Almost 90% of companies in the insurance industry have integrated or are planning to integrate AI into their solutions. This massive adoption has caused the market size of AI in insurance to expand. Statistically speaking, it is estimated to grow to $91 billion by 2033!

For insurance companies that have relied on legacy systems for years, integrating AI into their processes can seem daunting and complex. However, AI-driven benefits such as faster claims processing, more accurate risk assessment, and improved customer experience make it worth the effort.

As AI technology matures and challenges are addressed, the potential for it to transform the insurance industry for the better also increases.

Read on as we take an in-depth look at AI in insurance and examine its impact on efficiency, customer service, and innovation.

Impact of AI on Insurance Operations—Which Aspects Have Improved?

Insurance is a complex and multifaceted industry. AI’s strength lies in its ability to handle this complexity in multiple ways, improving workflows and streamlining operations on several business fronts. AI in insurance offers benefits across many areas, such as:

Risk Assessment

When assessing risks, more data results in more accurate results. AI can analyze large sets of data from various sources, such as public records, social media, etc., quickly to give accurate risk assessments.

AI leverages predictive analytics to identify patterns and trends in data, allowing insurers to predict future claims with greater accuracy.

Use case example: Auto insurance companies can leverage AI to assess vehicle telematics data. They can use it to evaluate driver behavior in real-time and adjust premium amounts based on the risk involved.

Fraud Detection

As per Forbes, about 10% of property and casualty claims made in a year may be fraudulent. To tackle this challenge, AI uses highly sophisticated algorithms that easily detect data anomalies to deter fraud.

Traditional methods rely on pre-defined rules to flag suspicious claims. However, AI can uncover patterns and relationships that might be missed through manual evaluation.

Use case example: Health insurance companies can use AI algorithms to cross-check medical claims with patient records. This can help them detect anomalies and, subsequently, fraudulent claims.

Claims Processing

Manually processing claims used to be an arduous, time-consuming task for large-scale insurance providers, but not anymore. With the implementation of AI in insurance, businesses can process thousands of claims in minutes.

It also streamlines the process for legitimate claims by automatically flagging low-risk claims for quicker approval. Faster and quicker claim processing also leads to increased customer satisfaction.

Use case example: Home insurance companies can implement AI to automatically review damaged photos submitted by customers to check for fraud and process claims quickly.

Underwriting

Insurance underwriting is a painstaking yet essential part of the business. Before AI, underwriters would assess the risk involved in insuring people purely based on their understanding.

But like claims processing, AI has also automated the entire underwriting process through predictive modeling, making it faster and more efficient.

By considering a wider range of data points, AI helps reduce bias in the underwriting process, which can lead to fairer policy pricing. Moreover, through its rule-based engines, it strongly enforces underwriting guidelines to increase insurance safety.

Use case example: Life insurance companies can use AI to analyze data sets like genetic information to tailor the underwriting process to individuals precisely.

Routine Administrative Tasks

By implementing complex machine learning algorithms, AI has automated most of an insurance provider’s routine administrative activities, including data entry, document management, scheduling, and more. This frees insurers time to focus on more complex tasks and customer service.

Overall, AI empowers insurance companies to make complete data-driven decisions, leading to better risk assessment, pricing, and resource allocation.

Use case example: General insurance companies can leverage AI-powered chatbots to handle customer service and provide quick resolutions.

How Has Gen AI Influenced Customer Engagement and Experience?

According to data, about 46% of individuals prioritize customer satisfaction when choosing an insurance provider. This highlights its importance. But before the integration of Gen AI, ensuring customer satisfaction in the insurance industry was a rollercoaster ride.

With the introduction of Gen AI, this has changed drastically. The implementation of this technology in the insurance sector largely revolves around providing a seamless, fulfilling experience to its customers. Here’s how:

• Personalized Offerings: Gen AI deeply analyzes customer data. This enables insurance businesses to offer policy recommendations that are personalized to a customer’s unique needs and preferences. In commercial P&C, this can be used to tailor property and liability coverage options for a manufacturing company based on its specific operational risks.
• Streamlined Policy Management: Gen AI automates crucial policy management tasks like policy updates, renewals, amendments, etc. This means customers can skip the queue and get their insurance up and running in no time. For instance, Gen AI can automatically update and renew complex commercial P&C policies for a retail chain, minimizing manual intervention.
• Data-driven Insights: Gen AI leverages data-driven insights to improve customer interactions. Simply put, it helps insurance businesses gather feedback to understand customer psyche and serve them more coherently. In commercial P&C insurance, this can be used to gain insights like customer feedback and refine coverage options.
• Better Customer Support: The use of Gen AI-enabled chatbots, virtual assistants, and communication tools helps insurance businesses provide an engaging yet efficient service to their customers. In commercial P&C, chatbots can assist companies with detailed queries about their property policies, providing quick and accurate information.

Cognitive Document Processing (CDP)—A Key Implementation of AI in Insurance

AI has many implementations in the insurance industry. One of the key ones is Cognitive Document Processing (CDP).

What is Cognitive Document Processing (CDP)?

CDP is a document processing method used in the insurance industry. It mainly uses AI technologies like machine learning and Natural Language Processing (NLP) to automate the handling of both structured and unstructured documents.

Benefits of CDP

• Increased Processing Speed: CDP is a relatively quicker document processing method. From data extraction to input—everything is automated. Thus, processing speed is also much faster.
• Improved Accuracy: Since CDP is based on AI technology, the need for human intervention is negligible. This improves the accuracy of document processing.
• Enhanced Compliance: Due to the integration of AI, CDP ensures that every document is processed in accordance with laws and regulations, minimizing the chances of legal issues.
• Reduced Operational Costs: Again, since no manual labor is required, CDP also reduces the operational costs involved in insurance document processing.

Evolving Insurance LLMs—Enhancing Customer Interactions with Efficiency

As the implementation of AI advances in the insurance industry, Large Language Models (LLMs) are gaining prominence.

LLMs are advanced AI systems precisely designed to understand, generate, and respond to queries made in human language. A popular example is GPT-4—an LLM tool that provides instant resolutions to queries based on your prompts.

In the insurance industry, LLMs are increasingly implemented to handle customer queries. In fact, their role is not limited to providing resolutions. LLMs can also assist in policy explanation, claims processing, and underwriting. Besides, each of these is done with maximum accuracy and personalization.

In the future, as LLMs become more advanced, it is expected that they will play an even more prominent role in the insurance industry. So, if you haven’t implemented it yet, start incorporating LLMs into your insurance solutions.

Examples of Insurance Companies Who Leveraged AI to Flourish

Here are three insurance companies that are successfully using AI to enhance their operations:

Cytora

An insurtech company, Cytora aimed to reduce the risks associated with inefficient underwriting. In an attempt to do so, they implemented the power of AI and ML into their underwriting operations. The technology did wonders. By analyzing vast sets of raw data sources with its internal algorithms, Cytora’s new AI-ML-powered underwriting mechanism could make risk-free, data-driven underwriting decisions.

Insurify

Insurify wanted to revolutionize the ‘insurance shopping’ experience through personalization. They leveraged AI and machine learning to analyze data instantly, generating personalized insurance quotes on the spot.

It also uses AI to match individuals with the right car and home insurance providers for them. Depending on a person’s demographics and locations, it gives customized suggestions on the best insurance provider for their needs.

Hippo

Hippo, a home insurance provider, surged in 2023, with both total generated premium (TGP) and revenue experiencing significant growth in the final quarter and for the whole year. Part of the success is attributed to utilizing AI for underwriting, analyzing property data (like satellite imagery), and customer information to offer faster quotes and potentially lower premiums.

Potential Challenges of Implementing AI in the Insurance Business

The benefits of AI in insurance are many—higher efficiency, better customer satisfaction, and so on. However, it also entails some drawbacks. Here are a few of them:

Data Privacy Issues

A study conducted by KPMG in January 2024 found that a whopping 63% of consumers were concerned about the potential of Gen AI to misuse their personal data or make it vulnerable to attack. And why not? AI requires large amounts of data to function. If attacked by hackers, all of it can get compromised in no time.

High Initial Costs

The cost of moving from legacy systems to cutting-edge AI technology can be just as expensive as revolutionary. This basically involves upgrading to AI-enabled software and hardware, which makes up for the high initial costs.

Training Needs

Introducing AI-integrated technology and systems is just half the battle won. To implement AI in insurance in full swing, you must have human resources with the right skills and knowledge to handle the technology. So, there’s also a need for rigorous employee training.

Ethical Issues

While AI can automate tasks like underwriting and claims processing, these activities do require some human acumen to ensure fairness. AI’s full-fledged involvement in these ethical aspects of the insurance business can raise questions about its equity.

Compliance Challenges

AI in insurance is still a work in progress. In fact, its regulatory landscape continues to evolve. Thus, businesses must stay vigilant and aware of laws and regulations and update their AI systems periodically to ensure compliance.

Wrapping Up

AI in insurance is a breakthrough. Its implementations in every area of the industry—from operations to customer satisfaction and document processing—have proven to have transformative effects on efficiency and profitability.

However, there’s another thing we must not forget—AI implementation in the insurance sector is still in its nascent stages. There’s still so much room for exploration and adoption. So, analyze new arenas for AI implementations and join the revolution.