With big data and data analytics, diseases are diagnosed quickly and accurately. Previously, doctors examined patients and tried to recognize the disease by comparing symptoms with a list of conditions they knew, studying the literature, and consulting with colleagues.

But big data offers a more advanced way to diagnose patients. Patient data entered suggests an algorithm with the most likely diagnoses. The algorithm can also select relevant tests. Computer vision is also widely used in diagnostics. 

As a rule, this method of analysis is used for the primary information classification. Descriptive analysis is among the most used types of big data analytics. That is, analyzing large arrays of heterogeneous information.

The result of the descriptive analysis is the preliminary separation of data into subsets. It begins with a clarification of exactly what to deal with – the result is information about the quantity or volume of something

Big Data has already found plenty of applications in insurance. From fraud detection to better personalization, insurtech companies are heading towards the future and proving that Big Data will soon change this industry forever. 

The concept of Big Data implies that there is an endless, infinite stream of data that is accessed to get useful information using limited resources. This means that a significant part of useful information is not used at all, but simply lost.

However, the growing interest of insurance companies towards further implementation of Big Data shows that the demand for it will only grow in the upcoming future.

A smart and productive business always strives for a deeper customer engagement – analyzing consumer behavior and supply chains, personalizing their ecommerce store for a better experience, with purposeful promotions and improved conversion rates. It is also one of the foundations of any startup’s success.

Such resources can only be acquired by employing big data in business – for better customer communication and understanding, more effective promotion and competitive advantage.

The basic principle of machine learning applications is the adaptive automated generation of problem-solving models. Based on experience in solving similar tasks, the program must “learn” to generate algorithms that would allow obtaining correct results. To put this in place, a developer must have knowledge of mathematical statistics, numerical methods, optimization methods, probability theory, graph theory, as well as other complex data manipulation techniques.

AI in finance offers numerous benefits. Perhaps, the main advantage of AI is that it gives countless automation opportunities. In turn, automation can help financial organizations increase the productivity and efficiency of many processes. Besides, given that AI can replace humans in certain situations, it helps eliminate human biases and various errors caused by emotional or psychological factors.

Obviously, AI is also better at analyzing data. Machine learning enables computers to identify patterns in data, providing decision-makers with valuable insights, and helping organizations get more precise reports.