Machine learning is training a mathematical model on historical data in order to predict an event or phenomenon on new data. That is, an attempt to force program algorithms to perform actions based on previous experience, and not only on the basis of available data. For training, you need historical data (training sample) and the value of the target variable (what we predict), which corresponds to the given historical data. The model observes and finds dependencies between the data and the target variable. These dependencies are used by the model on a new dataset to predict a target variable that is unknown. Machine learning includes a whole set of methods and algorithms that can predict a certain outcome from input data. For example, if you have some information on how much a security was worth at any given moment over a long period of time, machine learning algorithms can predict how much these securities will be worth in the future. There are a lot of machine learning algorithms: some are effective for solving one type of problem, the other for another. It is important not to confuse machine learning, neural networks and artificial intelligence, these are fundamentally different things. Artificial intelligence can be trained not only by machine learning methods, but in addition to neural network algorithms, there is also classical learning and reinforcement learning.
Machine learning techniques
What is machine learning?
It is divided into three main types:
- With a teacher (Supervised machine learning).
- Without a teacher (Unsupervised machine learning).
- Deep learning.
Let's take a closer look at each of the methods and their fundamental differences. Nowadays in machine learning consulting are used such variants.
Supervised machine learning
For convenience, we will consider this method using a conditional example of analyzing aptitude for certain subjects - the program will enter data about students and what results they achieve. The teacher is a person who drives data into a computer.
Unsupervised machine learning
At the beginning of the article there was a video about how AI learned to walk. This program received an assignment from the developer - to get to point B. But she did not know how to do it - she was not even shown what walking looks like, but this did not prevent the AI from completing the task. Therefore, learning from games with Applandeo is one of the most effective methods of machine learning. Here's a simpler example - the program gets data about how far away some objects are from it, and can choose how best to move in the Snake game in order to get more points.
Deep learning
Deep learning can be with or without a teacher, but it implies the analysis of Big Data - such a large amount of information that one computer will not be enough. Therefore, Deep Learning uses neural networks to operate. Neural networks allow you to divide one large task into several small ones and delegate them to other devices. For example, one processor collects information and transmits it to two others. Those, in turn, analyze it and pass it on to four more, who perform some more tasks and pass it on to the next processors.
To sum up
Machine learning refers to a variety of mathematical, statistical and computational methods for developing algorithms that can solve a problem not in a direct way, but based on finding patterns in a variety of input data.
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‘AI is a vital part of the fourth industrial revolution and will impact every aspect of people’s life’- Fei Fei Li, Professor at Stanford University.Artificial Intelligence has been one of the most reformative technological inventions in the history of humankind as it has made information systems more adaptive for humans.
Over the years, AI has played a definitive role in redefining different industry sectors & sections and the insurance sector in this context has a similar story as well.We need no superlative analysis to understand the importance of medical insurance, especially in the present turbulent times of the global pandemic.
Artificial Intelligence (AI) and Machine Learning (ML) have proved to be a vital cog that has streamlined end-to-end claim settlement, thereby increasing customer satisfaction.
The mediclaim management process can now be done faster and better with fewer or no errors.Gartner, in 2016, predicted that almost 1/3rd of the companies on a global level would use AI in at least one aspect of their sales process by 2020.
Moreover, there have been cases where the mediclaim insurance companies have instigated investigative operations to find the genuineness of the claim done by the policyholder.
Through in-depth data analytics, insurers can automate the process of fraud detection.It helps them to pre-assess the claims while simultaneously automating the damage evaluation process.After understanding the changes in brief that Artificial Intelligence (AI) and Machine Learning (ML) has brought in the insurance sector, we now dig deep to see the role these two techniques play.Role of AI and ML in the health insurance sectorThe expansion of AI and ML in different industry sectors, especially in the last decade or so has opened new avenues for the insurance industry and has allowed the insurers to provide a better customer experience.
