Artificial Intelligence (AI) is transforming the insurance industry by enhancing various processes from acquiring new clients to underwriting policies. Insurtech companies are leveraging AI to improve customer experiences, streamline operations, and manage risk more effectively. Let’s explore some key areas where AI is making a significant impact.

Acquiring New Clients and Product Development

AI-powered analytics can help insurance companies better understand customer needs and preferences, leading to more personalized product offerings.

• Personalized Insurance Suggestions: AI algorithms analyze customer data to suggest the most suitable insurance plans and coverage amounts, ensuring customers receive the best value.
• Predicting Product Success: By analyzing market trends, customer behavior and historical data, AI can forecast the success of new products, enabling companies to make data-driven decisions in product management.

Sale and Distribution

AI simplifies and speed up the sales and distribution process, enhancing customer satisfaction.

• Electronic KYC and Automate Form Filling: Using AI-based technologies like facial recognition and voice prints, insurers can automate the KYC process and reduce the time required for customer verification.
• Faster Customer Onboarding: AI-driven solutions help streamline customer onboarding, making it faster and more efficient, resulting in a smoother user experience.

Underwriting

AI revolutionize underwriting by enabling real-time risk assessment and decision-making.

• Automate Underwriting: AI analyzes vast amounts of data, including demographic information, past claims history and external sources to make accurate underwriting decisions. This reduces fraud, optimizes risk management and allows for direct policy purchase.
• Improving Claim Forecasting: Machine learning models enhance traditional statistical approaches in predicting claims, offering more precise risk assessments.
• Handling Complex Medical Reports: AI can assist, underwriters in detecting inconsistencies in medical reports, ensuring better policy suggestions, especially for high-risk cases.

Lapse Risk Analysis

Policy lapses can significantly impact insurance company’s profitability and market reputation. AI help mitigate these risks.

• Understanding Lapse Risk: Policy lapses occur when policyholders terminate their policies. Those with concerns about their health or other insurability issues tend not to lapse, potentially leading to more claims than expected.
• Impact on Profits: High lapse rate can reduce expected profits and damage trust among new customers. For example, the Fifth Quantitative Impact Study (QIS5, 2011) by the European Insurance and Occupational Pension Authority (EIOPA) rated lapse risk with a score of 50%.
• Real-World Insights: The Life Insurance Association of Malaysia (2017) reported by a growth of 4.7% in new business but also noted a 3.2% increase in claims, indicating a need for better lapse risk management.

Insurtech Innovation in AI

Leading insurers worldwide are adopting AI-driven solutions to enhance their operations and customer service.

• Kyobo Life Insurance, South Korea: Launched an AI-based underwriting platform that processes large datasets using machine learning, making it easier to approve or deny contracts based on pre-existing conditions and medical history.
• Fukoku Mutual Life Insurance, Japan: Introduced IBM’s Watson Explorer AI to analyze medical histories and surgical procedures, ensuring accurate payout calculations.
• Prudential Singapore: Developed and AI-powered chatbot to provide customers and financial consultants with instant, personalized information about life insurance plans.
• Clearcover: Allows users to receive AI-generated quotes tailored to their needs, simplifying the decision-making process.
• Lemonade: The digital insurance company known for its innovative use of AI and technology, set a world record for the fastest claim settlement in the insurance industry. In 2016, Lemonade processed and settled a claim in just 3 seconds using an AI-powered claim system.

Case Study: Predicting Lapse in Life Insurance Policies

This study (linked https://www.kaggle.com/code/balgdrum/life-insurance-policy-lapses-feature-selection#Methodology) showcases how AI can predict lapse risks using advance machine learning techniques. The study covers a step-by-step methodology, including:

• Business Understanding: Defining the problem and objectives.
• Data Preparation: Cleaning and preparing the data for analysis.
• Feature Selection: Comparison of correlation analysis, chi-squared tests, with XGBoost for identifying relevant features.
• Model Application: Implementing classification algorithms namely Random Forest and XGBoost to predict policy lapses.
• Evaluation and Comparison: Assessing the model’s performance and comparing results.

AI is revolutionizing the insurance sector, from improving customer acquisition and underwriting to managing lapse risks and enhancing overall operational efficiency. As Insurtech continues to evolve, AI will play an increasingly critical role in shaping the industry’s future, offering innovative solutions that benefits both insurers and customers.

By: Bal Gopal Lal Shrestha

Chief Manager