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The Ways Machine Learning Companies Can Redefine Insurance

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Most insurance companies tend to process only a small part of their data — around 10 to 15%. The rest of the data in their databases are not being processed adequately, meaning that they are probably missing on insights in the data they keep but never analyze.

However, in order to analyze the unstructured data that will help you bring on better business decisions and prevent intruder attacks, the use of advanced technology is needed. Machine learning comes to the scene here because it is able to analyze lots of structured, semi-structured, or completely unstructured data the insurance companies tend to store in their databases.

The benefits of machine learning are numerous:

● Understanding risk

● Understanding premium leakage

● Managing expenses

● Subrogation

● Litigation

● Fraud detection

Since insurance companies deal with a lot of sensitive data and assets, they need to have an efficient way of finding any fraudulent activities and preventing them. This will increase their trustworthiness in the eyes of current and potential clients.

Stick with us while we explain the possible challenges when it comes to machine learning before we jump to explaining how machine learning companies can be of use for insurance services providers.

Challenges of Applying Machine Learning

Just like any other new thing you are trying to apply and implement for the first time, machine learning also brings some specific challenges. The most important ones are listed and explained down below.

Every system needs to be trained and fed with data that stimulate and support various scenarios. But since it is impossible to cover every single scenario, it leads to the system having certain unavoidable loopholes.

For example, if the insurers are looking for an AI-powered system to implement in billing, it will require them to have a separate training system. This is where the issue comes up — you need to provide the aforementioned data in order to train the AI system, and sometimes that is not physically possible.

Data sources

In machine learning, the quantity of data you provide will play a great role in training the AI system. The more data you feed into the system, the better predictive models can be created. However, let’s not disregard the fact that not only the quantity but quality of data is also very important.

If you feed the system with bad data, the predictive models will not be of any value. The sources of the data need to be representative and relevant, to avoid any bias in the future.

One of the biggest challenges with machine learning is that it can be very hard to predict and calculate the expected ROI (return on investment). This happens because machine learning is a continuous process, so if you dig up some findings at the early stage of the project and calculate the budget you’ll need, this may not be relevant at later stages of the project.

This is because there might be some new findings in the process that will request additional funding. These new findings may influence the ROI.

Pros of Machine Learning

After explaining the potential challenges when it comes to machine learning, it is time to explain the pros of applying machine learning in insurance processes. Here are some of the areas where machine learning is being used in insurance:

Lapse management – Machine learning plays a great role in finding out what policies in insurance are very likely to lapse, so it helps to identify them and find a way to prevent them from lapsing.

Recommendation tool – Machine learning can analyze all the individual insurances and automatically provide the best one for the given situation.

Property analysis – If you are using machine learning in property insurance, you can utilize it to identify the areas that will potentially need maintenance. You can also use AI to schedule any maintenance in the future.

Fraud detection – Probably one of the biggest pros of machine learning and the reason why most insurance companies want to use AI. Fraud detection and prevention play a vital role in insurance due to the fact that insurance companies deal with a lot of personal data.

Personalization – AI can be used to create personalized offers for policyholders. This can improve customers’ experience because the offer will be based on their past history with the insurance provider, so it will be customized to their habits and possibilities.

Prediction – Machine learning can be used for various statistical purposes, like predicting certain types of behavior in the future. You can use it to create models regarding prices, budgeting, risks, etc. The possibilities are really endless.

As you can see, machine learning is used not only for fraud detection and underwriting — there are so many other useful features machine learning is being used for in insurance.

Image Credit: https://unsplash.com/photos/rg1y72eKw6o

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Source: https://datafloq.com/read/the-ways-machine-learning-companies-can-redefine-insurance/17967

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