Future Of Insurance: Artificial Intelligence And Ecosystems

Digitization is currently experiencing an unprecedented boom. A fundamental digital transformation is about more than just providing technical infrastructure and supporting traditional processes. All possibilities should be used that the new technologies, such as artificial intelligence (AI), robotics and big data, have available. This results in entirely new strategies, business models, value chains and ecosystems.

Sustainable Digital Future

With the dynamic development of digitization and networking, established business models and value creation processes are increasingly under pressure to change. Digital ecosystems are created through the data-based networking of companies across industry boundaries and value chains. With new data-driven platforms, more sales power and seamless customer interaction and better analyses and predictions can be achieved. The key innovation, artificial intelligence, takes systems to the next level.

In the insurance industry, too, it is now undisputed: the sustainable future is digital. The power of digital transformation follows a simple rule of three:

  1. Everything that can be measured will be measured.
  2. Everything that is measured can be analyzed and forecast.
  3. Everything that can be forecast can be improved and digitized.

Artificial intelligence stands for the most significant human evolutionary step to date and the replication of human intelligence and decision-making structures:

  • Independent solution of known and unknown problems through learning
  • Inclusion of empirical knowledge, weighing of information and uncertainty

Artificial intelligence consists of self-learning algorithms and can handle unstructured data autonomously and utilize the findings.

Also Read- Healthcare: How Companies Ensure Their Cybersecurity During Rapid Digitalization

Intelligence, Performance, Quality And Customer Focus

More intelligence, more performance, more service, more efficiency, and new culture through robotics and AI systems offer great opportunities and significantly impact companies’ future success. The increasingly powerful and intelligent systems will therefore revolutionize products, processes and interactions along the value chain. Cognitive algorithms store all data and the robots, which learn from them using machine learning or deep learning, integrate seamlessly into the process and interaction landscape. Therefore, intelligent systems should be part of every future and technology strategy and be optimally promoted.

Company In The AI ​​Change Process

The primary innovation AI is developing rapidly and revolutionizing society and the economy over the next few years. Companies are key players in the change process for the community as a whole and, as contacts and bearers of the relevant responsibility, should take their stakeholders with them on a journey into the AI ​​future that is yet to be created to make theirs business models, services and products sustainably successful through efficient and risk-adjusted processes.

Machine learning has developed considerably in recent years thanks to big data and advanced algorithms. Algorithms create significant added value and are an essential economic asset. The automation of decisions and ethical standards, the quality and protection of data, quality assurance of AI-supported decisions and the value-adding interaction between man and machine are exciting topics.

For people as colleagues of an AI, there are specific new roles, such as AI manager, AI controller, AI trainer and AI specialist, as many activities are certainly no longer in demand on the job market. In the new future AI working world, people will increasingly concentrate on activities related to the solution of problems and the creative development of innovations. However, the greatest challenge for organizations does not lie in the ecological and only indirectly in the social dimension, but in the readjustment of the decision-making processes and routines and the associated information and communication.

To secure their future viability in the long term, it is essential for insurance companies to actively help shape these future technologies and to mobilize the existing AI potential. This means that they are setting the course for the future of AI, creating supportive framework conditions and a vital ecosystem.

Investment Boost And Cooperation

A surge in investment in education, research and development in Germany, especially in AI, is essential. Partnerships and collaborations in science can, for example, be established regionally via an endowed professor together with partners, and related projects can be initiated and funded. In this way, knowledge transfer from science to business practice can occur in a targeted manner and vice versa.

The Bavarian AI network and program as part of the Hightech Agenda Bavaria (HTA) based on the AI ​​centre in Munich convinced the Coburg University of Applied Sciences, among others, with two of their submitted concepts. There is a professorship for “Explainable and Responsible Artificial Intelligence in Insurance” in insurance research and teaching. This is a great success for the university and the science location. Because the new professorship in insurance science can also significantly strengthen the insurance location. There is also great future potential for insurance companies and the business location in cooperation with the HessianAI and the corresponding institution of future-oriented and practice-oriented research areas, which must be exploited.

Sustainable Areas Of Application In The Insurance Industry

In insurance companies, AI can be used along the entire value chain:

  • Corporate management and corporate development
  • Product and service design, underwriting and risk assessment
  • Customer experience, insurance, and ecosystem
  • Policy, claims and customer service management

The use of AI can offer the following advantages, for example:

  1. Data structuring: AI algorithms can classify data, for example, when extracting information from documents, for instance, from letters. These can then be saved in the system again and used for other purposes.
  2. Make complex decisions: Intelligent systems can solve complex problems that are almost impossible for humans. Using modern machine learning and deep learning systems, customer risks can be calculated based on millions of parameters or cases of fraud through irregularities or conspicuous patterns can be detected. Once the knowledge has been learned, it can be transferred to other systems and used regardless of time and place.
  3. Human-machine interaction and knowledge management: Processes in insurance companies often require a high level of customer interaction and specialist knowledge. The use of AI as a ChatBot, VoiceBot or voice assistant in the service centre or when processing customer concerns or complaints supports more efficient customer communication and optimized resources. Resources can be used for other activities.
  4. AI algorithms and Robotic Process Automation (RPA): The synergetic combination of RPA technology and AI is apparent. Because RPA enables fast process processing with high process quality, AI systems perform tasks that allow interaction between humans and RPA without media discontinuity. This combination is, therefore, a central key for insurance companies. An RPA can forward an incoming damage report to the damaged area, and, in addition to the OCR engine, an AI can recognize the language and the context of the damage process. Another AI agent can also evaluate the attached images and validate the loss amount and the extracted loss event. The “RPA-RoBot” takes over the other claims process up to the completion of payment.

The development of sustainable AI, RPA and data strategies requires appropriate economic and technical expert know-how. For example, AI is about learning based on the decisions of the technical experts, the further development of the AI ​​algorithms through structured and unstructured data, and the interaction between humans and machines.

Insurance companies should be aware of the objectives underlying the use of the respective technologies and data models to set up sustainable, successful strategies, set up roadmaps and provide reasonable budgets and resources for the projects.

Data and analysis methods are essential for implementing strategic goals of the transformation of the insurance industry and form the basis for the next level of digitization and data-driven insurance. A particular focus should be placed on the organization and operation to get the maximum potential from the respective technology and be in harmony with the corporate strategy. The organizational and procedural framework conditions are probably even more important here than the technical platform.

It has been shown that the organization and the people are decisive for the excellent development of an AI-supported and data-driven insurance company. The wisdom that the founder of modern management Peter Drucker once formulated applies here: “Corporate culture eats strategy for breakfast.” What applies to strategy in general also applies to technological strategy, initiatives and programs: only if the organizational framework is correct, only if the employees are taken along, only if they understand the meaning behind technology and are aware of the advantages of using it, then they will be ready to break new ground.

Also Read: Artificial Intelligence At Work For Health

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