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Embracing AI in insurance: From experimentation to enterprise-wide implementation

Embracing AI in insurance: From experimentation to enterprise-wide implementation | EasySend blog
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Artificial intelligence (AI) is radically transforming industries, and insurance is no exception. Although the insurance sector is still in the early stages of adoption, AI has the potential to reshape insurance practices across the insurance value chain, from customer service through to risk assessment, distribution, and fraud detection. 

According to Unit8, “AI could save $390 billion in costs across insurers’ front, middle, and back offices by 2030”. In addition, “insurance companies that have already implemented advanced analytical solutions are outperforming the competition today by 76%”.

Multiple factors have driven the AI revolution in the insurance industry:

  • An explosion of data
    Insurance is a data-driven industry. Over the past two decades, there’s been a massive increase in the amount of data generated globally, and a significant amount of that data comes from sensors in industrial settings and connected consumer devices. According to McKinsey, experts estimate there will be up to one trillion connected devices by 2025. 
  • Increase in computing power
    The exponential increase in computing power has enabled the processing of large data sets and complex algorithms at previously impossible speeds. 
  • Covid-19
    As face-to-face interactions were reduced during the pandemic, many insurers turned to AI for processing claims and underwriting remotely. For the same reason, AI chatbots and automated customer service become more popular.
  • The rise of generative AI
    When ChatGPT was released in November of 2022, it essentially kicked off an “AI race”, and was soon followed by many innovative generative AI solutions. These systems introduced new capabilities for generating and processing natural language, which are used for enhancing customer service through chatbots, automating claim processing, generating and personalizing policy documents, and more.

The insurance industry first adopted AI with simpler applications, focusing primarily on automating routine tasks and data analysis in the areas of fraud detection, risk assessment, customer service, and process automation. However, these initial experimental steps have quickly evolved into full-scale implementations and become a cornerstone strategy. According to a study by Gartner, 40% of insurance industry respondents reported increasing investments in AI/ML. 

The genesis of AI in insurance

The insurance industry began experimenting with AI as early as the 1980s, but it was only in the late 1990s and early 2000s that AI began to gain real traction. The initial use of AI was focused on customer service and the analysis of data sets to identify insights for fraud detection and risk assessment. 

Customer service

Early efforts in applying AI to customer service in the insurance industry focused on basic automation and data-handling tasks. This included simple, automated systems for answering basic customer inquiries via phone or online interfaces. These systems were able to answer frequently asked questions regarding policy details, coverage, and payment processes, and had several applications, including:

  • Chatbots and virtual assistants, which were primarily rule-based and somewhat rudimentary
  • Emails, automatically categorizing incoming emails and responding with pre-set answers
  • Sorting and managing large volumes of customer data, making it easier for customer service agents to access the information they needed

Fraud detection

Early AI systems were programmed to recognize patterns that could indicate fraudulent claims. This was often based on rule-based algorithms that could flag claims if they matched known fraud scenarios. AI systems then evolved to include anomaly detection, which used machine-learning models to flag unusual patterns in large datasets. 

Risk assessment

Early applications of AI involved the use of very basic machine-learning algorithms. These rule-based algorithms analyzed structured historical data such as age, driving records, or health metrics to predict the likelihood of claims and price premiums accordingly. 

Overcoming skepticism

The insurance industry has been traditionally slow in adopting new technologies. This is largely due to the complex nature of its business operations, as well as intricate regulatory environments that need to be carefully navigated. 

The use of AI has also raised concerns regarding ethical and privacy risks for consumers, as well as possible data privacy breaches and security vulnerabilities. Questions regarding the reliability of AI systems also resulted in many companies taking a cautious approach. 

However, early experiments resulted in extremely positive outcomes - reduced fraudulent claims, optimized risk assessment, and improved customer engagement, and all of it at scale. AI advancements showed real potential to drive innovation and provide additional revenue streams for insurance companies, which helped to pave the way for wider acceptance and integration of AI throughout the industry.

Advancements in AI technologies and their impact

From the early experiments described above, AI has seen remarkable advancements in the insurance industry. Several technologies have played a key role in the revolution, including:

Machine learning (ML)

Machine learning is a technology that uses data and algorithms to teach a machine how to identify patterns to perform a specific task. Essentially, machine learning aims to imitate the way that humans learn, gradually improving its accuracy. 

In the insurance sector, ML is used for several applications, including risk assessment, claims processing, and fraud detection. By analyzing large datasets, ML algorithms can identify insights that would be impossible for humans to detect within a reasonable timeframe.

Natural Language Processing (NLP) 

NLP is a branch of AI that focuses on understanding, generating, and manipulating human language, and it has revolutionized customer service within insurance. Chatbots and virtual assistants that use NLP can understand and respond to customer queries in natural language. 

NLP is also used to analyze large datasets and identify fraud patterns. For example, NLP can flag unusual claim activity by identifying possible irregularities in the language used in claims. It can also analyze customer reviews to see if there are any negative sentiment indicators.

Predictive analytics 

Predictive analytics uses historical data to predict future events and trends. In insurance, this technology is used for predicting risks, customizing policies, and anticipating future claim trends. For example, it can predict which claims will be straightforward and can be automatically processed, and which require human intervention. Predictive analytics can assess risk for specific customers, enabling insurers to create hyper-personalized products and services, adjust premiums, or even refuse coverage altogether. It can also allow insurers to take proactive steps to prevent fraud. 

Computer vision

Computer vision is an AI technology that enables computers to interpret and analyze visual information. This technology has several applications in the insurance industry:

  • Auto insurance, assisting with claims processing by assessing vehicle damage from policyholder-submitted photos, speeding up damage assessment, repair cost estimation, and fraud detection.
  • Property insurance, evaluating risks and damages. This is especially useful following disasters. 
  • Risk assessment and underwriting, analyzing images of properties, vehicles, and businesses to identify risks.
  • Fraud prevention, detecting discrepancies in claim-related images and videos by comparing them against existing records.

Real-world applications of AI-driven improvements in the insurance industry

The following examples showcase how AI technologies are completely reshaping the insurance industry, making processes more efficient, customer-centric, cost-effective, and scalable. 

Claims processing

Several insurance and insurtech companies are making great strides in using AI technologies to optimize the claims process. 

For example, a Nordic insurance company was struggling to manually process claims from multiple sources in multiple data structures. Using various AI technologies, including machine learning and NLP, the company automized its claims process by converting unstructured documents into structured data. 

Geico is another insurance company that uses AI technology (developed by Tractable) to speed up claims processing. Using computer vision, photographs of damaged vehicles are analyzed to provide accurate estimates.

Policy personalization

Lemonade uses several AI technologies to personalize policies. For example, their AI chatbot, Jim, collects data and tailors insurance policies to align with Lemonade’s customer’s specific needs and preferences. 

Risk management

Swiss Re uses an AI model to predict flight delays, analyzing over 200 million historical data points and data from over 90,000 flights per day. These predictions are used to determine the price of flight insurance. In the event of a flight delay, customers who purchased this insurance when buying their ticket will receive an instant payout, without the need to file a claim.

Zurich is another insurance group that uses machine learning models to improve risk selection. According to Jane Rheem, Zurich North America’s Chief Data and Analytics Officer, as a result, underwriters can now gain critical insights to assess risk and determine policy premiums. 

The pivotal role of high-quality data in AI success

While the benefits of AI to the insurance industry are undeniable, the success of AI initiatives depends on the quality of the data fed to the different AI technologies. 

Digital data intake is an excellent way to create a strong data foundation for effective AI implementation. In addition to improved accuracy, fewer human errors, and faster processing, digital data intake also provides AI systems with the comprehensive datasets necessary for effective pattern recognition and predictive analytics.

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Vera Smirnoff
Vera Smirnoff

Vera Smirnoff is the demand generation manager at EasySend. She covers digital transformation in insurance and banking and the latest trends in InsurTech and digital customer experience.