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The insurance industry is undergoing an unprecedented revolution, and the engine behind this change is Artificial Intelligence (AI). In this era of transformation, Helvetia, one of the leading insurance companies, is at the forefront of innovation in the insurance sector. The company promotes cutting-edge initiatives encompassing the insurance spectrum, from claims settlement to optimizing decision-making processes and implementing conversational interfaces to enhance sales.  

A Unique Approach to AI Adoption  

Helvetia has adopted a unique approach to AI adoption, recognizing the speed at which this technology progresses and diversifies. Instead of focusing exclusively on the development of specific technical skills, the company has outlined a clear strategy:  

• Maturity Requirements: Helvetia has adopted a maturity model to establish general requirements for AI adoption within the company.  
• Governance and Quality Control Principles: It has implemented governance and quality control principles to ensure the integrity and effectiveness of AI throughout the organization.  
• Agile High-Return Initiatives: The company promotes AI adoption through practical, agile, and high-return-on-investment initiatives, with clear expectations for results.  
• Adaptation of Capabilities: Helvetia has chosen to adapt project management, project delivery, and project governance capabilities, both in IT and in business, to address AI challenges.  
• Initiative Classification Scheme: AI-related initiatives are defined practically and executively with a predefined classification scheme.  
• Cybersecurity: The company is adjusting cybersecurity mechanisms and processes to address the development of AI technologies.  

AI Use Cases for Maximizing Value  

One of the keys to maximizing the potential of AI is the formulation of specific use cases. Helvetia has mapped AI use cases and classified them based on two main attributes:  

• Type of Outcome: This attribute distinguishes whether a use case expected result is known in advance. For example, using AI for content comprehension falls into the first case, while methods like churn prediction or the creation of new products are examples of results not known in advance.  
• Impact Type: It distinguishes how AI application positively impacts the company. It is referred to as "bottom-line" impact if it reduces costs or "top-line" impact if it increases revenues.  

Based on these two dimensions, Helvetia has labelled AI-related initiatives into four categories:  

• Operational Effectiveness: Use cases with results known in advance and an impact on the top-line.  
• Market Effectiveness: Results are known in advance and affect the top-line.  
• Operational Engineering: Results should be known in advance and impact the bottom-line.  
• Market Engineering: Results are not known in advance and influence the top-line.  

Three Automation Pillars at Helvetia

Helvetia has focused on three main AI-based automation pillars:  

• Internal Process Automation: AI has automated internal processes such as claims settlement and life policy transfer. Recognizing and extracting information directly from documents is crucial in this process.  
• Decision Process Automation: Helvetia has implemented AI to automate decision-making processes related to claims settlement, enabling prompt payments.  
• Conversational Interfaces: The company has leveraged AI to create intelligent conversational interfaces that support sales processes. These virtual assistants provide interactive assistance to customers.  

Automation of the Legal Dispute Management Process

One of the most complex and interesting cases was the automation of the legal dispute management process. This process requires a deep understanding of incoming documentation, which is varied and detailed.  

In the above context, AI was used to:  

• Understand and categorize incoming legal documents into different categories.
• Perform constrained summarisation (see summarisation blog) to give the claim adjuster a brief and concise view of the claimants.
• Extract specific information from each type of document.  
• Open or update legal cases in IT systems, mapping the extracted data.  

The results were astounding, significantly reducing human working hours and AI accuracy exceeding 95%.  

Expanding Fast-Track Adjustment

Helvetia has extended fast-track adjustment by using AI to identify "simple" claims that can be settled at the appraisal value without involving human adjusters, leading to significant time and resource savings.  

Helvetia's approach to AI is proving successful, leading to increased operational efficiency, significant savings, and improved cybersecurity.  

It demonstrates the transformative potential of AI in the insurance industry.  

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