AI And Machine Learning: Is This The Future Of InsurTech?


It would be a truism to say, that the digital transformation in broad terms, will sooner or later change almost every existing industry, and will be an impulse for the birth of new ones. What is of much more importance is the knowledge allowing to identify which branches of industry will be affected by this multi-dimensional and quite unpredictable phenomenon in the near future. Without a doubt, the insurance industry is among them.

Digitally processed insurance

The aforementioned digital transformation has even spawned its own terminology, with fintech receiving much popularity worldwide, proptech being spoken of more often, or the digitally transformed real estate market. Therefore, it is worth asking a question about what makes insurtech stand out in comparison to the traditional insurance market. Surely, already one may say, that one of the distinctive features is the use of advanced algorithms based on machine learning, which is currently generalised and reduced to the concept of artificial intelligence.

Big data processing for insurtech purposes

It should not be a surprise, that the starting point for the use of the so-called artificial intelligence in the insurance industry, and in result its efficient transformation into insurtech, should be the digitalisation of data. It is the large data sets processing, that allow the advanced algorithms to specialise and be increasingly efficient in their analysis from cycle to cycle, also in risk analysis. The issue poses one of challenges for insurance companies in confrontation with insurtech start-ups. 

At times, the former posses decades of experience, however their bases often lack the structure that would allow them to be processed by deep learning algorithms. However, start-ups, having increasing data sets in an appropriately structured form, have much more capability to find patterns and regularities by means of artificial intelligence, as their basic premises already include the use of AI on a large scale.

The aforementioned situation yields many results. Among the challenges, always standing before the insurance industry, have always been the scam attempts aiming at filing fraudulent insurance claims. Due to machine learning, it is possible to develop patterns, and with their use, to establish a system of identification of characteristic data that particularly appear in claims, that would turn to be fraudulent afterwards. Obviously, the final decision belongs (for the time being) to a human, however, such warning system may yield enormous benefits.

New products generated by AI

Big data processing and using it to train algorithms, may serve the purpose of designing new products and expanding the insurance offers, with similar success. This way – i.e. by means of a profoundly automated market and consumer needs research – an increasing number of enterprises is operating already. Japanese companies, i.a. Fujitsu and Epson have shown very interesting achievements in this area. The monozukuri production tradition lies within the principles of their production creation philosophy, which currently assumes new, innovative forms.

Production in accordance with the monozukuri principles is primarily directed towards customers’ needs. Owing to machine learning, Fujitsu managed to develop a machine learning framework, which, to a degree – based on the already collected data – is able to forecast these needs. Obviously, the construction of such a complex system requires enormous resoruces, however, one may imagine a situation when, in the nearby future, on the basis of the collected data and by means of artificial intelligence, the needs of customers may be estimated more efficiently, than it is done by humans today.

The final touch: customer support

Whether we like it or not, contact with another person plays an ever decreasing role in terms of service selection. Furthermore, years of malpractices formed a belief among the consumers, that during a direct meeting with a representative of a certain company, the customer is more prone to manipulation, and is under the pressure of time regarding finalisation. Therefore, no wonder, that we are more and more eager to choose applications that allow buying and ordering products and services without the participation of a human salesman, and allow for profound examination of the offer, within a time schedule that is fitting for the customer.

Fintech, as well as, insurtech start-ups are very well aware of this potential. Here, customer support presents a wide area for the implementations of artificial intelligence. Today, chatbots are not some primitive automatons created to respond to predefined questions, but self-learning assistants using cognitive algorithms on a large scale, which are often more trusted by customers than human salesmen. Considering the savings generated by such a solution, already today one may assume that they will be popularised broadly.

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