Artificial Intelligence And Its Role Towards Insurance Technology

As we move on, the topic of Insurtech is raising a growing interest. This is primarily because of the vast size and importance of the insurance market, however, this can also be attributed to the promising new opportunities offered by new technologies, just like Artificial Intelligence.The applications are very diverse and players in the Insurtech space can be roughly divided into different categories. A unifying trait, however, is that many of these Insurtechs have the common approach to tackling their problems by leveraging data and Artificial Intelligence (AI).

Insurance firms have always been very skilled and efficient IT organizations compared to different industries and data has continuously played a significant role. Its analysis, however, often happens retrospectively by aggregating historical data and descriptive analysis of the same, e.g. from past claims incidents.

The spread of sensing element technology, for example, provides the chance to better understand the client in damage-free conditions and therefore to be more proactive, which will have enormous implications for future insurance products. Especially in this context, we have a tendency to believe that AI can create the most important changes within the business in the coming years.

What actually is AI?
In the field of AI or Artificial Intelligence, it’s a kind of making an attempt to develop machine-driven systems that emulate human intelligence, or, in different words, perform tasks that in our understanding require some form of intelligence. These include e.g. tasks of perception, natural language processing (NLP), pattern recognition, inference, but also knowledge representation and robotics.

Artificial Intelligence could be a technology that has already been horizontally incorporated into several areas of our everyday lives, like virtual personal assistants, (semi-) autonomous cars, spam filters, and referral services, but also into many very traditional industries, e.g. the steel industry.
Since the solutions to most issues tackled by AI systems are too complicated to be “manually” outlined, we tend to use refined techniques to automatically learn these from the data. While one sheet of paper is enough to write down the logic rules of the game of chess so as to calculate the potential game outcomes of a move, this is no longer attainable with a lot of complicated games. The rules do not necessarily have to be more complex, but the possible game situations are no longer calculable due to their sheer number. This is where machine learning (ML) techniques are used to automatically extract rules and patterns from the underlying data.

Why now?
Machine learning and AI are no new fields of analysis. Neural networks, which are the basis of deep-learning techniques that are very prominently represented in the press, are not new either. There have usually been breakthroughs within the application of AI, every time causing publicity, followed by disappointment and a so-called “AI-winter”. Why is it different now? Today’s successes are made possible by 3 basic factors that may not disappear so quickly, and will even become more important:

  1. Advances in AI Research: because of its ambitious goals and rigorous nature, the field of AI has always attracted many researchers since its creation in the past. From various perspectives, with different interests and motives, researchers of AI and its contained/adjacent areas have made massive advances in AI research and applications in various domains in recent decades.
  2. Massive computing capacity within the cloud, available to us at any time. While in the beginning algorithms had to be trained on individual machines, we developed ways for parallel processing of the machine instructions with connected computers and several parallel processors (CPUs), up to powerful graphics cards, with hundreds or thousands of processors operating in parallel (GPUs ). With the availability of high-performance systems in the cloud on-demand and scalable as required, there is virtually no barrier of entry for using computation-intensive applications.
  3. Data, data, and more data. Many AI applications have only been facilitated by the big amounts of information available to us today, be it unstructured data, such as text documents, images, and videos, or structured data that is predefined and machine-readable. These might stem from services that give data freely or proprietary sources (e.g. weather data, crime statistics, etc.), data from various platforms and social media (Youtube, Facebook, LinkedIn) or through our digital footprint on the web. An ever-growing factor that is of high importance to the insurance industry is data produced by sensors and the Internet of Things (IoT).

What does that mean for insurers / insurtechs?
In the past, insurance data was only available internally. A sizeable policy pool constituted a competitive advantage that required protecting. Today the advantage can progressively be gained by the mixture of internal and external data sources. In doing so, the amount of data can be increased or the data can be enriched to include additional information.

The unique position given by the static data collected and kept in-house will presumably hardly play a role for insurers in 3 to 5 years from now or at least not provide any real competitive advantage. The data is consistently changing so that it needs to be recoverable and processable in real-time. From here it’s only a small step moving far from the reactive business and towards predictive models, particularly for applications like damage prevention.

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  • Get discounts the moment they are live
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  • We are based in Singapore
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