What are the applications of machine learning in insurance?

Customer relations

For several years now, insurance companies have used intelligent conversational agents, also called “chatbots”. These robots, based on artificial intelligence and machine learning, proved that they brought A real capital gain in terms of customer experience.

Easy to use and available 24 hours a day, chatbots are able to automatically respond to customer requests, support them in certain steps and provide them with advice. A real time saving, both for insurers and for consumers.

In addition, contrary to what one might think, conversational agents do not harm human interactions. Indeed, they allow collaborators to free themselves from the most forbidden tasks for focus on advice and support missions with high added value.

Finally, the machine learning is distinguished by its predictive dimension, which makes it possible to anticipate the change of status of an insured in order to offer him an adequate service. Thus, the company may know in advance that a customer is preparing to change insurance or that he has an unposed new insurance need.

Detection of fraud

Each year, fraud costs several billion euros to insurance companies. A shortfall that is possible to limit thanks to the machine learning.

Indeed, this technology is able to automatically detect anomalies in the description of an incidentof the situations that are not covered by an insurance contract or even overestimated financial losses. What precisely score the declarations in order to direct manual treatments for repression of fraud.

There are, for example, fraud detection systems based on scenarios. The data and comments of the insured are combined and analyzed to identify the fraud, then sorted according to their Modus Operandi. Finally, each alert is classified according to its relevance, according to a pre -established rating system. This type of system is particularly effective and is synonymous with high added value for insurers.

Risk management

Machine learning makes it possible to analyze future trends more finely, and therefore Better control the risks. To do this, we study a multitude of data from large to the open goal, with the aim of predicting new profiles and emerging risks. With this approach, insurers can identify priority axes and design insurance products that meet the needs and expectations of customers.

In addition, new data sources, such as connected objects and social networks, make it possible to build an exhaustive mapping of the risks to which users are exposed. Thanks to the automatic data processing and analysis, insurance companies are able to offer ever more personalized offers. In addition, anticipating risks is an excellent way to prevent rather than cure, by launching targeted prevention actions with the insured.

In automotive insurance, for example, the machine learning makes it possible to adapt the offer according to the driving mode and the behavior of the insured, thanks to a connected case installed in the vehicle. This latter collects a lot of information related to braking, mileage, speed or acceleration. This data is then treated to draw predictive analyzes.

Contract subscription

The stage of subscribing an insurance contract can be a real obstacle for customers, still too often faced with endless forms. To solve the problem, insurers need a simple and fast solution, suitable for consumer expectations in a digital world.

Once again, new technologies provide precious help. Indeed, the centralization of customer data makes it possible to avoid the procedure for collecting information through a form. Better still, artificial intelligence is responsible for writing a personalized insurance policy based on data. The insured can thus Subscribe a contract in a few minuteswithout any intervention (or almost).

The declaration of claim

With machine learning, it is possible tocompletely or partially automate claims declarationswhich considerably reduces the period of acceptance of insurers.

In parallel, this technology allows a Better qualification of claims : Something to obtain rapid expertise, without necessarily using specialized experts. Finally, insurance companies save precious time thanks to the automatic evaluation of damage and the prediction of repair costs.

Pricing

The ability to process large volumes of data allows insurers to get to know their customers better, but also to better segment them. Therefore, they can offer insured prices perfectly suited to their profile and the risks encountered.