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The Benefits of Transitioning from Statistical Models to Machine Learning

In case you missed last week’s article, we talked about the many ways machine learning is used in the insurance industry, and how it not only adds value to their work. This week, we shed light on the benefits of transitioning from statistical models to machine learning.

In regulated markets, insurers rely heavily on statistical models, including methods like Generalized Linear Model (GLM) which is an advanced form of linear regression modelling-to measure the relationship between the characteristics of an insurance policy and the expected losses from that policy.

To compare, statistical methods mainly comprise of mathematical models trained and derived from historical data to generate loss predictions for prospective new policies and prices the business accordingly: whereas machine learning starts with the best way to solve the prediction problem.


Let’s look at how machine learning is the future of prediction curation:

a) It was made for wider data sets: Analysts seek to enrich predictive models with new data, so data sets are getting “wider” –there are more variables in the data that may have a relationship with the issue at hand. Ten years ago, insurance analysts rarely worked with more than a hundred variables. But today, many analysts routinely sift through thousands.

b) It maximises extraction: Sometimes, there’s too little data. For applications like submission prioritization, price quotes, and litigation prediction, minimal data is available. Machine learning extracts maximum predictive power from thin data.

c) It helps you keep up with the ‘now’: Several converging trends contribute to the current surge of interest in machine learning, such as the radically declining cost of computing. Today, computing is not only more accessible and powerful, but it’s also more affordable – allowing scientists, researchers, and practitioners introduce new algorithms at a dizzying pace.

d) It is always improving: Each new algorithm is a little better than its predecessors, but the cadence of innovation is so rapid that the cumulative impact is radical. In some fields, we see tenfold improvements in speed and scalability every six to twelve months.

e) It adapts to your business: With cheap computing and constant innovation, machine learning practitioners accumulate knowledge and experience. They are better able to meet business requirements because they know how to configure and tune algorithms to produce the best possible predictions.


Accelteam has been actively working with customers from various industry segments driving business improvements using machine learning solutions like Datarobot. Be it a customer with or without data scientists in their organisation, Accelteam has the right solution to meet the customer’s expectations in their machine learning and AI quest.

In fact for the past two decades, AccelTeam has been one of the leaders in providing data-driven analytics solution in this region. If you would like to know learn more about DataRobot and machine learning solutions, please reach out to us at