received a lot of good press, and for good reason. The AI-based NOMI Digital Banking Assistant is four things in one:

  • Offers real-time personalized financial data to customers
  • Automatically finds and saves money by analyzing cash flow
  • Budgeting tool that is driven by insights derived from data
  • A chatbot that can respond to both text and voice inquiries

NOMI is built on the Personetics Engage AI platform, and RBC customers can simply click on a button to enable its use. There’s no setup required; it gets to work automatically providing insights, suggestions and budgeting help based on the customer’s own personal trends, data and preferences. 

The magic of this AI-powered solution is that it’s completely tailored to the customer from day one. When clients are reviewing their banking, they get insights that help them be more intentional with their money, such as “You spent $56 on coffee last week.” The find-and-save feature is also very popular where the AI technology can determine how much money could be automatically saved each month without hurting cash flow. 

More than 1.1 million users have enabled NOMI on their accounts, and less than 1% of customers turn the AI-powered platform off. As a result, users have received more than 1 billion insights, helping them spend better, save more, and learn about their finances. Talk about next-level customer service.

  1. Capital One Chatbot: Eno

Capital One’s chatbot, Eno, is used to help customers access account information, see transaction history and pay bills. Eno was the first natural language SMS chatbot from a U.S. bank. This means that Eno understands a variety of text lingo and short forms (helped by the pilot project that saw 100,000 users try out the platform). 

Eno doesn’t just answer questions for customers though, he also detects spending behaviour that is out of the ordinary. Eno can flag possible fraudulent activity to keep your accounts safe, but it can also provide insights on activity that seems out of the ordinary, such as a higher than usual tip or a monthly bill that seems to be more than normal. 

Eno sends messages to clients just like a human would and it analyzes the responses using its AI-powered chatbot. By understanding natural language and detecting human intent, it can converse with clients and help them solve problems in a way that feels customized and friendly. Clients love that they can check their balance or pay a bill simply by sending a text.

What Will the Future Bring for AI in Banking and Finance?

To take things one step further, and looking beyond the solutions that already exist in finance, there are additional options that AI and chatbots can harness for even more powerful customer experiences. The following is just a taste of what added technologies can bring to banking and finance:

  • Virtual reality+chatbots = illustrating the impact of long-term savings
  • Real-time status+chatbots = an update on a cross-border transaction
  • Facial recognition+chatbots = zero-click transactions
  • IoT devices+chatbots = voice conversations with customers in many locations

If you are wondering how you can incorporate a financial chatbot, reach out to a Chatbot Consultant. Our experts understand the industry and chatbot compliance requirements.