AI and Insurance: The Dawn of Insurtech

9 min readAug 25, 2024

As consumers become more tech-savvy, they seek a more interactive approach to managing their finances and purchasing insurance products. This shift has spurred technology-driven innovations within the insurance sector, leading to a significant transformation driven by emerging technologies such as AI, big data, IoT, wearable devices, and blockchain.

Known as Insurtech, this innovative approach is creating unparalleled opportunities for insurers and policyholders, reshaping traditional models. Within the Insurtech space, AI serves as a catalyst for change, driving efficiency and innovation across the insurance value chain. Insurers are increasingly competing by offering customized insurance products and a more streamlined customer experience through AI, which has significantly attracted the attention of investors, recognizing the potential for growth and innovation driven by these consumer-centric strategies.

The global AI in insurance market size was estimated at USD 4.59 billion in 2022 and is projected to reach approximately USD 79.86 billion by 2032, growing at a CAGR of 33.06% from 2023 to 2032. While the Asia Pacific region, with its emerging markets and growing middle-class population, is expected to grow at the fastest rate during this period, North America is anticipated to dominate the market. This dominance is largely due to the active adoption of AI solutions by insurance companies in the region.

AI in Insurance Market Size, 2022 to 2032 (USD Billion)¹

How AI Pioneers Change in Insurtech?

AI enables the insurance industry to fully utilize extensive data, facilitating tasks such as learning, recognizing patterns, understanding natural language, and making decisions. Key technologies like machine learning, data analytics, natural language processing, and robotic process automation are pivotal in this transformation.

According to Accenture’s research, the banking and insurance sectors have the highest potential for AI-driven automation and augmentation. This includes automating routine tasks such as data entry, claims processing, and underwriting, which can be managed more efficiently by AI algorithms. Additionally, AI can augment existing processes by enhancing decision-making, providing personalized customer experiences, and improving risk assessment accuracy. Therefore, from enhanced risk assessment and streamlined underwriting processes to improved customer experiences, AI has great potential to foster a more responsive and customer-centric insurance ecosystem through personalized services and optimized claims processing.

Share of working hours in selected industries in the U.S. that could be automated/augmented by the use of AI²

Furthermore, blockchain technology has enormous potential to revolutionize the insurance industry by enhancing transparency and security in transactions. Blockchain ensures data immutability and reduces the risk of fraud. When combined with AI, blockchain can further enhance the efficiency and reliability of insurance operations, creating a more robust and trustworthy system.

This convergence of AI and blockchain in the insurance industry not only enhances operational efficiencies but also delivers superior value to policyholders, heralding a new era of insurance driven by technological advancements.

Challenges in the Insurance Space

The insurance industry is navigating a rapidly evolving landscape, confronting several significant challenges driven by technological advancements, regulatory pressures, and shifting customer expectations:

1. Data Privacy and Cybersecurity

Insurers collect vast amounts of sensitive data, making them prime targets for cyberattacks. Ensuring robust data protection measures is critical to maintaining customer trust and compliance with regulations. According to the Munich Re Cyber Risk and Insurance Survey 2024, 87% of global decision-makers state that their companies are not adequately protected against cyber risks³. The increasing sophistication of cyber threats necessitates substantial investments in advanced cybersecurity solutions to safeguard data and systems. Rapid technological advances and geopolitical crises further exacerbate these challenges, emphasizing the need for heightened risk awareness and improved cyber defenses.

2. Changing Customer Expectations

Customers today demand quick, personalized, and seamless digital interactions. Meeting these expectations requires significant investments in digital infrastructure and customer service technologies. Therefore, insurers must innovate continuously to provide better customer experiences, such as through AI-driven chatbots, personalized policy recommendations, and efficient claims processing.

A positive digital experience is crucial, as personalized services can boost customer retention by 81% and engagement by 89%⁴. Additionally, 55% of customers prefer digital channels over traditional ones, rising to 68% among younger consumers⁵. Insurers must bridge the gap between online and offline channels to meet these expectations effectively. Despite only 11% of decision-makers currently prioritizing omnichannel experiences, 42% of consumers value engagement across multiple devices and in-person interactions⁴. Furthermore, 80% of consumers favor insurers that connect their products to broader ESG initiatives, and 59% desire rewards for healthy living⁵ ⁶.

3. Climate Change and Emerging Risks

The increasing frequency and severity of natural disasters due to climate change pose significant challenges for insurers. Developing accurate risk models and offering affordable premiums in high-risk areas are ongoing issues. A recent article by Munich Re mentions that successful underwriting in insurance requires a detailed understanding of natural hazards and climate risks, as natural disasters become more frequent, less predictable, and often cause significantly higher losses⁷. Customers increasingly expect insurers to play an active role in mitigating these risks, which involves not just coverage but also prevention strategies.

4. Fraud Detection and Prevention

According to a study by the Coalition Against Insurance Fraud (CAIF), insurance fraud costs the U.S. $308 billion annually, accounting for approximately 1.5% of the GDP⁸. Fraudsters are becoming increasingly sophisticated, leveraging technology and organized crime networks to commit insurance fraud, particularly online. These criminals use advanced methods to create deceptive claims, making it harder for traditional detection methods to identify fraud. The growing complexity and frequency of fraudulent activities necessitate a continuous evolution in detection and prevention strategies to safeguard the insurance industry and reduce financial losses.

Transforming Insurance: AI for Today’s Challenges

Artificial Intelligence has emerged as a transformative force across various industries, revolutionizing processes and creating new opportunities. From healthcare, where AI assists in diagnosing diseases, to finance, where it enhances trading algorithms, AI is making a significant impact. In the insurance industry, AI is being utilized to address numerous challenges and drive innovation through various use cases such as claims management and fraud prevention.

One of AI’s most significant contributions to insurance is its predictive capabilities. AI algorithms can analyze extensive data to identify patterns and trends that would be challenging for humans to detect. This predictive power enables insurers to assess risk more accurately, set premiums with greater precision, anticipate future claims with increased confidence, and many more. Predictive analytics optimizes the underwriting process, leading to improved decision-making and profitability.

How?

Improving Customer Service

AI is revolutionizing the way insurers interact with their customers. Chatbots and virtual assistants, powered by natural language processing, are becoming the first point of contact for customer service. These AI-driven tools provide quick, personalized responses to customer inquiries, handle routine tasks, and even guide customers through complex processes. This not only improves customer satisfaction but also frees up human agents to focus on more complex and high-value interactions.

Streamlining Claims Management

Many aspects of claims processing, such as analyzing the contents of claims and supporting documents, assessing claims and determining eligible coverage, prioritizing claims based on their urgency, identifying missing information or documents, and updating claim statuses, can be automated with AI technologies. This automation leads to faster claims resolution, improved accuracy, and enhanced customer satisfaction.

Detecting and Preventing Fraud

Insurance fraud is a significant issue, costing the industry billions of dollars annually. AI is proving to be a formidable tool in combating fraud. Advanced machine learning algorithms can analyze claims data to detect unusual patterns and anomalies that might indicate fraudulent activity. AI can also leverage techniques such as voice and facial recognition to verify identities and detect deepfake content, making it harder for fraudsters to succeed.

Personalizing Insurance Products

One of the most exciting developments in Insurtech is the rise of personalized insurance products. AI enables insurers to analyze individual customer data and tailor policies to meet specific needs and preferences. This level of personalization can lead to more accurate pricing, better customer retention, and a stronger competitive advantage. Customers benefit from policies that are more closely aligned with their unique circumstances and risk profiles.

Redefining Underwriting

Underwriting, the process of evaluating risk and setting policy terms, is being transformed by AI. Traditional underwriting relies heavily on historical data and human judgment. AI, however, can process vast amounts of current data from various sources, providing real-time insights that enhance the accuracy and efficiency of the underwriting process. This leads to faster policy issuance and more competitive pricing.

Blockchain in Insurance

Blockchain technology is poised to bring about significant efficiency gains, cost savings, transparency, faster payouts, and fraud mitigation in the insurance industry. Blockchain allows for data to be shared in real-time between various parties in a trusted and traceable manner. This technology can also streamline claims processing, reduce administrative overhead, and prevent fraud by providing an immutable record of transactions. Blockchain also opens the door to new insurance practices and products, such as peer-to-peer insurance and parametric policies, further driving innovation in the sector.

Future of AI in Insurtech

The advent of AI in Insurtech is set to bring about a significant transformation in the insurance industry, enhancing efficiency, accuracy, and customer satisfaction. However, this technological leap also introduces substantial regulatory compliance challenges that insurers must tackle to effectively utilize these advancements.

Insurers must navigate a complex and ever-changing regulatory environment. Therefore, adapting to new regulations that address emerging technologies like AI and blockchain requires continuous monitoring and adjustments to stay compliant. A recent KPMG article highlights that the introduction of these technologies often outpaces regulatory frameworks, creating a challenging dynamic for compliance⁹. Additionally, the European Union’s Digital Operational Resilience Act (DORA) introduces stricter requirements for managing Information and Communication Technology (ICT) risks, including a framework for monitoring risks from third-party service providers¹⁰.

In the United States, the SEC’s new cybersecurity regulations mandate more detailed disclosure of cybersecurity risk governance and management, requiring public companies to report material cybersecurity incidents within four business days¹¹. Furthermore, data privacy regulations such as the General Data Protection Regulation (GDPR) in Europe¹² and the California Consumer Privacy Act (CCPA)¹³ impose stringent requirements on how data is collected, stored, and protected.

On the other hand, many insurers still operate with outdated legacy systems. Integrating advanced technologies, such as AI and machine learning, into these systems is essential but can be complex and costly. This integration is essential for staying competitive and meeting modern customer demands, but it is a complex and costly process. The transition involves not only technical challenges but also substantial financial and operational investments. Insurers must navigate these hurdles to update their infrastructure, which often requires overhauling existing systems, training staff, and ensuring compatibility with new technologies. The complexity and expense of these upgrades can be daunting, but they are crucial for maintaining relevance in an increasingly digital marketplace.

By effectively addressing regulatory and technical challenges, insurers can better position themselves to leverage the full potential of AI in Insurtech. This proactive approach will not only drive innovation and enhance customer satisfaction but also ensure compliance and security. Successfully navigating the technical, financial, and regulatory complexities is essential for insurers to thrive in an increasingly digital and competitive marketplace.

About the author: Burcak is an MBA Student at Duke University, Fuqua School of Business concentrating on Investment Finance and Strategy.

Unless otherwise indicated, commentary on this site reflects the personal opinions, viewpoints, and analyses of the author and should not be regarded as a description of services provided by Newlin or its affiliates. The opinions expressed here are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual on any security or advisory service. It is only intended to provide education about the financial industry. The views reflected in the commentary are subject to change at any time without notice. While all information presented, including from independent sources, is believed to be accurate, we make no representation or warranty as to accuracy or completeness. We reserve the right to change any part of these materials without notice and assume no obligation to provide updates. Nothing on this site constitutes investment advice, performance data, or a recommendation that any particular security, portfolio of securities, transaction, or investment strategy is suitable for any specific person. Investing involves the risk of loss of some or all of an investment. Past performance is no guarantee of future results.