AI: How Insurers Serve the Needs of Modern Customers

AI continues to dominate the C-suite agenda, and the insurtech sector remains a prime candidate leading this transformation. This technology is reshaping critical aspects of insurance operations, especially in claims management and customer experience.

According to the Goldman Sachs Asset Management Global Insurance Survey 2024, 73% of insurers are using or exploring AI to reduce operational costs. Further, 39% of insurers are using or considering AI for underwriting, with 20% leveraging AI for investment evaluation. Recent data from McKinsey suggests that AI could potentially deliver up to US$1.1tn in additional value to the insurance sector annually by 2030, while a PwC survey found that 68% of insurance companies use or plan to implement AI in their operations.

Alan O'Loughlin, AVP Data Science, International, LexisNexis Risk Solutions, says: "AI and machine learning (ML) technologies empower rapid, data-driven decision-making that means customers should enjoy faster, fairer and more accurate quotes and a more personalised service at insurance claim." 

He continues: "Data normalisation through AI and ML techniques is creating standardisation and consistency for usage-based insurance based on this data." 

Insurers are now able to harness vast amounts of structured and unstructured data from various sources, including telematics devices, wearables and social media platforms. This wealth of information allows for more accurate risk assessment and pricing models.

The transformation is by no means cosmetic; it's changing how insurers interact with policyholders. At the heart of this change lies the exponential growth in data availability and processing capabilities. 

Daniel Cole, Senior Managing Director, Financial Services & Insurance Practice at Publicis Sapient adds: "AI revolutionises risk assessment and underwriting in insurance by analysing vast data sets from diverse sources like social media and financial records. It improves accuracy through machine learning algorithms, automates routine tasks, and enables personalised underwriting models." 

He continues: "Real-time decision-making and continuous learning further enhance efficiency and customer satisfaction. Additionally, AI strengthens fraud detection, ensuring integrity across insurance portfolios."

A recent survey by Gallagher Bassett, featured in "The Carrier Perspective: 2024 Claims Insights" report, indicates that 83% of UK insurers have either implemented or are in the process of implementing AI chatbots or generative AI to enhance claims resolution.

Greg Cole, Head of Claims at AND-E UK, provides insight: "With the right generative AI (Gen AI) capability, virtual agents can respond to customers in a natural and conversational manner, while delivering precise answers whenever they need them. AND-E UK has seen 36% of calls successfully directed to virtual agents, freeing up human agents to deal with the more complex customer needs.

"When combined with live voice transcription, AI can listen and provide handlers with answers and next best action recommendations in conversations with customers. This ensures that handlers have the information they need to provide timely and accurate support, directly contributing to positive customer outcomes as mandated by the Consumer Duty."

AI algorithms are being employed to detect fraudulent claims with unprecedented accuracy. A recent study by The Coalition Against Insurance Fraud (CAIF) indicates that insurance fraud can cost US consumers US$308.6bn yearly. That amount includes estimates of annual fraud costs across several liability areas, including life insurance (US$74.7bn), property and casualty (US$45bn), workers compensation (US$34bn) and auto theft (US$7.4bn).

Alan adds: "Full automation of fraud detection is unrealistic in the short-term. It needs the right mix of human skills, data and technology. Digital forensic tools using AI can be used to identify pixel and image manipulation, even spotting fake images created by Gen AI.

"The future of fraud detection will therefore continue to rely on the 'gut feel' of an experienced claims professional but there will be more and more tools (especially AI and generative AI) so that those current basic rules are not as restrictive and become more flexible depending on the type of claim."

Daniel adds: "AI is pivotal in detecting and preventing insurance fraud by leveraging advanced analytics and machine learning. It identifies patterns and anomalies in vast amounts of claims data, flagging suspicious activities for further investigation. Behavioural analysis compares current claims against historical behaviours to spot inconsistencies, while predictive modelling assesses risk factors to prioritise high-risk cases."

The rise of parametric insurance and IoT integration 

One of the most innovative developments in the insurance landscape is the rise of parametric insurance products. These policies, which automatically trigger payouts based on predefined parameters rather than traditional claims assessments, are gaining traction across various insurance lines. For instance, in crop insurance, satellite imagery and weather data are used to determine payouts, significantly reducing the need for on-site assessments and expediting the claims process.

The Internet of Things (IoT) is playing a pivotal role in this transformation. Connected devices in homes, vehicles and even on individuals are providing insurers with real-time data, enabling more accurate risk assessment and proactive risk management.

"LexisNexis Vehicle Build allows insurance providers to price based on the Advanced Driver Assistance System (ADAS) features on the car. An ADAS classification system was created using machine learning to scan millions of lines of car manufacturer vehicle data to logically sequence and classify vehicle safety features and the component's intended operation or purpose," says Alan.

Improving trust and efficiency 

As insurers continue to navigate the complex landscape of technological innovation, the focus on data security and privacy has intensified. The implementation of regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States has necessitated robust data governance frameworks.

"Regulatory compliance is paramount in AI implementation for insurance, ensuring adherence to stringent data protection, fairness, and transparency laws. Compliance with regulations like GDPR and HIPAA is essential to safeguard customer data, maintain fairness in AI decisions, and avoid legal consequences,” Daniel highlights. "Steps to achieve compliance include educating teams on relevant regulations, designing AI systems with ethical considerations, implementing robust data governance practices, and conducting thorough risk assessments."

Ryan James, Managing Director of nFocus Testing, says: "Too often, quality assurance principles and practices are left until the end of a project.

“This can be damaging because if any errors or problems have occurred during the initial installation, those issues can escalate into significant conflicts elsewhere." 

What are the best practices for integrating AI with existing insurance IT systems?

Integrating AI into existing insurance IT systems requires a strategic approach. Begin by assessing the current infrastructure and defining clear AI integration objectives, focusing on areas such as claims processing and customer service. Ensure compatibility with existing systems and establish robust data integration processes for seamless data access and analysis. Scalability, flexibility, and stringent security measures are crucial for protecting data and regulatory compliance. 

Pilot projects and thorough testing, supported by change management and training, are key for refining AI performance. Automated testing streamlines the process, allowing for frequent testing and immediate issue identification, thereby enhancing the chances of successful integration.

To do this successfully, Ryan recommends working with a dedicated testing partner with the right skills who can make the process easier in several ways.

• Test engineers will manage the testing processes for you from start to finish. Bringing in an external testing partner means that you will have the right skills and expertise to manage those tests before, during, and even after the project goes live.

• Automating the quality assurance checks will not only speed up the efficiency of each test but will enable your digital transformation team to benefit from regular feedback and reassurance throughout the implementation.

As a result, insurers will be more confident that their chosen AI integration is working as it should and is bringing the business benefits that they expected from the outset.

• Working with an experienced testing team means that they can focus solely on the quality assurance checks. This will free up your transformation team to focus on the installation itself, which can significantly speed up the installation process.

What’s more, an external testing partner will know how to work with your third-party suppliers to make sure that the process is going smoothly.

• Experienced test professionals will be able to scale up your testing processes. They will be able to run automated tests in parallel on multiple devices and operating systems. This promises enhanced quality assurance checks that could be crucial to the smooth and safe running of any AI implementation.

He recommends implementing automated testing services into your project management plan to ensure AI systems work correctly from the moment they go live: "Bringing in an external testing partner means that you will have the right skills and expertise to manage those tests before, during and even after the project goes live. Automating the quality assurance checks will not only speed up the efficiency of each test but will enable your digital transformation team to benefit from regular feedback and reassurance throughout the implementation."

Insurers are investing heavily in cybersecurity measures, with global spending on information security in the insurance sector expected to reach US$9.2bn by 2025, according to Gartner.

The customer experience overhaul in insurance is not about adopting new technologies; it's about fundamentally rethinking the insurer-policyholder relationship. 

"Consumer Duty presents an opportunity for insurers to refine their operations and improve customer outcomes. By leveraging AI, insurers can enhance their understanding of customer needs, streamline claims processing, detect fraud more effectively, and ensure compliance with new regulations. These advancements not only help meet the requirements of the Consumer Duty; they also position insurers as leaders in an increasingly competitive market," says Greg Cole.

Daniel Cole says: "The future of AI in insurtech promises transformative advancements across several key areas. Companies are increasingly leveraging advanced analytics and predictive modelling to personalise insurance products and improve risk assessment accuracy. AI-driven chatbots and natural language processing are enhancing customer service and claims processing efficiencies. There's a strong focus on AI for fraud detection and proactive risk management, alongside efforts to enhance customer experiences through personalised offerings."

As the industry moves towards more proactive, personalised, and data-driven models, the lines between insurance, risk management, and lifestyle services are blurring. The insurers that successfully navigate this transformation will likely emerge as the leaders in an increasingly refined market.

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