The Role of Artificial Intelligence in Insurance Underwriting

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As artificial intelligence (AI) in business has been increasingly implemented, it has enabled insurance underwriting processes to be more accurate, efficient, and personalized. 

AI is particularly important in the insurance industry because it’s an industry that relies heavily on large amounts of actuarial data. Collecting large datasets manually is inherently painstaking and time-consuming. 

Traditional underwriting processes have relied on historical data and human judgment for risk management and policy design. 

AI analyzes massive amounts of data in real time, allowing insurance companies to recognize patterns they may have otherwise overlooked and make faster decisions. 

AI is poised to revolutionize several facets of insurance underwriting by reducing administrative tasks, improving risk assessments, personalizing policies, streamlining claims, and improving customer service. 

Shortcuts:

The Roles of AI in Insurance Underwriting

Benefits of AI In the Insurance Industry

Concerns About AI In the Insurance Industry

The Roles of AI in Insurance Underwriting

As AI rapidly transforms the insurance industry, it has become a whole new world in the underwriting departments. Tools like predictive analytics, natural language processing, and machine learning streamline workflows while enhancing decision-making and reducing human error. 

AI also enables insurance companies to create new models, enabling more accurate, efficient, and personalized services. 

AI assists underwriters by collecting new data sets and combining them with what underwriters already know. As a result, it’s reshaping how insurance companies market, assess risks, and interact with customers. 

As an example, Zurich Insurance uses AI in more than 160 processes. 

The Lemonade insurance company is a relative newcomer to the insurance industry. The company offers home, tenant, car, pet, and term life insurance policies. Lemonade processes claims using AI and chatbots rather than humans. Carrier Management reports that Lemonade has processed minor claims in as little as two to three seconds. 

The Flyreel app has also become an important underwriting tool in the insurance industry. The company has partnered with Lexis Nexus to streamline inspections, personal property inventories, and claims submissions without human assistance. 

Afiniti came up with an app that optimizes customer interactions. Its primary function is using AI  to pair customers with an insurance agent who can best serve their needs. 

AI will help underwriters detect anomalies in the claims process to help them detect fraud. According to the Coalition Against Insurance Fraud, consumers lose $308.6 billion because of insurance fraud. 

Reducing Administrative Tasks

By automating data entry and other routine tasks, underwriters can spend more hands-on time evaluating risks that require a human to decide whether to issue a policy, offer a renewal, or handle a claim. 

For example, chatbots and digital assistants can handle initial and routine customer inquiries. Claims specialists can intervene at any juncture where a human being needs to intervene.

Improving Risk Assessment and Fraud Detection

AI is highly valuable in assessing risks and detecting fraud because it can draw large amounts of data from multiple sources. 

Underwriters can use the new information to identify potential risks and predict emerging trends. 

Timely, accurate data reveals suspicious patterns and inconsistencies that would otherwise be overlooked, helping to cut down on losses where fraud is at play. Early fraud protection protects customers and saves them money.  

At policy issuance, AI can help develop more accurate risk profiles for customers. As a result, policyholders may qualify for better programs.

From a cost standpoint, an underwriter’s job is to help their companies assume the risks of future events and develop pricing models for premiums. AI can help underwriters and other employees to determine the most appropriate pricing for individual risks.

Creating Personalized Coverage

Whether we’re talking about health, life, property, or casualty insurance, AI reveals far more about customers than was previously possible. 

Underwriters can leverage this information to create custom insurance products and price them accordingly. For example, a personal lines insurance company might decide to reward its customers with an extra discount if they meet criteria that have proven to reduce risks, such as:

  • Being married
  • Insuring two or more cars
  • Having no claims on the home and car for five years

Property and casualty insurers now commonly offer safe driver discounts based on data the company collects from a mobile phone app or sensor attached to the car they regularly drive. 

These are examples of how auto policies no longer have to be one-size-fits-all.

A personalized approach ensures that customers have the right coverage in the right amounts to meet their unique needs while eliminating coverage gaps. 

Streamlining Claims Processing

AI can analyze claim forms, documents, and visual data to prioritize claims based on urgency and complexity. 

Zurich in Germany developed an AI tool to detect fraud patterns or anomalies by assessing the data in a claim. The AI tool also learns from previously identified cases of fraud. 

Insurance companies submit Images for examination, including the metadata, to help verify the authenticity of the data they collect.

Zurich Germany’s AI can even differentiate between types of damage to vehicles, such as dents and scratches. Models can examine digital images to help determine whether a car was struck in a parking lot, as a customer stated, or whether the car was in motion at the time of the accident.  

Improving Customer Experience

AI can analyze customer reviews and social media posts to help agencies better understand clients’ needs and preferences. 

AI has made a huge difference in the customer service arena by using machine learning to understand each customer’s intent. 

As customers provide small bits of information, AI can pull masses of information lightning-fast and put it at the fingertips of customer service representatives, enabling them to serve customers better and faster. 

Benefits of AI In the Insurance Industry

While AI has been an industry disrupter, it offers valuable benefits to insurance companies. 

AI enables insurance companies to significantly understand risks better, enabling them to be proactive on the risk management side and in their claims response. 

As AI creates efficiency in the underwriting process, customers ultimately benefit. 

Risk Assessments and Underwriting

Predictive analytics help insurance companies evaluate things like driving records, health records, and behavior to help them establish risk profiles with better accuracy than manual methods. The results allow insurers to price policies according to individual risk profiles rather than placing policyholders into a broad risk group. 

Claims Forecasting

Every claim is unique, making it difficult for insurance companies to predict future costs. AI aids insurers in analyzing patterns in past claims, which enables them to set appropriate reserves and price risks as accurately as possible. 

Fraud Detection

Insurance fraud costs insurance companies a lot of money. In turn, it has hurt consumers as fraud causes insurance companies to increase premiums. The U.S. Coalition Against Insurance Fraud is an organization that’s comprised of consumers, insurers, and government agencies. Their research shows insurance fraud costs consumers $308.6 billion every year. 

Their report lists the following common types of insurance fraud:

  • Insureds or agents lying on applications
  • Forgery of beneficiary
  • Fake policies sold by fake agents
  • Faked deaths where people are still living or never existed
  • Murder of an insured by a beneficiary
  • Agents pocketing premiums

As you can see, fraud may be committed by policyholders or agents. 

AI can help spot inconsistencies that shed light on these and other claims. 

Customer Retention and Acquisitions

Insurance companies can also implement AI to evaluate customer behavior and satisfaction levels. 

Customers can expect a more agile and customer-centric response from their insurance companies by utilizing AI. The data uncovers which customers will likely renew and which will switch to another provider. 

Armed with this information, insurers can tailor their marketing strategies toward retention, offer personalized discounts, and address customer concerns proactively. 

Operational Efficiency

Unlike humans, AI can handle a large volume of customer interactions at the same time. 

One of the issues with insurance claims is having enough claims representatives when there is a catastrophe. Predictive AI tools can help forecast claim volumes and customer service needs.

When a disaster hits, AI enables claims departments to allocate resources, staff an adequate number of claims representatives, and allocate resources generally. 

Additionally, chatbots and virtual assistants can provide 24/7 customer support to answer common questions without customers having to speak with an employee. 

Concerns About AI In the Insurance Industry

While breakthrough technologies like AI are undoubtedly fascinating, insurance companies must be aware of the risks versus the rewards and take strategic steps to protect their companies and those they insure related to AI technology. 

Data Privacy and Security

Insurance companies handle and store massive amounts of personal data on policyholders, and AI evaluates and uses that data. There are two main data privacy and security concerns – sensitive data exposure and an increased attack surface. 

With so much personal data being stored in cyberspace, data breaches have become the norm in every industry. The intersection of AI and sensitive data increases the risk of data breaches because it’s valuable data for hackers to obtain. Also, AI systems create more potential entry points, giving cyber attackers a greater attack surface. 

The HIPAA Journal reports that the Office of Civil Rights (OCR) received 5,887 reports of data breaches of healthcare records between 2009 and 2023. The breaches exposed nearly 520 million healthcare records, which is 1½ times the United States population. 

Health insurance companies must be very vigilant about AI and compliance with HIPAA laws to avoid serious negative consequences. 

Here are some of the concerns with AI and compliance with health systems:

  • Providing oversight of the use of AI technology
  • Reviewing contract templates to ensure compliance
  • Updating trainings on the use of AI and risks of non-compliance
  • Developing a code of conduct regarding how AI can be used
  • Updating privacy notices
  • Setting up protocols for regular HIPAA risk assessments
  • Hiring cybersecurity professionals and technicians to minimize AI risks

Governments and insurance regulators are primarily concerned about two facets of AI, including how AI systems collect and use personal information and how to regulate its use and output. 

For example, the General Data Protection Regulation (GDPR) governs how anyone in the world processes or transfers the personal data of EU individuals. The EU AI Act complements GDPR as it governs the deployment and application of specific AI systems.

The California Privacy Protection Agency (CCPA) is working on similar regulations related to how AI uses data to make decisions to ensure the process is fair. Other states may soon follow their lead. 

Insurance companies will have to be vigilant about staying on top of evolving laws and regulations to prevent the risk of penalties and lawsuits. 

Bias and Fairness

A huge concern about AI in all industries is that AI models are trained on historical data. Existing data may contain biases against certain demographics of people. Bias in the insurance industry could lead to unfair pricing for certain populations. 

AI could also unintentionally reinforce stereotypes or cause insurance companies to discriminate against certain groups of people based on race, gender, or socioeconomic status, raising ethical and legal concerns.

To combat bias, insurance companies will need to train their AI systems on representative, unbiased data and continue monitoring and updating AI systems to eliminate biases. 

Transparency and Explainability

Deep learning models are complex and difficult to interpret, essentially leaving the exact processes hidden in a black box. The lack of transparency leaves insurance companies in the dark about how they arrived at the results that affect policyholder premiums.  

The insurance industry is highly regulated, and insurance companies may struggle equally with how to explain AI-driven decisions to governing authorities. 

Laws and regulations around AI and data privacy are continuing to evolve. Changes could cause insurers to face legal challenges or fines for not complying with laws, either intentionally or unintentionally. 

Accountability and Liability

AI is a science, but it’s not a perfect science. It can only make decisions based on the information it has. As a result, an AI algorithm could make faulty or biased decisions that could result in significant financial or reputational harm. The caveat is knowing who is accountable and who should be held liable for the damage. 

Insurance companies could also face ethical challenges if they deny coverage to someone based on factors outside of a customer’s control. For example, AI could glean data from social media activity that may be inaccurate. 

Job Displacement

Since AI does the work of humans faster and more efficiently, there is a concern about insurance companies cutting back on staff for underwriting, claims, and customer service departments, adding to job losses. 

While insurance companies may need fewer employees, insurance companies will need to fill the gaps in the new skills needed to work with AI systems. As a result, insurance companies may need to overturn employees or upskill existing employees. 

Overreliance on Automation

As AI takes over large areas of operations, there is a risk of overreliance on AI. If the systems fail or make an error, it could take down large operations with it, significantly affecting the company’s reputation.

Customers may perceive automation as cold and impersonal, leaving them wondering where the human element is. This could be especially problematic in the area of claims where policyholders are dealing with catastrophes. 

Fraud Detection and Ethical Implications

We noted earlier that AI could be instrumental in detecting fraud, yet insurance companies must be aware of the ethical concerns around customer privacy and reasonable boundaries around surveillance activities. 

Insurance companies may also encounter situations where AI systems produce false positives, leading to wrongfully denying claims. Such issues could harm customer trust and negatively impact an insurance company’s branding.

Overall, insurance companies must consider these pitfalls and take measures to balance the benefits of AI with protecting data integrity and customer rights.   

Balancing the Pros and Cons of AI in Insurance Underwriting

It’s true that AI can process huge amounts of data quickly, boosting productivity and automating routine tasks. The benefits of AI may help reduce errors, detect fraud, and make faster decisions. 

On the downside, AI doesn’t have the same capability to reason and make predictions as a human employee can. It lacks the creativity that is often needed in dealing with insurance customers. 

Ethical concerns and costs are also considerations when deciding how and when to implement AI systems. 

As for the future, insurance companies will likely consider AI systems a work in progress.