Tag: IA Conversacional

AI and the transformation of the CX in Banking (I)
Finance is a fundamental part of our daily lives, and of course, banking services are essential in modern society. In recent years, technology has transformed the banking industry and the way we relate to it. Artificial Intelligence (AI) and the transformation of the Customer Experience (CX) in Banking are closely related.

Evolution of the Banking Sector

There are many changes that we have witnessed in the banking sector in recent years, among others, we can list by way of example:
- Digital Banking, nowadays all banks have digital platforms that allow us, in self-service mode, to carry out all kinds of operations and transacctions: account opening, transfers, currency exchange, investment in stock markets…
- Conversational Banking which, using “digital banking” platforms, has enabled banking and customers to interact in real time through text messaging, voice, mobile applications, messaging apps and websites to offer new channels of contact and unprecedented levels of service; let’s not forget that the goal of the “digital banking” is to “empower customers and banks to interact in real time through text messaging, voice, mobile applications, messaging apps and websites to offer new channels of contact and unprecedented levels of service. Let’s not forget that the objective of “Conversational Banking” is to try to reproduce the experience of the “offline” customer relationship and provide a complete and quality online experience.
- The Open Banking which, following regulation by the European Parliament in 2015 (Payment Services Directive PSD2, Directive (EU) 2015/2366), has brought about a decisive change by promoting the development and use of innovative solutions that are integrated into the ecosystem of banking solutions thanks to the use of APIs.
- The emergence of Neobanks and Fintechs, whose core business is fully aligned with digital and innovation, have opened the doors to a multitude of competitive and innovative services in the sector.
Artificial Intelligence has had a special impact on the Transformation of the Customer Experience in Banking. Virtual assistants are here to stay and facilitate multiple operations for customers.

Application of AI in Banking

The use of AI in banks is not new; investment banks, for example, have used AI and Machine Learning long before other sectors to predict behavioral patterns. Over the last decade, banking has embraced AI in a variety of ways to improve operational efficiency, customer experience and decision making.

Banks are currently applying AI to create portfolios, choose assets, perform analysis of large financial data sets, fraud detection, make predictions of economic trends and investment risks.

The opportunities for AI applications go beyond basic banking activities. Implementing AI in customer interactions, in line with strategies such as “conversational banking,” among other things, makes it possible to manage frequently asked questions and provide quick answers to customers or personalized recommendations for banking or financial products and services, improving the customer experience and customer service and extending its coverage to 24/7.

In a recent study, the McKinsey Global Institute (MGI) has estimated that, in the Banking sector, Generative AI also known as “GenAI” could have a huge impact generating an additional value of between $200 billion and $340 billion per year; all this by increasing productivity in functions such as marketing, sales and customer interactions, among other use cases. [1].

Use cases of conversational AI in Banking

If we put the focus on marketing activities and customer interaction, we can consider multiple use cases for Artificial Intelligence, but in this specific article we will focus on Conversational AI.

Conversational AI is a branch of artificial intelligence that simulates human conversations (if you want to know more about Conversational AI, you can read our article: “What is Conversational AI and how did we get here“).

The strategies of closure and reduction of physical offices of banks, due to the growing demand by customers for non-face-to-face services, have transferred a significant volume of these face-to-face interactions to the digital customer service channels that banks make available to their customers: digital banking, telephone banking…

Perhaps the most relevant use cases for the application of Conversational AI in Banking are focused on the automation of conversations through AI Conversational Assistants or intelligent banking assistants.

Banking Assistants

AI has been integrated into mobile apps and voice assistants, allowing customers to perform banking transactions, check balances and obtain current financial information immediately through voice commands.

Recommendations of personalized products and services, such as savings accounts, credit cards or investments.

Wizards for improved Customer Service, thanks to the attention of frequently asked questions and provide quick answers to customers online, improving customer service 24 hours a day.

Benefits of Conversational AIs in Banking

We can not fail to review some of the key benefits from a Customer Experience (CX) perspective, we could surely list many more, but to point out the most important ones are:
- Extending customer service and transactional request management to channels with high demand such as WhatsApp or chats in a fully automated way.
- Improving accessibility to services for groups with disabilities or the elderly after the disappearance of physical offices, adapting the experience to their needs and most common operations and keeping open the possibility of referring them to other services.
- Hyper-personalization of the customer experience based on data analysis, with the possibility of personalized notifications and recommendations.
- Service improvement and offer prospecting taking into account real customer demands.
Additionally, it is not easy to ignore the economic impact that AI could bring to the banking sector equivalent to an additional value of between 200 and 340 billion dollars per year! A not inconsiderable sum…

Challenges of AI in Banking

The integration of artificial intelligence (AI) in the banking sector, while promising, is not without significant challenges. Challenges that are not entirely different from those found in other sectors.

Protecting the privacy of financial data becomes crucial, with the need to ensure robust security measures. In addition, AI, lacking a full understanding of the human context, faces challenges in interpreting nuances and making ethical decisions, underscoring the importance of addressing these issues for effective and ethical deployment in future banking.

In conclusion, the integration of Artificial Intelligence taking place in the banking sector has marked a significant milestone, as it is redefining the customer experience. From intelligent assistants that provide instant answers to data analytics systems that personalize recommendations, AI has driven a positive revolution in customer service.

This marriage of advanced technology and financial services has not only improved operational efficiency, but has also raised customer satisfaction, setting a higher standard for the industry as a whole. Artificial intelligence is not just a tool, but a catalyst that has transformed the way customers interact with and perceive banking services, paving the way for a more agile and user-centric financial future.

Discover how MAIA can be your best ally. Experience firsthand how Artificial Intelligence is a determining factor in the transformation of the Customer Experience in Banking.

[1] The economic potential of generative AI: The next productivity frontier, McKinzey June 14, 2023
22/11/2023
Intelligent conversational assistants for the internal management of companies. HR Case
How can conversational AI contribute to HR work?

There are more and more technological tools that allow managing talent and internal communications in Human Resources teams, being able to identify and develop the people who lead their teams towards the established organizational goals. Can you imagine all that a Conversational AI or intelligent conversational assistants can do in your company?

During the last few years, Artificial Intelligence has positioned itself as an indispensable tool to make the internal management of companies more efficient and, as expected, the optimization of human resources management is no exception.

Intelligent conversational assistants can enhance the efficiency of selection processes, employee care, data analysis, communications, retention and training processes, among others.

If you want to know more details about Conversational AI you can read our article: What is Conversational AI and how did we get here?

Conversational AI such as MAIA can boost job performance and improve the experience in the corporate environment.

Among the possible uses of conversational AI in HR, the following stand out:

Recruitment and Selection

Conversational assistants can assist in reviewing resumes, scheduling interviews, conducting candidate satisfaction surveys and managing candidate communications.

Employee onboarding

Conversational AI facilitates the onboarding process for new employees. It provides information on company policies and procedures, delivers necessary documents, and answers common questions that have historically been handled manually by people.

Training and development

Intelligent conversational assistants can offer information on training programs, provide development materials, and track employee progression in their professional development.

Benefits and compensation management

Assistants can answer benefit questions, provide flexible compensation information, assist with enrollment in benefit plans, and track employee applications.

Time management and attendance

Conversational assistants have the ability to help employees, for example, request vacation time, record hours worked, and resolve questions about time and attendance policies.

Troubleshooting and frequently asked questions

Attendees can provide answers to common employee questions, such as company policies, leave request procedures and more.

Performance evaluation and feedback

Assistants can help in gathering feedback from employees on their performance evaluations, facilitating the feedback process between employees and supervisors.

Internal communication

Attendees can send event reminders, important announcements and personalized messages to employees, improving internal communication.

Documentary management

A conversational AI makes it possible to access organizational documents in a simple and structured way without the need for infinite and complex searches. They provide quick, simple and accessible information.

What are the benefits of Conversational AI within HR?

Intelligent conversational assistants brings many benefits to HR teams, some of the most prominent:
- Improve employee experience and engagement.
- Allows for a higher participation and response rate in the team.
- Time optimization.
- Improved internal communication.
- Traceability of interactions to anticipate scenarios.
- Assists in the detection of behavioral patterns, such as Burnout syndrome.
We can conclude that conversational AI is currently a tool with great benefits for companies and their teams. By incorporating these innovations, organizations can stay ahead of the curve in an ever-changing business environment.

Do you want to take the management of your teams and their communications one step further?

Anticipate with #MAIACognitive.
15/11/2023
Artificial Intelligence arrives to revolutionize customer service efficiency
Consumer behavior has changed. They come for the product or service, but stay for the customer experience. Consumers want immediacy and quality, but it is often difficult to obtain these two characteristics due to the tight budget for these services.

The costs of everything have increased and so have labor costs. At that point, you have to think, where can I optimize costs without having to decrease the quality of my service? It is a difficult task, because what differentiates one company from another is not only the price, but also the customer service it provides to its clients. For that reason, companies have to start thinking about differentiation, and yes, technology is the key.

For the past few years, AI has been all the rage, but not everything that exists is AI. Simple business rules, simple statistics… they call it “Artificial Intelligence”. But Artificial Intelligence (AI) is not that, but the combination of algorithms created with the aim of solving processes that a human being could perform, but perhaps spending more time. For this reason, Artificial Intelligence has become a necessary tool to transform customer service and customer experience. AI can maximize resources, i.e. people, and increase profitability through the efficiencies it generates.

Savings from Artificial Intelligence application

AI can be applied in various areas of customer service, from task management to providing customers with products or services that meet their needs. These types of tasks performed by AI allow us to streamline processes and offer more efficient solutions.

Here are some examples of how AI is generating savings in this field:
1. Automation of repetitive tasks: One of the biggest savings comes from automating routine tasks. This not only improves customer satisfaction by providing immediate responses, but also frees up customer service agents for more complex tasks. For example, why talk to a person to block your lost card when you can do it yourself by talking to a conversational assistant? MAIA, for example, is able to perform this kind of tasks and many more.
2. Data analytics: AI can analyze large volumes of data from customer interactions to identify patterns and trends. In this way, companies can understand their customers’ needs and preferences, enabling them to make informed decisions on product, service and process improvements. AI can offer the electricity tariff that best suits the consumption of individuals.
3. Personalization: AI can personalize the customer experience by providing targeted recommendations based on purchase history and past behavior. This not only increases customer satisfaction, but also drives sales and retention. If you are always traveling abroad in summer, why not offer the best roaming rate?
4. Reduction of human error: Humans are prone to make mistakes, especially in repetitive tasks due to mental fatigue. AI can help reduce these errors, reducing costs associated with returns, refunds and customer complaints. Human beings get married and sometimes make mistakes, a conversational assistant is always fresh and always does what you have taught him without making a mistake.
5. Scalability: AI allows companies to scale their customer service operations more efficiently, without the need to hire large numbers of staff. This saves labor costs and ensures consistent customer service at times of high volume. Life is full of unforeseen events… so you can have 2 virtual assistants or 100 when you need them.
Higher levels of efficiency and satisfaction

AI is playing a crucial role in transforming the customer experience, generating huge savings for all those companies that implement it.

Certainly those companies that adopt AI solutions in their customer service strategy are better positioned to deliver exceptional experiences with high levels of efficiency and satisfaction, reducing costs and staying competitive in a constantly evolving market.

The present and future of customer service is undoubtedly driven by AI.

Do you want to improve your customers’ experience? Find out how MAIA can help you.
03/11/2023
What is Conversational AI and how did we get here?

Conversational AI is a branch of artificial intelligence that simulates human conversations.

In order to understand what conversational AI is, it is essential to know the concept of Natural Language Processing and how it has evolved to allow us to talk about conversational AI.

Natural Language Processing

Natural language processing seeks to teach machines to understand, interpret and generate human language responses. Its beginnings date back to the 1950s, when researchers started working with rule-based approaches and formal grammars to analyze and generate text. However, these systems were very limited as they relied on predefined rules and could not adapt to variations or ambiguities of natural language.

In order to solve these problems, important advances have been made in recent years, of which we can highlight some key moments.

Neural Networks

In the mid-1980s, artificial neural networks applied to NLP began to be used. These were inspired by the structure and functioning of the human brain to develop models capable of learning complex patterns and structures from data. As the availability of large amounts of digitized text data and computational capacity increased, machine learning became an essential tool for the evolution of NLP.

Deep Learning

Already at the beginning of the 21st century, NLP underwent a significant transformation thanks to the application of deep algorithms, what we know as Deep Learning. These models improved the ability of systems to understand and generate natural language text, as well as making them more robust and accurate, which is why they began to be applied to tasks such as machine translation, text summarization, text generation or document classification.

Transformers

The big change came with the introduction of Transformers in 2017. The most remarkable thing about this architecture is that it contains an attention matrix that evaluates the relationship of each word with all the other words in the sentence, thus taking into account the whole context and allowing to grasp the meaning of the text as a whole.

Generative Models

And I’m sure by now you’re wondering at what point we’re going to talk about the famous ChatGPT. Well, the emergence of generative language models such as ChatGPT or LLaMa, introduced in late 2022 and early 2023, are based on an architecture containing several layers of Transformers, which implies that they have hundreds of millions of parameters and also require a corpus with billions of words for training. These models have marked a before and after in AI and are generating strong discussions regarding their impact, as their capabilities in terms of text generation are almost comparable to those of a human.

Once we have traced the evolution of natural language processing, it is easier to understand how we have come to develop what we know today as conversational AI. This is based on models that allow the understanding of language and context, as well as the generation of responses according to the conversation and similar to those that a human would give.

Would you like to know what a Conversational AI can do for you? We invite you to meet MAIA.

26/10/2023
Chatbots vs. Conversational Artificial Intelligence

In our busy day, we often have to contact our bank, cell phone provider, or even the gym! In addition, we are often short of time, and therefore want to resolve our queries and formalities as quickly as possible.

Before making a phone call, we make use (voluntarily or involuntarily) of chat or messaging applications such as WhatsApp. In this way, companies provide us with the information we need without having to talk or interact with a person, and for this they use “chatbots”.

“Chatbot” is a very common term to name, in a standard way, all the technologies that allow to automate the answers in a chat without the need for human intervention.

So, when we present MAIA as a conversational Artificial Intelligence, it is normal that we encounter the following questions from some customers:

Is a Chatbot the same as a Conversational AI? What are the differences between one and the other?

Let’s start with the most important: No, a Chatbot and a Conversational Artificial Intelligence are not the same, but what is the difference?

Conversational Artificial Intelligences are mainly characterized by capabilities such as:

Speech recognition.

Unlike chatbots, conversational AI’s are designed so that, in addition to processing written language, they can identify spoken language.

Chatbots are usually limited to written channels such as chat or messaging services (WhatsApp, Telegram, etc).

Natural Language Understanding (NLU).

A Conversational Artificial Intelligence, it is able to recognize a customer’s requests when he expresses himself in his own words, so it is open to recognize complex requests. Not everyone says the same thing in the same way. For example.

In the case of chatbots, there is no comprehension capability, the way a customer interacts is through predefined options or a decision tree that guides the conversation through specific topics.

Dialogue management.

Conversational AI’s are dialog-oriented. They manage interactions dynamically. They also have the ability to “remember” what they have discussed in previous requests with a customer, pick up on topics, because they manage the context of the entire conversation.

In contrast, a chatbot manages interactions in a fixed, rule-based way, often guided by menus of options, with no flexibility or ability to go outside the flow. Chatbots, moreover, do not have the ability to remember previous queries to execute or respond to complex requests.

Natural Language Generation (NLG).

A conversational AI responds using human and natural language to customer requests and can adapt to the way of communicating in a given situation. While a chatbot gives predefined answers that do not vary or adapt to the context that the customer may have provided.

Machine Learning (ML).

The use of Machine Learning allows conversational AI to constantly learn from responses, conversation history and user preferences; this allows to personalize responses and user experience. It also allows the quality of responses to constantly evolve because AI learning is parallel to its operation.

Technicians take advantage of all the experience acquired by Conversational AI to teach it what to say in new situations not previously identified.

Chatbots do not learn, their responses are static so they do not allow customizing the user experience; they also need manual programming to keep them updated.

The future… Conversational Artificial Intelligence or Chatbots?

What is certain is that both Chatbots and Conversational AI’s are here to stay.

Both allow companies to automate information processes in marketing, sales and after-sales services, mainly thanks to their ability to provide 24/7 attention.

However, with the recent emergence of Open AI and Chat GPT, and the visibility that is being given to the capabilities provided by Generative AI models, many companies have considered using, or evolving their chatbots, and implementing Conversational AI.

But what is the main reason? It is undoubtedly to provide a better customer experience, more personalized attention and, above all, to attend to more complex processes in an automated way, resulting in significant savings.

Do you have any doubts about why you should choose a Conversational AI?

If so, we’d be happy to talk to you! Shall we talk?

11/10/2023
Interview Chief Digital & Information Officer of MAIA at Contact Center Hub

Eduardo Balseiros, Chief Digital & Information Officer of MAIA, was interviewed by Contact Center Hub about the new trends in the BPO world. During the conversation, Eduardo shared his vision and experience on the application of digital strategies and Artificial Intelligence (AI) technologies in the world of customer service.

Eduardo emphasized the importance of deeply understanding the needs and preferences of consumers in the digital era. According to him, “Digitizing is not just about automating processes or implementing chatbots. It involves a deep understanding of what our customers really need.”

The interview also served to highlight MAIA’s capabilities as an end-to-end solution, which combines customer service expertise and knowledge combined with machine learning algorithms to create the most satisfying conversational experience possible. MAIA’s main goal is to help companies improve customer service and provide the best conversational experience.

Enjoy the full interview Here!

MAIA Team, Eduardo Balseiros and Miguel Catanedo (in the center)

09/10/2023