Tag: GenAI

Artificial intelligence in insurance
Introduction

Artificial Intelligence is having a significant impact today and the insurance industry is no exception. In the insurance industry AI is being used to improve customer experience, operational efficiency and profitability.

Conversational AI is a technology that enables communication between computers and people through natural language, both written and spoken. Conversational AI is based on natural language processing (NLP), which is the ability to analyze, understand and generate human language.

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

Technological advances promise a significant expansion of Conversational Artificial Intelligences in the insurance sector.

Fields of application of AI in the Insurance Sector

It helps insurers to manage claims more efficiently and effectively. For example, conversational AI is used to facilitate communication and coordination between the different actors involved in the process, such as the client, the adjuster, the garage or the insurer, and to optimize resource allocation and decision making. All this allows faster and more informed decisions to be made.

Risk assessment

In this sense, Artificial Intelligence refines risk assessment, enabling the collection and accurate analysis of data from various sources, such as electronic sensors, social networks, medical records or public records. In addition, it enables the creation of predictive models capable of estimating more accurately the probability and impact of potential adverse events, thus allowing early warnings to policyholders.

Personalization of products and services

AI can help us to accurately identify the varied needs, preferences and expectations of individual customers. This approach translates into the creation of personalized proposals that are specifically tailored to each risk profile. Moreover, these proposals can be communicated by conversational AI through various channels such as WhatsApp, emails, voice or other channels agreed between the company and the customer.

Fraud prevention

Fraud prevention is crucial for insurance companies, as it helps minimize financial losses, establish competitive rates and preserve customer confidence by ensuring integrity and fairness in claims handling.

Using anomaly detection techniques, the establishment of verification and validation mechanisms are actions that can currently be carried out by Artificial Intelligence developments. Conversational AI facilitates the identification of verification patterns or identify specific terms that may indicate fraud or inconsistencies in the customer’s history, potential signs of deception.

The Future of Conversational AI in the Insurance Industry

Technological advances promise a significant expansion of Conversational Artificial Intelligences in the insurance sector. Further automation is foreseen, not only in existing processes, but also in the creation of new innovative products. The emergence of these products will allow the creation of insurance products that are more adapted to the singularities of the users, thus marking a significant change in the services offered in the sector.

Exploration of case studies on Artificial Intelligence in general in the insurance sector.

After a brief investigation of the sector, we have been able to identify companies that have applied AI in business processes with interesting results. Some examples of these case studies are:
- The analysis of customer claims and assignment to the most appropriate claims managers according to their complexity and urgency. The system also helps to detect potential fraud and improve customer satisfaction.
- Offering travel insurance based on real-time flight data. The systems are able to use blockchain-based smart contracts to automate the payment of compensation in case of flight delays or cancellations, without the need for the customer to file a claim.
- Machine learning to analyze claims data, underwriting and other processes. A company that applied artificial intelligence to these processes and among the benefits were: reduced fraudulent claims, increased underwriting accuracy and optimized business processes.
- Using machine learning to analyze weather trend data, traffic data and other data, which has helped the company better understand the risks associated with extreme weather events, driver behavior and other factors.
In summary, companies in the insurance sector are progressively integrating artificial intelligence into the core of their business structure. In this context, conversational artificial intelligence plays a crucial role in the smooth interaction in a sector where there are certainly many players, such as the customer, the adjuster, the workshop, the mediator or the insurer…

Want to see how MAIA can help improve your business processes and customer experience? Book your demo here.
28/12/2023
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
Generative Artificial Intelligence
What is GenAI and who are the main players?

For several years now, Artificial Intelligence has been revolutionising our society and transforming the way we interact with technology. But many people call things “Artificial Intelligence” that are not Artificial Intelligence… so what is Artificial Intelligence?

The concept of Artificial Intelligence was born in the 1950s, when pioneers such as Alan Turing and John McCarthy began to explore the possibility of creating machines that could simulate human thought. It was at that time when it was determined that AI is the ability of machines to learn, to reason and to make decisions autonomously, imitating human intelligence. Over the last few years, Artificial Intelligence has grown spectacularly and we see it in a lot of things and the concept of Generative Artificial Intelligence has become a trending topic.

Generative Artificial Intelligence

Generative AI is a branch of artificial intelligence that focuses on creating original and autonomous content, such as images, music and text, from input data. The difference between traditional AI and generative AI is that the first is based on rules and historical data and the second uses neural networks to produce new results.

Generative AI has been in development for several years, but it was the rise of ChatGPT that made it popular. The influence of Open AI’s GPT-3 (Generative Pre-trained Transformer 3) language model has been abysmal, setting an important milestone for the high quality of the texts generated thanks to prompts.

But what is a Prompt? A prompt is a question, a request, an instruction to an AI program to generate a result. Depending on the AI you use, you can create articles, images, music, videos or programming code.

Principal players

A number of companies are currently leading the way in the development of generative AI. These include:
- Google: has developed a number of generative AI models, such as BARD based on the LaMDA experimental language model designed by Google specifically for dialogue applications. Like ChatGPT, it is a conversational system where the user interacts with the model through regular messages and receives an output.
- OpenAI: Well known for its ChatGPT, OpenAI has also developed DALL-E 2, which is an interface that is able to generate realistic images from textual descriptions.
- Meta: Mark Zuckerberg’s company also has its own Generative AI product called Llama 2, which it has recently incorporated into its company products.
- Adobe: has developed the Sensei model, capable of generating creative content, such as videos and animations, and Firefly, which, from just a few words, can create graphics, use infinite colour combinations, fill or enlarge images and generate text effects.
- Microsoft: The Redmond-based company has Github Copilot, which is an AI assistant that helps develop code. It also has the Microsoft 365 Copilot product, which is the office suite assistant that makes certain types of tasks easier.
The truth is that every day more and more companies, using AI, help us to make our lives easier and increase our productivity. Like MAIA, which is a conversational AI capable of optimising the customer experience, improving customer satisfaction by managing complex processes in shorter times and in any contact channel.

MAIA is the conjunction of technology and human experience; it provides, first-hand, what users need thanks to the data and the interpretation of the people who work with them on a daily basis. Do you want to know more about MAIA? You can do it here.
20/10/2023