Machine Learning For Insurance
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Machine Learning For Insurance

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What is Reinsurance

When many insurance companies share risk by obtaining insurance policies from other insurers to limit their overall loss in a disaster, it is known as reinsurance.
Moreover, reinsurance companies, which is referred to as “insurance of insurance companies” do not have too much exposure to a catastrophic disaster.

An insurance firm transfers risks of clients whose coverage would be too much for a single insurance company to bear alone. Where the insurance agency will be reassuring or sharing the risk of a customer with another insurance company who is in the reinsurance business.
The premiums paid by the insured are usually shared by all of the insurance firms involved.

What is Machine learning?

Machine learning (ML ) is the study of teaching machines how to analyze data and make intelligent decisions.

It is seen as a part of artificial intelligence. Machine learning algorithms use sample data to create a model that can make predictions or decisions without being specifically programmed to do it. As a result, machine learning algorithms are used in various applications, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

In its application across business problems, machine learning is also referred to as predictive analytics.

Machine Learning Algorithm Uses for Reinsurance

Linear Regression

If we try to figure out the cost of an asset without looking at the assets value sheet, most probably we might likely look (visually analyze) at the appearance of assets and estimate using a combination of visible parameters, which be correlated to the value and cost of the assets by a relationship. Can be used to estimate real values such as cost of assets, cost of liability, total expenditure etc., based on a continuous variable. We establish a relationship between independent and dependent variables by fitting the best regression line.

Decision Tree

It is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables. In this algorithm, we classify the population into two or more homogeneous sets. This is done based on the most significant attributes/ independent variables.

To split the population into different heterogeneous groups, it uses various techniques; decision trees work similarly by dividing a population into as different groups as possible until reach-out a decision.

It can be used for Proportional Reinsurance, Loss-occurring Coverage, Non-proportional Reinsurance, and to pay the claims accordingly.

KNN (k- Nearest Neighbors)

It can be used for both classification and regression problems, such as Excess-of-Loss Reinsurance, Risk-Attaching Reinsurance. However, it is more widely used in classification problems in the industry. K nearest neighbours is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbours. The case is assigned to the class is most common amongst its K nearest neighbours measured by a distance function.

KNN can easily be mapped to our real lives. If you want to get more details of the disaster to pay the loss, you might find more information by doing some research or collecting evidence about people who are live nearby that disaster or by CCTV cameras. Details of the disaster and the circles’ disaster history, and the present situation gain more information.


It is a form of an unsupervised algorithm that solves the problem of clustering. Its process employs a basic and straightforward method for clustering given data set into a predetermined number of clusters. As a result, to peer groups, data points inside a cluster are homogeneous and heterogeneous which are errors. This is useful for reinsurance since it can easily identify each claim amount assigned by the portfolio.

Final conclusion

You can now observe how machine learning has benefited the insurance business in a variety of ways. From improving customer service to swiftly identifying fraudulent claims, we’ve got you covered.

Machine learning will continue to develop over the coming years allowing businesses to continuously update their policies, resulting in better bargains for customers.

Informatics is a custom software development firm that delivers bespoke insurance software solutions, offering various infrastructure solutions and services to help insurance companies, banks, and other businesses maximize their performance with the help of technology.

We assist insurance and financial service providers in streamlining their processes and providing ongoing support to their customers.

Contact us today for enquiries.


Written by Siththy Waseema