Artificial Intelligence and Machine Learning
min read

The Impact of AI on Insurance: 18 Top Use Cases You Must Know

Discover the top use cases of AI in insurance that leaders in the industry are rooting for.
Pinakin Ariwala
Pinakin Ariwala
Updated on Feb 21/2024
Artificial Intelligence and Machine Learning
min read
The Impact of AI on Insurance: 18 Top Use Cases You Must Know
Discover the top use cases of AI in insurance that leaders in the industry are rooting for.
image
Pinakin Ariwala
Updated on Feb 21/2024
Table of contents
Introduction
Real-World Applications of AI in Insurance
Conclusion

Introduction

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%.
  • 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 18 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 18 real-world artificial intelligence insurance use cases vividly demonstrate this paradigm shift.

real world applications of ai in insurance

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 survicing 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 processingunderwriting, 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.

10.Churn Reduction

Churn reduction, even with marginal improvements, can yield substantial revenue gains, amounting to hundreds of thousands of dollars. Natural Language Processing (NLP) within the AI space enables the detection of customer sentiments in call transcripts. This helps identify discontented clients and boost proactive measures to prevent and reduce customer churn.

AI tools pinpoint pivotal factors driving customer attrition, including delayed claims processing, service dissatisfaction, or premium fluctuations. They provide timely warnings and recognize early indicators of possible churn, enabling insurers to engage in personalized retention strategies.

Furthermore, AI streamlines claims processing, enhancing efficiency and the overall customer experience. 

Avaamo, a conversational AI platform, enables seamless communication with customers in more than 100 languages. This allows insurance companies to serve diverse customer bases, expedite claim processing, personalize underwriting, and generate customized quotes.

11.AI-supported Customer Service

AI-powered chatbots can help streamline customer interactions, provide quick responses, and be available around the clock. They can assist with policy information, claim processing, premium calculations, and personalized recommendations.

In addition, AI tools can monitor representative calls and track performance based on long pauses, customer rating, customer tone, and more. These insights aid in creating tailored training programs that can help representatives offer better services.

how ai can improve customer service and retention in insurance

Lemonade, an insurtech startup, leverages AI to undercut more prominent pricing and customer acquisition players. Their enthralling digital experience has made Lemonade a top insurer for younger demographics.

Lemonade's AI claims bot, Jim, adeptly manages many claims and customer queries. In 2019, Jim handled over 20,000 claims and customer queries without human involvement. This has reduced operational costs, streamlined workflow, and elevated customer satisfaction.

12.Predictive Maintenance

The availability of vast datasets, data analytics, and machine learning tools can enable insurers to predict upcoming maintenance. This proactive strategy helps prevent costly breakdowns, reduce downtime, and optimize resource allocation.

For example, AI can predict the maintenance needs of insured properties like houses or cars. It can also flag potential health warnings based on health and wellness data. Ultimately, predictive maintenance reduces the risk of claims and enhances the longevity and reliability of insured assets.

Lucie Bakker, Chief Claims Officer and Board Member at Allianz Versicherungs-AG said, “AI is also helping us to adopt a more predictive and preventive approach – evolving from an insurer who analyzes risks from a rear-view mirror perspective and pays claims to an organization that helps customers to mitigate risks and avoid losses in light of natural catastrophes and related events.”

13.Document Digitalization with OCR

OCR can accurately capture and reconcile data from diverse sources, thus eliminating the need for manual data entry.

OCR can render each pixel and translate it into digital input when coupled with computer vision. AI tools can then validate the submission against existing database entries. Such enhanced automation can yield significant cost savings.

Additionally, OCR applications can enhance customer onboarding and Know Your Customer (KYC) procedures. Insurers can digitally extract data from ID photos and promptly add it to customer profiles. This enables them to digitally onboard customers while reducing onboarding costs and increasing speed and customer satisfaction.

14.Usage-Based Insurance (UBI)

Telematics devices and smartphone apps enable insurers to closely monitor driving behavior, including speed, braking habits, acceleration, and adherence to traffic rules. This real-time data can also help insurers assist their customers in choosing a less accident-prone or less congested road. This proactive approach minimizes accident risks and dramatically enhances the overall customer experience.

usage based insurance

Such insights allow insurers to tailor policies to each driver's risk profile. Safer drivers are rewarded with reduced premiums, while those with riskier habits pay premiums in line with their actual risk exposure. This approach fosters equity and encourages safer driving practices.

Nauto, an AI-enabled driver safety platform, helps predict, prevent, and end distracted driving. The company leverages dual-facing cameras, computer vision, and proprietary algorithms to assess how drivers interact with vehicles and the road to pinpoint and prevent risky behavior in real-time.

15.Vehicle and Asset Tracking

Vehicle and asset tracking, when combined with Artificial intelligence, offer a powerful solution for reducing risks and enhancing recovery in theft cases. By integrating GPS, sensors, and machine learning algorithms, this system provides real-time monitoring and analysis of the location of the assets.

Depending on the location and recent happenings in the locality, geospatial analytics platforms and satellite imagery analysis  can accurately gauge the risk associated with an asset.

In the event of theft, machine learning models for risk prediction   can swiftly detect unusual patterns, such as unauthorized movement or location changes, and trigger immediate alerts to owners and authorities. Such proactive measures increase the likelihood of theft prevention and recovery.

 iNetra is an AI-powered system that uses deep learning-based analytics to enhance safety and security in set premises, providing real-time monitoring, instant notifications, and detailed reporting.

16.Product Innovation

AI-driven product innovation is ushering in a new dawn in the insurance industry. By leveraging the power of artificial intelligence, insurers can analyze vast datasets, detect emerging market trends, and gain invaluable insights into ever-evolving customer needs and risks. This data-driven approach paves the way for developing highly customized insurance solutions that cater to the dynamic demands of policyholders.

Predictive models can allow insurers to underwrite entirely new insurance categories. For example, AI can process satellite imagery and weather data to design insurance products tailored to the needs of farmers in regions lacking historical data.

17.Dynamic Pricing 

AI-powered dynamic pricing is reshaping the insurance landscape, providing greater fairness and precision in premium determination while accommodating the ever-evolving nature of risk. 

Traditionally, companies considered factors like credit score, income, education level, occupation, and marital status to calculate premium pricing. These factors penalized low-income buyers and did not promote fair pricing.

Advanced algorithms and continuous data analysis enable insurers to tailor premium pricing to reflect policyholders' changing circumstances and behaviors. This dynamic model considers various variables, such as driving habits, health data, and environmental factors. It adjusts premiums in real time based on risk factors or individual choices.

For example, accident-prone commuting routes could trigger immediate increases in auto insurance premiums. Likewise, engaging in adventure sports might lead to surges in life insurance costs. Conversely, a commitment to regular workouts could lower health insurance premiums.

Hyper-personalized pricing benefits policyholders by offering equitable pricing and allows insurance companies to stay agile in a rapidly changing world.

18.Automated Compliance Checks [Ensuring Adherence to Regulations] 

Regulatory compliance risk management is a paramount challenge for every insurance company. AI enables insurers to automate complex and labor-intensive compliance monitoring processes. 

AI algorithms can swiftly and accurately scan vast amounts of data, pinpointing deviations from regulatory standards. This not only enhances efficiency but also reduces the risk of human errors.

Insurers can promptly identify and address compliance issues, minimizing the potential for fines and legal complications. Moreover, AI-driven compliance checks offer real-time monitoring, allowing insurers to adapt swiftly to evolving regulations, ensuring they remain fully compliant in a rapidly changing regulatory landscape.

Conclusion

These diverse use cases of AI in insurance serve as a blueprint for insurers to adapt to the ever-evolving environment of the insurance sector. Whether providing tailored product recommendations, predicting claim risk and value, automating insurance workflows, or enhancing customer support, AI is a transformative force. 

AI’s versatility holds the potential to revolutionize a multitude of business areas within the insurance industry. Leaders firmly believe that AI can be pivotal in boosting cost savings and business growth. A recent survey highlighted the potential of AI in the insurance space as -

  • 54% of leaders see AI as a catalyst for change in marketing and claims.
  • 47% recognize its potential for revolutionizing administrative tasks.
  • 46% believe AI can substantially impact underwriting.
  • 43% anticipate a transformation in customer onboarding.
  • 61% of industry leaders said that the primary advantage of implementing AI in insurance lies in enhancing staff efficiency and productivity. 
  • 48% said AI can also deliver superior customer services.

Thus, AI holds the immense power to revolutionize  every node of insurance operations, spanning from customer onboarding to policy servicing and claim settlement. If you're still deliberating on how to surf the AI wave and identify the areas where AI can elevate your operations, connect with our experienced AI consultants for strategic insights.

We offer a full suite of AI services catering to the insurance space. We helped one of our clients digitize their entire underwriting process by implementing OCR (Optical Character Recognition) and deep learning technologies. This helped them reduce the time spent on underwriting tasks by 40%, increasing their overall efficiency.

At Maruti Techlabs, we work closely with insurers to help them identify unique opportunities where AI can be applied to enhance customer experiences, streamline operations, and unlock new dimensions of business growth. 

Get in touch today!

Pinakin Ariwala
About the author
Pinakin Ariwala


Pinakin is the VP of Data Science and Technology at Maruti Techlabs. With about two decades of experience leading diverse teams and projects, his technological competence is unmatched.

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  • UI/UX
  • Product Strategy

  • DelightfulHomes (Product Development)
  • Sage Data (Product Development)
  • PhotoStat (Computer Vision)
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  • A20 Motors (Data Analytics)
  • Acme Corporation (Product Development)

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