The insurance industry is fundamentally based on data analysis and data processing. However, the critical point at which many companies fail is precisely the operationalization of applied models in everyday business.
Core insurance systems are known to play a key role in the day-to-day operations and strategic decision-making of modern insurance companies. With the continuous changes and development of the market, the proper integration of analytics into these systems has become a necessity, considering that they are the ideal channel for its practical application.
1. Incorporation of analytics into insurance business processes
Many insurers have unique and robust models of risk analysis and assessment, which can bring certain challenges, but also failures of their integration into daily processes. This failure often stems from the perception of complexity and required resources for such an integration, so the knowledge of the development team and the team of business analysts is of great importance.
If data analysis is not innovative on the one hand, and on the other hand adopted and available at the right time to those who need them – what is its’ real benefit?
Therefore, the operationalization of analytics in every business process is essential for every insurance company to use the full potential of its data. Failure to use analytics can lead to numerous missed opportunities and suboptimal decision-making, resulting in loss of profit or market share. A successful and thorough integration of analytics can, for example, empower and innovate claims assessment professionals’ tools, improving their ability to assess risk and process claims, but also positively impact sales processes.
In practice, we find numerous examples of incorporating analytics into insurance processes, and one of them is the categorization of the insurance portfolio that we wrote about here.
2. From raw data to informed decisions
To remain competitive, insurers must recognize the strategic importance of data and analytics. A key step in this transition is moving from data collection to efficient analysis and use as useful sources of information.
Modern and meaningful analytics provide insurers and experts with timely and useful information. These insights enable smarter decisions in risk selection, pricing, processing and resolving consumer requests. As a result, insurers can more precisely determine the price of risk, optimize the results of claims processing, improve the loyalty of their clients, as well as their number, but also simultaneously monitor their own performance and results.
A simple example of data visualization at the sales agent level, My Insurance Portfolio:
3. Interoperability
Interoperability in this case represents seamless exchange of data between different systems and platforms for the needs of the insurance company. Without interoperability, even the best analytical models can be underutilized due to isolated data. At the same time, the need for integration far exceeds the limits of the insurance information system and uses the analytics of other related industries.
Achieving interoperability involves solving technical obstacles and adopting standard protocols for better decision-making or service design. The use of APIs and industry-standard data exchange protocols can facilitate this process. By achieving interoperability across domains, insurers can create a cohesive ecosystem where data flows smoothly, supporting better decision-making and operational efficiency.
One of the current trends is better management and assessment of climate risks. Integration of the above can further enrich analytics and insurance products with climate models from multiple sources, as well as the expertise of experts from other industries. You can learn more about this in one of our previous articles here.
Equally important is the ease of connecting the core system with external platforms. To further encourage collaboration, we at InCubis have developed a specialized subsystem – the API Management Center. Using it, we integrate the core system with external services in a safe, transparent and intuitive way, which results in new business opportunities and better use of external data sources.
To conclude:
The comprehensive incorporation of analytics into business processes, the transition from a data collection system to an information system, and ensuring interoperability are key prerequisites for the successful operationalization of analytics by modern insurers.
Successfully responding to these challenges improves operational efficiency and enables insurers to adapt to a dynamic market, offering superior protection and service to their customers. By focusing on this area, insurers can more easily manage the many demands of an increasingly dynamic insurance market, improve client retention and attract new ones more effectively.
