Leveraging AI to Stay Ahead: Designing and Updating Insurance Products

‍Learn how insurance companies can use AI to design new products and update existing ones, meeting customer needs and staying ahead of the competition.

Insurance companies operate in an ever-changing environment influenced by customer needs, economic trends, regulations, and competition. 

Developing new insurance products or updating existing ones is essential for staying relevant. Artificial Intelligence (AI) is transforming how insurers approach this process by offering faster, data-driven insights. 

In this article, we’ll explore how insurance companies can leverage AI for product development and improvement, covering both new product creation and updates to existing offerings.

Using AI to Design New Insurance Products

Developing a new insurance product involves multiple steps, from identifying market opportunities to launching the product. AI can greatly streamline and improve these steps, offering insights, predictions, and efficiency throughout the process. 

Let's explore how AI enhances each step with a fictitious example: a new "CyberShield" cyber insurance policy designed for small businesses.

1. Identify Customer Needs and Market Opportunities

Tasks:

• Conduct market research to understand customer pain points, preferences, and trends.
• Study competitors to identify gaps in the market.
• Analyze regulatory requirements and economic factors influencing customer behavior.

How AI Adds Value: AI excels at analyzing vast amounts of data from various sources—market reports, customer reviews, and social media—to detect trends and customer pain points. AI also tracks competitor offerings and regulatory changes to help insurers identify market gaps.

AI in Action: AI discovers that small businesses in the tech industry are particularly vulnerable to cyber risks and have growing concerns about data security. This insight helps the insurer identify an opportunity to create the "CyberShield" policy—specifically designed for small tech businesses. 

Limitations: AI cannot replace primary research, such as focus groups or in-depth interviews, which are necessary to gather nuanced customer insights. And, human input is still essential in validating these findings.

2. Define Product Objectives

Tasks:

• Determine the target audience (e.g., millennials, small business owners).
• Set goals such as coverage areas, premiums, and profitability margins.
• Outline the problem the policy will solve.

How AI Adds Value: AI uses demographic and behavioral data to help insurers define the target audience more precisely. It can analyze customer data to predict which market segments are most likely to need the product and their preferred policy features.

AI in Action: AI analyzes trends and reveals that small business owners in the tech sector prefer flexible, scalable insurance plans that can grow with their business. AI suggests setting affordable premium rates for startups while including customizable coverage for businesses with higher risk profiles.

Limitations: AI is highly valuable in predicting needs based on historical data but still requires human decision-making to refine the final product objectives and understand emotional or subjective needs.

3. Design the Policy Structure

Tasks:

• Define the coverage details (e.g., inclusions, exclusions, limits).
• Set pricing models and premium rates based on actuarial analysis.
• Decide on policy durations and renewal terms.

How AI Adds Value: AI helps design optimal policy structures by analyzing historical claims data and customer behavior. It can suggest coverage terms, exclusions, and pricing models that are competitive and well-suited to the target audience.

AI in Action: For the "CyberShield" policy, AI suggests offering coverage for data breaches, ransomware, and business interruption due to cyber incidents. The pricing model is adjusted based on the likelihood of a claim, using AI to analyze industry-specific risks and business size.

Limitations: While AI can provide recommendations, human input from actuaries and legal teams is necessary to ensure compliance with regulations and that the policy structure aligns with the insurer’s risk appetite.

4. Perform Risk Assessment

Tasks:

• Use historical data to calculate potential risks.
• Collaborate with actuaries to forecast losses and establish reserves.
• Ensure risk pricing aligns with market competitiveness.

How AI Adds Value: AI analyzes historical claims data, predicts future losses, and assesses risk more efficiently. Its ability to process large datasets ensures faster and more comprehensive risk assessments.

AI in Action: AI uses data from similar cyber insurance policies to assess the likelihood of data breaches for small businesses in the tech industry. Based on this, it adjusts pricing for the "CyberShield" policy to better reflect the actual risks.

Limitations: AI might struggle with rare or unprecedented risks, such as emerging threats that lack sufficient historical data. Human judgment remains critical in these cases.

5. Compliance and Legal Approvals

Tasks:

• Collaborate with legal teams to ensure the policy adheres to insurance regulations.
• Submit the product for approval to regulatory bodies, if required.

How AI Adds Value: AI can help check policy drafts against regulatory databases to flag compliance issues.

AI in Action: Before launching the "CyberShield" policy, AI cross-checks the policy’s terms against cyber insurance regulations in different states to ensure that it meets the minimum required coverage for data privacy and other areas.

Limitations: Legal interpretation of complex regulations still requires human expertise. AI assists with the technical verification but cannot fully replace human judgment in legal negotiations.

6. Pilot Testing and Feedback

Tasks:

• Test the policy in a limited market segment.
• Gather feedback from brokers, agents, and customers to identify areas of improvement.

How AI Adds Value: AI helps analyze data from pilot testing by identifying patterns in customer feedback or claims performance. It can quickly spot which features customers find confusing or which claims have the highest frequency.

AI in Action: AI analyzes early feedback on the "CyberShield" policy and detects that customers are particularly concerned with the language used in exclusions. It highlights these areas for further clarification before full-scale launch.

Limitations: Gathering qualitative feedback still requires human interaction, such as direct conversations with brokers or agents. AI is best used for analyzing results and feedback trends.

7. Launch and Marketing

Tasks:

• Develop a marketing strategy targeting the intended audience.
• Train brokers and agents on the new policy.
• Use digital platforms to promote the product.

How AI Adds Value: AI optimizes marketing campaigns by analyzing customer data and recommending the best channels, messages, and timing.

AI in Action: AI helps the marketing team of the "CyberShield" policy target small business owners on LinkedIn with tailored ads. It identifies that LinkedIn is the best platform for reaching this audience and optimizes ad timing and content for maximum engagement.

Limitations: While AI provides valuable insights, the creativity behind compelling marketing campaigns still relies on human input, as well as the personal touch needed in training agents and brokers about the new product.

8. Monitor Performance

Tasks:

• Track policy adoption rates, claim frequency, and customer feedback.
• Use these insights for future refinements.

How AI Adds Value: AI continuously tracks key performance indicators and identifies trends in real time. For example, it can detect a spike in claims for a particular coverage area, suggesting the need for adjustments. AI can also analyze customer feedback to pinpoint dissatisfaction, such as lengthy claim processing times.

AI in Action: AI detects a rise in claims related to data breach incidents in the "CyberShield" policy, indicating that the policy might need to increase coverage for such incidents. It alerts the product team to review the policy’s terms.

Limitations: AI cannot independently decide what refinements to make. Human oversight is needed to interpret the data and take appropriate actions.

By following these steps and leveraging AI, insurance companies can create better products faster and with more precision, responding to customer needs and market dynamics with efficiency and insight.

"AI is not just a tool; it's a game-changer for insurance companies looking to stay ahead. By helping us understand customer needs faster and refine our products more efficiently, AI allows us to deliver policies that truly resonate with people. It's about combining data-driven insights with a human touch to create insurance solutions that matter."
‍— Himanshu Gupta, Chief Operating Officer (COO), iorta

Using AI to Update Existing Insurance Products

1. Review Policy Performance

Tasks:

• Analyze customer feedback, claim trends, and complaints to identify issues.
• Check if competitors have introduced better features or pricing.
• Evaluate changes in regulations or market conditions.

How AI Adds Value: AI analyzes vast amounts of feedback and claims data to identify trends, such as frequent complaints about specific exclusions. It also tracks competitor pricing and features to highlight areas where the policy lags. For example, AI might detect a surge in customer dissatisfaction due to high claim rejection rates tied to outdated exclusions in a health insurance policy.

Limitations: AI may not be able to fully interpret qualitative nuances from customer complaints, such as emotional factors influencing dissatisfaction.

2. Identify Areas for Improvement

Tasks:

• Add new features (e.g., wellness benefits in a health plan).
• Modify exclusions or adjust coverage limits to meet current needs.
• Revise premiums to reflect updated risk models.

How AI Adds Value: AI can simulate the impact of adding new features or revising premiums by analyzing historical data and predicting customer behavior.

Limitations: AI cannot finalize which features align best with the company's strategy or customer relationship goals. These decisions require human oversight.

3. Consult Stakeholders

Tasks:

• Engage with brokers, agents, and customers to validate proposed updates.
• Discuss with legal and compliance teams to ensure changes meet regulatory requirements.

How AI Adds Value: AI can summarize stakeholder feedback from surveys or focus groups, making it easier to identify consensus on proposed updates. It also flags potential compliance risks by cross-referencing regulatory databases.

Limitations: Building trust and gathering qualitative input require human interaction. AI cannot replace the nuanced discussions with stakeholders or interpret their emotions and intentions.

4. Test Changes

Tasks:

• Roll out updates to a small customer segment to gauge acceptance.
• Monitor claim ratios and overall customer satisfaction for the updated plan.

How AI Adds Value: AI accelerates analysis during pilot testing by quickly processing feedback and claim ratios.

Limitations: AI relies on data volume and may struggle with limited pilot test data, making it less effective for smaller test groups.

5. Implement and Communicate Updates

Tasks:

• Fully integrate changes into the policy offerings.
• Inform existing policyholders of the updates and how they impact them.
• Update marketing materials and train distribution channels.

How AI Adds Value: AI personalizes communication by tailoring messages based on customer profiles. For example, it can generate email campaigns explaining updates in a way that resonates with different demographic groups. AI also optimizes marketing strategies by identifying the most effective channels for outreach.

Limitations: AI cannot always convey the emotional reassurance often needed in communication about policy changes. Human input remains essential for sensitive messaging.

6. Monitor the Revised Policy

Tasks:

• Continuously track the policy’s performance post-update.
• Assess whether the changes improved customer satisfaction, claim ratios, or profitability.

How AI Adds Value: AI provides real-time tracking of metrics like adoption rates and claim ratios.

Limitations: AI cannot interpret broader implications of performance metrics, such as reputational impact or long-term customer trust, which require human analysis.

Final Thoughts

AI is transforming how insurance companies develop and update their products. It streamlines market research, analyzes customer behavior, and provides actionable insights that allow insurers to respond quickly to changing demands. 

While humans remain essential for conducting primary research and bringing creativity to product design, AI complements these efforts by offering efficiency and precision. 

By embracing AI and combining it with human expertise, insurers can stay ahead in a competitive industry while better meeting their customers’ needs.

How We Can Help:

Ready to integrate AI into your insurance operations? At iorta, we bring the expertise to guide you toward smart, sustainable, and future-ready AI solutions tailored to your business. Let’s explore the possibilities—email us at hello@iorta.in to schedule a call!

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