Artificial Intelligence Technology

Top questions about Machine Learning: pt. 1

Artificial intelligence and machine learning have been technologies that many companies have bet on. They are indeed complex but offer us a lot of benefits.

In a series of 4 articles, we have compiled the most relevant questions about Machine learning.

Shall we get started?

What is machine learning?

Machine learning is a branch of computer science that deals with programming systems to automatically learn and improve with experience. 

For example: Robots are programmed to be able to perform tasks by relying on data they collect from sensors. The programs learn automatically from the data.

What is overfitting in machine learning?

In machine learning, when a statistical model describes is good at predicting seen samples, but performs very poorly on unseen samples. 

The model shows poor performance that has been overfitted.

Why does the overadjustment occur?

The possibility of overfitting is due to the fact that the criteria used to train the model are not the same as those used to judge the effectiveness of a model.

It is possible that it was forgotten to leave enough samples to test the generalization power of the model on unseen samples.

In this technique, a model is usually given a set of known data on which the training is run (training data set), and a set of unknown data against which the model is tested.

The idea of cross-validation is to define a data set to “test” the model in the training phase.

Having technology such as Machine learning will always be an opportunity for innovation on the part of insurance companies. That is why, as well as this one, we will bring you in the coming days, a series of articles where we will develop the whole topic.

You can’t miss it!

Artificial Intelligence

Data science: these are its best uses pt. 2

This week we start with an interesting topic, Data Science and its uses. So, as promised, here is the second part, where you will see 3 more applications and how it impacts insurance.

So let’s get started!

Virtual patient assistance and customer care

Optimization of the clinical process is based on the fact that in many cases it is not really necessary for patients to visit the doctor in person. In simpler words, a mobile app can offer a more effective solution if it brings the doctor to the patient.

Another way to look at this point, is through AI-powered mobile apps, which can provide basic healthcare support, usually as chatbots. 

This translates to describing our symptoms or answering questions and then receiving key information about the medical condition derived from a network linking symptoms-causes. Similarly, apps can remind us to take medication and, if necessary, assign us to a doctor’s appointment.

What benefits can we get from all this?

  • Promotes a healthy lifestyle by encouraging patients to make healthy choices.
  • Saves time for patients who will not have to wait in line for an appointment. 
  • Allows physicians to focus on more critical cases.
Advanced image recognition

A clear example of this point is Facebook and its way of suggesting tags of our friends in various photos. This automatic suggestion function uses a facial recognition algorithm.

Continuing with the first point, another example we can mention is the ability of our AI to recognize damage caused to vehicles or homes, due to crashes, floods, leaks, among others.

Data science in the insurance sector

According to the article, transforming data to generate knowledge and optimize intelligent decision-making in business is a valuable tool. This will allow us to measure the effectiveness and customer satisfaction in every interaction.

Data is the gateway to the development of new products and services

Immediately, in the case of insurers, data analysis makes it possible to categorize users and idenify the type of service to provide them and to meet their needs.

Machine learning support is necessary because it speeds up the risk assessment of a potential policyholder, which is a key differentiating factor. 

As a result, companies reduce costs and improve the effectiveness of services, offering competitive prices in the market that also allow them to cover indemnities.

What can we conclude?

First, having an adequate infrastructure to capture and process information becomes vital to keep up with the current technological pace. It is not only a question of a single industrial sector, but Data Science is present everywhere.

Understanding the impact and benefits that we can obtain from this, will make it possible that more than a “threat”, it will be an opportunity for growth in a world so competitive and transformed as a result of the pandemic.

We cordially invite you to follow this series of articles! In the next contents we will focus particularly on the insurance sector.

Artificial Intelligence

These are the uses of Data Science: pt. 1

Data Science applications shook us all up in no time. Thanks to storage and faster computing, we can predict results in a few minutes, which could take a lot of human hours to be possible. 

In this series of articles that is about to begin, we will tell you about the most relevant uses of Data Science and finally the impact within the insurance industry.

01.Fraud and risk detection

It is known that the first applications of data science were in finance, in fact companies were tired of debts and losses every year. However, these had a lot of data that used to be collected during the initial paperwork when sanctioning loans, so they decided to bring in data scientists to rescue them from losses.

Thus, over the years, banking companies learned to divide and conquer data through customer profiles, past expenses and other essential variables to analyze risk and default probabilities. All the above helped them to push their banking products based on the customer’s purchasing power.

02.Health care

The healthcare sector receives great benefits from data science applications and here are the most important ones, according to Edureka’s article.

  • Medical image analysis: procedures such as tumor detection, artery stenosis and organ delineation employ several methods and frameworks to find optimal parameters for tasks such as lung texture classification, machine learning, content-based medical image indexing and wavelet analysis for solid texture classification.
  • Genomics and genetics: data science enables an advanced level of treatment personalization through genomics and genetics research. Consequently, it aims to understand the impact of DNA on health and to achieve biological connections on an individual basis between genetics, disease and drug response.

Similarly, data science techniques make it possible to integrate various types of data with genomic data in disease research, enabling a deeper understanding of genetic problems in the reactions of particular drugs and diseases. As soon as reliable personal genome data is obtained, a more extensive understanding of human DNA will be achieved.

03. Drug development

The drug discovery process is very complex and involves many disciplines. The best ideas are constrained by billions of tests, which translates into a large financial and time investment.

On average, it takes twelve years to make an official submission. Data science applications and machine learning algorithms simplify and shorten this lengthy process, adding insight at every step, from initial screening of drug compounds to predicting success rate based on biological factors.

Algorithms can predict how the compound will act in the human body by using advanced mathematical models and simulations, rather than traditional “lab experiments”. The idea behind computational drug discovery is to create computer model simulations of a biologically relevant network that simplifies the prediction of future outcomes with high accuracy.

Other uses are still missing! That is why we cordially invite you to follow this series of articles!

Artificial Intelligence

Big data: The formula for better decisions and strategic businesses

Today’s products and services depend on machines to automatically perform tasks such as reading documents, recognizing faces in images, understanding the emotion contained in the tone of voice of a telephone conversation, answering a customer’s questions in a chat room, predicting the energy expenditure of a factory, inferring which movies or songs each person will like best, among others.

And what do all these tasks have in common? Well, they require collecting and perceiving everything that happens in the environment through data acquisition, and all of them need to process the information for its interpretation and decision-making.

This is the origin of Big Data, a term that describes the large volume of structured and unstructured data that occupy large areas of business. What is really important about Big Data is what companies can do with the data, since by analyzing it they can obtain ideas that lead to better decisions and strategic business moves, as shown in the article “Big Data: What is it? Its importance, challenges and governance“.

The first objective is to manage the large amount of data, i.e., once Big Data architectures allow the storage and processing of thousands of petabytes of data, the challenge is to move on to the phases of data acquisition and interpretation to extract knowledge.

The Internet of Things (ioT), is a great contribution to be able to collect data, while cognitive computing, brings intelligence to extract knowledge.

The rise of M2M systems

M2M or Machine to Machine within the framework of the Internet of Things has promoted an exponential growth in the exchange of data between the machines themselves, moving from a traditional model where sensors obtained information that was then used by humans to a model where machines are autonomous, since sensor data are not “consumed” by humans, but are part of the network’s perceptive system.

There is currently an “ideal storm”, born thanks to the convergence of multiple technologies (Cloud, ioT, Big Data, among others), with which companies will be able to revolutionize themselves to more personalized services and products.

LISA has a technological fusion with which we provide the insurance industry so that they can leave aside the conventionality of their processes, offer new products and services and strengthen the relationship with their policyholders.

Visit our products section and learn more about our technologies.

Artificial Intelligence

Deep learning: The secret sauce of Artificial Intelligence

Who would have thought that at some point those robots and systems with human-like intelligences that we saw in the movies would be real? It seemed a matter of fiction, but the truth is that it happened.

The momentum of the digital transformation to which organizations were subjected, turned them into entities with a huge appetite for data and the desire to demand systems with advanced intelligence, capable of processing an avalanche of data. This whole new vision has been occurring in the vast majority of industries and it is really strange that some companies have not sought to benefit from intelligent and automated data analysis.

Learning is one of the keys to advanced AI, in fact we need machines to be able to self-program, i.e. learn from their own experience. Machine Learning addresses this challenge and cloud services to build applications that learn from the data they ingest.

What is Deep learning?

Machine learning is booming thanks to its application in the world of Big Data and IoT. Advances and improvements of the most traditional algorithms are constantly appearing, from ensemble learning to Deep Learning, which is on everyone’s lips due to its ability to get closer and closer to human perceptive power.

According to the article “Cloud Deep Learning: top three platforms compared“, it can be stated that deep learning is based on the concept of a deep neural network, which passes inputs through multiple layers of connections. Neural networks can perform complex cognitive tasks, improving performance dramatically compared to classical machine learning algorithms.

However, they often require huge volumes of data to train and can be very computationally intensive.

Deep learning uses logical structures that resemble the organization of the mammalian nervous system, having layers of processing units (artificial neurons), which specialize in detecting certain characteristics of perceived objects.

The Deep Learning represents a more intimate approach to the way the human nervous system works. This is because our brain has a complex microarchitecture, in which differentiated nuclei and areas have been discovered (its neural networks are specialized for specific tasks).

Once companies can have data and systems capable of processing it, it is time to take a big step: understanding that data, acquiring knowledge and extracting value, in simple words, something similar to what we humans do when we access data, interpret it using our brains and make decisions.

However, when we talk about gigabytes, terabytes or even petabytes of information, coupled with the need to make decisions in just milliseconds, humans are literally out of the game.

The most viable solution is to turn to machines that are able to interpret the data, understand it and intelligently draw conclusions.

Having explained all the above, we can say that Big Data, is, therefore, the fuel of Artificial Intelligence and therefore very relevant to LISA processes. We feed on the processed data and learn from it, creating and recognizing patterns and developing sophisticated analytics solutions for the insurance industry.

Let’s find out more about Big data and its impact in the next article, shall we?

Artificial Intelligence

How much will AI influence insurance in the next decade? Pt. 2

The sudden change that occurred in the last year, 2020, came overnight and organizations had to adapt to remote working and expand their digital capabilities. While most companies probably did not invest in AI during the pandemic, the increased emphasis on digital technologies and a greater willingness to embrace change will put them in a better position to be able to engage AI in their operations.

As a promise is a promise, here we share with you the trends that we missed in the previous article, which will complement and highlight the importance of constantly renewing companies to serve as development for their future:

3. Rise of data and open source ecosystems

As more data is in many places, open source protocols will emerge to ensure that data can be shared and used across industries. Many public and private entities will come together to create ecosystems to share data for multiple use cases under a common regulatory and cybersecurity framework.

An example of the above, is that portable data could be transferred directly to insurance companies, and data from connected homes and automobiles could be made available through Amazon, Apple, Google and a variety of consumer device manufacturers.

All this would make interoperability possible, which is really beneficial in a world where we are connected at all times, by enabling the ability to communicate between different systems and different data, so that information can be shared, accessible from various environments and understood by any of these.

What are the benefits?
  • Ensures barrier-free communication between all stakeholders associated with a traditional insurance company: insurance banks, startups, brokers, brokers and end customers.
  • Increases customer satisfaction.
  • Improves service quality.

4. Advances in cognitive technologies

Neural networks and other deep learning technologies currently used primarily for image, speech and unstructured text processing will evolve to be applied in a wide variety of applications. These cognitive technologies, which rely on the human brain’s ability to learn through decomposition and inference, will become the standard approach to processing the large and complex data streams that will be generated by “active” insurance products linked to behavior and occupations.

With the increasing commercialization of these types of technologies, operators will have access to models that are constantly learning and adapting to the world around them, enabling new product categories, while responding to changes in risk or behavior in real time.

Although technology has been by our side for years and has massified enormously, the truth is that in some industries it has taken a long time to arrive for one reason or another. That is why we must take advantage of the opportunities we have in our hands.

The insurance industry has not been as fanatical about technology as it would like to be, but new clients and trends are forcing it to move as fast as possible in order not to fall behind and keep up.

With artificial intelligence as a support, the insurance industry will be able to improve and create new products and services that are more accurate and useful, thus guaranteeing the satisfaction and recommendation of happy customers.

Artificial Intelligence Technology

How much will AI influence insurance in the next decade? Pt. 1

Technology is advancing by leaps and bounds and does not look back, which is why over the next few years we expect endless opportunities thanks to the massification of artificial intelligence in many industries.

Can you imagine the insurance industry being more influenced by technology? The truth is that this will be the case in the next decade and we must prepare ourselves now so as not to be left behind and to be able to achieve more competitiveness, differentiation and influence.

How do you think your customers would feel if you provide them with timely communication, facilities to contract new products and services that are tailored to their needs, secure processes and interconnected platforms that work 24/7?

It sounds a bit utopian to think that one of the industries that has had the hardest time getting out of its comfort zone can grow and develop to the point where it does not need manual labor, paperwork and long response times, doesn’t it? However, it is projected that within a decade, the insurance industry will undergo a metamorphosis as a result of artificial intelligence.

With the new wave of deep learning techniques, artificial intelligence has the potential to fulfill its promise of mimicking the perception, reasoning, learning and problem solving of the human mind.

In the same vein, technological evolution will enable insurance to move from its current state of “detect and repair” to “predict and prevent,” transforming all aspects of the industry in the process.

As AI becomes more deeply integrated into the industry, carriers must best position themselves to respond to the changing industry landscape. In that regard, insurance executives will need to understand the factors that will bring change and how AI will transform everything. Here are some of the technology trends that are enabled or enabled by artificial intelligence:

  1. Data bursting from connected devices: many industries have connected equipment with sensors, however, over the years this will increase.

The penetration of existing devices such as cars, fitness trackers, home assistants, smartphones and smartwatches will continue to grow rapidly. It is estimated that there will be up to one trillion connected devices by 2025.

The avalanche of new data created by the aforementioned devices will enable operators to understand their customers more thoroughly, resulting in:

  • New product categories.
  • More personalized prices.
  • Real-time service delivery.

And on the other hand, we get:

2. Major heyday of physical robotics

The field of robotics has had many achievements today and this innovation will continue to transform the way people interact with their surroundings. 3D printing will radically reshape the manufacturing and commercial insurance products of the future and it is expected that by 2025, 3D printed buildings will become more common and how this development changes risk assessments will need to be evaluated.

Related to the above, programmable autonomous drones; autonomous agricultural equipment; and improved surgical robots will also be more widely employed. By 2030, a much higher proportion of standard vehicles will have autonomous features such as driving.

We want to expand a little more on the subject, but in order not to make this article so long and heavy, we invite you to wait for the second part, which is quite interesting, so if this technology has not yet convinced you with everything you have read, with the second part it will.

Expect it soon!

Artificial Intelligence Technology

Do traditional industries employ machine learning?

Yes, and they use it to gather new business knowledge. We explain it to you in this simple example:

A machine learning startup called Second Spectrum created predictive models so that coaches could distinguish players with good shots and others who take bad shots and thus evaluate and correct the consequences in the middle of basketball games in the US.

Another example can be General Electric, a company that has a history of more than 130 years. This industry has made thousands of dollars processing data it collects from deepwater oil wells or jet engines to optimize performance, anticipate breakdowns, and streamline maintenance processes.

What’s Outside North America?

In Europe there are different banks that have changed the old statistical modeling approaches for machine learning techniques and in many cases, they were able to improve their numbers: they increased sales of new products by up to 10%, achieved savings of 20% in capital expenditures and even the 20% increase in cash collections.

How did they go about achieving all this? Through the design of new recommendation engines for retail and Pymes customers.

Are we close to machines replacing humans?

It is indisputable that changes as a result of the emergence of technology are coming and the data that is generated throughout the process is done with such speed that human participation has been moving aside.

However, in the coming years, we will see more of the use of Artificial Intelligence, as well as the development of autonomous corporations. Without a doubt, large companies will be able to carry out objectives autonomously and without direct human supervision.

If you want to revolutionize your insurance company with cutting-edge technology, this is your opportunity to work hand in hand with LISA.

Artificial Intelligence

These are the benefits of Machine Learning for insurers

As the analog world becomes increasingly digitalized, our ability to learn from data by developing and testing algorithms will become increasingly important to traditional businesses as we can meet the deepest needs of our customers and nurture the business with new products and services that adapt to each person.

What is Machine Learning?

These are algorithms that can and have the ability to learn from data without relying on specific rule-based programming.

Machine learning became a scientific discipline in the late 1990s, when constant advances in digitization and computing allowed data scientists to delve into how to train computers for certain functions.

The unmanageable volume and complexity of big data that the world is immersed in today has increased the potential of machine learning and the need for it to be more present than ever.

What is its impact on the insurance industry?

Due to the massive use of the internet, it is indisputable that within the insurance industry, agents want to better understand the needs of their clients and future policyholders. On the other hand, and in a similar wave, are the policyholders, who want better products and processes for their claims.

Both parties can benefit from getting more intelligence from the data.

The insurance industry clearly must take a page turn and overcome many years of accumulation of printed documents, which are known as “dirty data”, since it cannot be digitized so easily.

What if AI and Machine Learning could transform that valuable data into more accessible and user-friendly information? Clearly employees, customers and agents could get more out of the data.

To support and facilitate the digitization process in parallel, data can be scanned into a system, data patterns can be identified using machine learning, and the value of the data can be extracted through a highly automated process.

Predictive modeling will always be a good option for insurers as it can outline a retail business line that is insuring and track the sentiment of each customer. This way, agents will be able to study and determine who is happy with the service or, on the contrary, who can buy insurance elsewhere.

There is no doubt that these technologies can make the job of those who work in the insurance industry easier, more predictable and more precise, which translates into more profits and savings for clients.

At LISA, we take care of providing cutting-edge technology to traditional insurance companies, in order to make them more competitive, agile and disruptive.

And what are you waiting for to revolutionize the insurance industry?