The Impact of AI on Insurance: Top Use Cases You Must Know

Maruti Techlabs
8 min readJan 10, 2024


AI in Insurance

The insurance industry is on the brink of a significant paradigm shift.

From risk assessment and insurance underwriting to claims management and customer engagement, disruptive AI applications are transforming the very fabric of the insurance landscape.

But that’s not all. Beneath the surface, profound advancements in AI are underway. Personalized risk evaluation, dynamic pricing models, real-time damage analysis, and automated claims settlement — are crafting a new narrative.

Let’s delve into the statistics -

  • McKinsey says AI in insurance is poised to boost productivity and slash operational costs by up to 40%.
  • A survey quoted that 21% of insurance companies are preparing their workforce for AI-based systems.
  • According to Forbes, AI in insurance has led to a 99.99% enhancement in claims accuracy and a 95% improvement in customer experience.

These statistics paint a compelling picture of the use of AI in the insurance realm. However, as mentioned earlier, we have barely scratched the surface. With the seamless integration of connected devices, telematics, IoT, cognitive computing, and predictive analytics, we are glimpsing into a future ruled by intelligent workflows in insurance.

This blog contains the top AI use cases and applications every insurer must know in 2024.

Real-World Applications Of AI In Insurance

The transformative potential of AI is steering the insurance industry away from the age-old “detect and repair” approach towards an adaptive “predict and prevent” strategy.

These real-world artificial intelligence insurance use cases vividly demonstrate this paradigm shift.

1. Policy Servicing

The most predominant complaint among insurance consumers revolves around issues tied to policy servicing. Policy servicing refers to a range of tasks and interactions after a policy is issued. For example — policy amendments, premium payments, claims processing, policy renewals, customer inquiries, and more.

Integrating AI into the policy management system can significantly optimize operational efficiency. It can expedite processes, reduce manual labor, enhance accuracy, and elevate the overall customer experience.

By leveraging RPA in insurance, companies can automate the most tedious administrative and transactional tasks, including accounting, settlements, risk assessment, credit control, tax preparation, and regulatory compliance.

One prime example of this is WorkFusion. WorkFusion blends AI and machine learning techniques to analyze various documents and facilitate the automated intake of policy data. This lessens the manual effort needed to discover the pertinent fields for policy endorsements.

2. Insurance Distribution

Gone are the days when insurance distribution was a door-to-door sale.

Consumers are rapidly moving towards online platforms for insurance policy research, comparison, and informed decision-making. Forward-thinking insurtech companies are seizing this opportunity to revolutionize the insurance distribution phase.

Insurance Distribution

Modern insurtech companies are harnessing the power of optical character recognition (OCR), speech analytics, machine learning (ML), and natural language processing (NLP) to capture and capitalize on a customer’s digital behavior.

Their digital behavior aids in facilitating data-driven personalization. AI can tailor the policy offers and reach out to potential customers through the most suitable insurance distribution channels at the most appropriate time, thus resulting in a significant increase in uptake, higher revenue, and a larger market share.

3. Product Recommendation

The ‘one-size-fits-all’ approach no longer attracts modern consumers.

This has pushed insurance companies to adopt personalized product creation and recommendations. Insurance companies leverage multiple data sources, like connected devices, wearables, speech recognition, and social media, to extract valuable insights.

By capturing and analyzing these data points, insurers can better understand customers’ needs and preferences, which enables them to offer customized insurance offerings. This not only saves time and money but also instills confidence and loyalty.

Predictive machine learning models can significantly improve the accuracy and timeliness of these personalized recommendations. It’s worth noting that data quality is a critical component in this process — the more detailed and accurate the data, the more accurate the predictions.

How AI Can Personalize Insurance Recommendations?

Sproutt Insurance, an insurtech company, leverages AI to offer tailored life insurance plans. They use an AI-powered assessment considering various variables like lifestyle, emotional health, and nutrition. Based on these insights, Sproutt recommends life insurance products aligning with an individual’s needs. This innovative approach streamlines the insurance process and offers a highly personalized experience to customers.

4. Automated Inspection

Insurance assessments have relied on manual, costly, and time-consuming manual inspections. With AI-driven image processing and cognitive computing, insurers can swiftly and accurately examine car damages and provide detailed assessment reports. This not only reduces claim estimation expenses but also expedites the claims procedure.

AI also helps minimize human error and provides more reliable data for determining the final settlement amount. Companies like Liberty Mutual and CCC Intelligent Solutions are actively exploring AI-driven solutions to enhance the speed and accuracy of claim assessments.

Automated Inspections

5. Customer Segmentation

Customer segmentation is at the heart of personalization. With data-driven insights into policyholders, AI algorithms can categorize customers into specific segments based on their behaviors, demographics, and preferences. This enables insurance companies to tailor their products to optimize budgeting, product design, promotion, and marketing.

AI can also help identify high-value customers, predict their future needs, and recommend suitable policies, leading to more effective cross-selling and upselling strategies.

Furthermore, with machine learning models, insurers can continuously refine their segmentation, adapting to changing customer dynamics and ensuring their offerings remain relevant. Ultimately, AI-driven customer segmentation empowers insurance companies to deliver personalized services, improve customer satisfaction, and boost their bottom line.

Customer Segmentation

Insurify, an insurance aggregator, efficiently connects customers with car and home insurance companies tailored to their unique requirements. They leverage the RateRank algorithms to assess various factors, including a customer’s location and the desired discount amount, to identify policies that align with their needs.

6. Work Stream Balancing for Agents

Incorporating AI into workstream balancing can significantly boost operational efficiency and customer satisfaction. Predictive modeling tools can accurately anticipate peak workloads and customer demands, enabling managers to align resources effectively.

AI can also monitor an agent’s tasks and responsibilities in real-time, identify bottlenecks, redistribute work, and prevent delays in customer service. It intelligently assigns tasks based on the agent’s skills, experience, and workload, thus ensuring an expert handles each task.

Nika Lee, Chief Underwriting Officer for Aioi Nissay Dowa UK, says, “Investigations are now closed faster, accompanied by reports and supporting evidence. In addition, there is enhanced ownership by claims handlers because they trust the solution due to its explainable nature and want to contribute to its improvement.”

While RPA facilitates the automation of repetitive tasks, AI bots free up an agent’s time by handling repetitive queries. Machine learning tools help in customer segmentation, enabling agents to offer personalized services to targeted consumers.

Why RPA in Insurance?

Maia, a virtual assistant, resulted in a notable acceleration of agent servicing and self-servicing. Maia was instrumental in reducing the average waiting time for users in live chat interactions by a significant 40%.

7. Self-services for Policy Management

Most customers say, ‘attending sales calls for insurance policies is irksome’.

EY Insurance Industry Outlook 2021 reports stated that 69% of customers prefer to buy auto insurance online. AI-driven self-service platforms enable policyholders to manage and maintain their insurance policies independently. This includes tasks like policy renewals, updates to coverage, and claims processing.

AI-powered chatbots and virtual assistants provide real-time support, guiding policyholders through the process. They can assist policyholders in uploading the documents, determining their validity, and fast-forwarding the methods. Additionally, AI algorithms can proactively recommend policy adjustments based on changing circumstances, ensuring that policyholders are adequately covered while optimizing costs.

Sofya Pogreb, COO at Next Insurance, says, “The percentage of insurance applications that require human touch will go down dramatically, maybe 80% to 90%, and even to low single digits.”

Self-servicing with AI enhances customer satisfaction and reduces the administrative burden on insurance companies, leading to more efficient and cost-effective operations.

Self-servicing for Policy Management

Inshur, an insurtech app, harnesses the power of AI to enable professional drivers to conveniently search and compare various insurance quotes and select a policy that aligns with their specific requirements. It further facilitates the seamless transfer of existing policies, streamlines the claims reporting process, and provides real-time alerts, enhancing the overall insurance experience for users.

8. Claim Value Forecasting

Insurance claims value forecasting leverages AI tools to assess claim payouts accurately. AI considers various parameters like policy details, accident details, and claimant characteristics to estimate the likely value of a claim. This enhances claim processes, minimizes overpayment risk, prevents fraud, and boosts operational efficiency for fair, accurate, and consistent settlements, benefiting both insurers and policyholders.

As a result, insurance companies increasingly turn to AI-driven claim value forecasting to stay competitive and provide a superior customer experience.

“Outside of underwriting, sales, and customer support, perhaps the most exciting opportunities for AI in insurance are in the claims area.” — Robert Stewart, Head of Sales at the UK’s Claims Consortium Group.

Tokio Marine, an auto insurer, leverages an AI-based computer vision system and image recognition technology to assess damaged vehicles. This helps in reducing the time to process auto accident claims.

9. Process Mining

The insurance industry has complex and outdated processes that are both costly and difficult to change. Many organizations are stuck in these processes due to resistance to change. AI facilitates process mining that can help optimize workflows, cut down expenses, reduce processing time, and boost customer satisfaction and compliance.

AI scrutinizes extensive datasets to uncover patterns, bottlenecks, and inefficiencies in insurance processes like claim processing, underwriting, risk assessments, back office functions, and policy issuance. It helps streamline and optimize these processes, boosting efficiency and reducing processing times.

Furthermore, AI is pivotal in automating damage assessment, calculating claimant payouts, and identifying potential fraudulent claims. All of these contribute to a more suitable and accurate claim experience.

Read the full article: The Impact of AI on Insurance: 18 Top Use Cases You Must Know



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