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 7puentes.com 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.

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