Inteligencia Artificial Sector Asegurador

AI in the insurance sector

Artificial intelligence in the Insurance Sector

Each insurer has its own process for detecting bill anomalies. From a team of professionals dedicated to it, to logical and automatic rules that identify possible errors or cost overruns in the documents themselves. This is why artificial intelligence in the insurance sector comes into play.

The incorporation of artificial intelligence (AI) and machine learning will streamline all these processes, automatically identifying any fraud or error.

The digital transition is essential. So is incorporating your new tools.


AI-enabled cloud platforms replace on-premises solutions


Big data analytics are increasingly becoming the standard tool. For example, when assessing risks, analyzing damage or segmenting customers, the analysis of large amounts of data has decisive advantages here.

Not only is it crucial that all relevant data is accessible in all locations but, in addition, due to the increasing volumes of data, it is necessary to organize and provide it in an optimal way.

AI solutions are playing an increasingly important role in this. Cloud services apply AI and the use of machine learning (to prepare data) to measure the performance and competitiveness of companies in the future.


Artificial Intelligence in the insurance sector: simplified underwriting with predictive analytics


A time-consuming and consequently expensive job when buying insurance is an accurate risk assessment. It requires very detailed information, which traditionally has to be collected with extensive questionnaires.

The case of health insurance, for example, it is necessary to collect the history of previous illnesses, carry out medical examinations and, in some cases, evaluate laboratory tests.

In order to proceed more efficiently here, it pays to rank customers.

At the future, low-risk customers will be identified with predictive algorithms based on extensive profile and behavioral data. In this way, a simplified risk assessment process can be offered to these customers.

This measure optimizes both the customer experience and internal processes.


Claims management based on artificial intelligence in the insurance sector


Another complex process in the insurance industry is the evaluation of insurance claims, which requires a lot of information and documents from heterogeneous clients. This is largely analog data.

This poses a dual challenge to the insurers:

  • Optimize information analysis
  • Digitization process

A solution to this problem is the use of AI, or to be precise, the use of machine learning. Recognition and learning algorithms help insurance companies classify document types and identify particularly important sections.

Digital image processing and text recognition algorithms also open up great potential for automation when it comes to evaluating analog documents. AI-backed claims management leads to greater claims handling efficiency.


Clustering customers for optimal customer focus


Another trend is customer clustering. The goal is to better understand the available data and identify those groups that are especially relevant.

To achieve this, techniques called unsupervised machine learning are used. An algorithm recognizes similarities in large data sets without being given specific target values from the outside, as is the case with demand forecasting.

A combination of existing data and external data is used, in which the similarities are recognized and grouped.

The results of this process lead to customer segmentation that can be used to optimally address the respective customer group.


Artificial Intelligence: An opportunity for the insurance sector


The trends for the insurance industry are intended to reflect the broad spectrum in which massive changes are already taking place and will continue into the future.

Digitization and the development of new digital technologies represent a great opportunity for insurance companies.

This translates into advantages for the insurance companies themselves, but also for their clients: risks can be calculated better than ever and the prices of data-driven products can be adjusted accordingly.


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