AI in insurance underwriting: A complete guide

In recent years, the use of artificial intelligence (AI) in financial services has become a hot topic in the financial services industry. AI is now being used in a wide range of financial applications, including banking, investing, and insurance. AI is particularly useful ‌in insurance underwriting, where it can help insurers assess risk more accurately and efficiently.

Underwriting traditionally was a slow and manual process based on subjective judgment. However, AI technologies like machine learning (ML) algorithms and predictive analytics are making it possible to automate routine tasks and find patterns in large datasets. This technology helps underwriters make better decisions about risk profiles, which creates better outcomes for both insurers and policyholders.

As AI adoption continues to grow in insurance underwriting, the industry is poised for a revolution. AI is paving the way for innovation and better customer service, helping insurers ‌stay ahead in a competitive landscape.

AI in insurance underwriting is the use of AI technologies to automate and improve the process of assessing and managing risk. AI can get a comprehensive view of a client’s situation by pulling credit reports or checking outstanding loans. When it comes to mortgage insurance underwriting, AI uses predictive analytics to better identify risk. It can research a property’s location, the housing market in general, and other relevant data, such as nearby sales or weather reports. With ML, AI can study an image or a video to quantify property features and condition hazards.

Once AI finishes analyzing, it can recommend lending and policy renewal opportunities. The human underwriter can use AI's recommendations, research, and rationale. Then, the human underwriter can make the best decision possible.

More than 65% of insurance claims and underwriting professionals plan to invest a substantial amount in AI, with many companies intending to spend over $10 million on AI technologies. When customers have negative experiences, it can lead to lost renewal revenue. Smart managers understand that when employees use AI, it can boost value for everyone.

Claimants face a variety of challenges when trying to settle their claims. These pain points include slow settlement speeds, a lack of personalization, and channel issues. AI help address these challenges.

• Speed: AI chatbots offer self-service resources and handle simple cases.
• Personalization: AI can review past interactions to suggest products or policies that fit the individual’s needs.
• Channel: AI works in omnichannel environments, so claimants can pick up the conversation anywhere, easily switching from desktop to mobile without losing their place in line.

Not only does AI benefit policyholders, but it also helps human insurance underwriters. Underwriters are often overburdened with manual tasks, including administrative work and basic claims-status checks. This can lead to employee burnout and productivity loss. AI can help by automating these tasks, freeing up underwriters to focus on more strategic work.

AI automation can handle simple tasks like sending renewal emails or prefiling data collection forms. Many AI chatbots know when a claim is complicated and ask for more information. AI can handle basic conversations and pass on more complex issues to customer service employees.

Underwriters gather information from various sources to make informed decisions and recommendations. They reach out to agencies and request paperwork from claimants. For life insurance policies, it can take a month for an underwriter to collect the necessary medical information. AI can expedite this process by collecting the materials.

Here’s how AI improves risk assessment accuracy for insurance claims management:

• Comprehensive data use: AI can analyze massive amounts of data that human underwriters might not traditionally include, such as social media accounts or behavioral patterns.
• Real-time updates: AI models can continuously update risk profiles based on real-time data and adjust recommendations.
• Risk scoring: By combining traditional metrics with new data sources, AI can generate more detailed risk scores.
• Dynamic adjustments: Policies can dynamically adjust as customer circumstances change.
• Fraud detection: With features such as proactive monitoring and anomaly detection, AI can help identify inconsistencies in applications.

Let’s take a look at how AI transforms each step of the insurance-underwriting process.

1. Application review: AI reviews applications and automates data collection by prefilling information using third-party sources, such as public records. It can send more complicated cases to human underwriters once it’s reviewed the application.
2. Property valuation: AI can use tools like satellite imagery to detect structural issues and measure dimensions. This is a cheaper, more accessible process that leads to precise valuations.
3. Risk assessment: Machine-learning models analyze historical claims, environmental data, and behavioral patterns to identify risks. Predictive analytics provide dynamic risk scores, enabling more accurate and fair policy pricing while reducing the likelihood of human error.
4. Decision-making and policy issuance: AI systems offer personalized recommendations for policy terms, premiums, and coverage options based on risk assessments and customer behavior. Human underwriters should always review this step for bias.
5. Policy renewal: AI monitors changes in customer behavior, property conditions, and external risk factors to recommend adjustments during policy renewals. Businesses can automate AI to send policy-renewal communications out to policyholders ahead of time.

AI is changing how property risks are evaluated and managed. With AI solutions, property underwriters can work more efficiently and accurately. AI can simplify the underwriting process and improve decision-making, helping underwriters to better assess and manage risks.

Underwriters have all the information they need to make decisions in one place, thanks to automated data collection prefill. AI extracts data from public records and historical claims to prefill applications, saving time on research. This reduction in manual input also minimizes human errors.

AI powers risk assessment models like predictive analytics, dynamic risk scoring, and scenario simulations. ML algorithms continually update outputs using the latest updates to the claimant’s life and the broader market.

Virtual surveys are an incredibly useful tool for assessing property conditions remotely. Using satellite imagery or drone footage, these AI-driven inspections can help you spot damage without having to send an adjuster to the site. This is especially helpful in cases where it’s not safe for a person to walk around the property, such as when there’s substantial roof damage. Virtual surveys are also more cost-efficient than traditional inspections, as you won’t have to pay for travel or other expenses.

Addressing the challenges of AI underwriting is crucial for ensuring the technology's effectiveness and reliability. By tackling key issues, insurers can build trust and maintain high standards in their underwriting processes.

Financial institutions using AI must be especially mindful of bias and fairness. Bias can lead to unfair decisions, like denying loans to specific groups of people or in certain neighborhoods. One way to address this is to train AI with a diverse team that looks at data from multiple sources. But even then, AI outputs will always need to be reviewed by humans. When adopting AI, look for trusted solutions with clear processes for eliminating bias.

In highly regulated industries such as financial services and specifically insurance, staying compliant with laws and regulations is critical. As technology evolves and cybercrime becomes more advanced, regulatory bodies and government agencies are always updating restrictions and guidelines. It's important to stay up-to-date on the latest changes to ensure your business is compliant.

AI can assist insurance companies in meeting regulatory compliance. It automates compliance checks, helping to ensure that underwriting processes follow the latest requirements. This reduces the risk of legal penalties. AI also maintains detailed records of decisions, which promotes accountability and transparency within the organization.

Innovations in generative AI are set to support underwriters with automations like automated policy drafting and compliance documentation. Additionally, it will also improve customer experiences through personalized marketing materials, renewal notices, and proposals. AI allows insurers to offer tailored products based on customer behavior and preferences, driving higher conversion rates. The future of AI in insurance writing is both highly technical and hyperpersonalized. Some emerging trends include:

• Real-time, dynamic underwriting based on live-chat support conversations using AI
• Personalized insurance experiences through integrated customer data, which helps inform and improve interactions based on behavior and preference
• Enhanced fraud detection due to pattern recognition through AI and other software during the intake process
• Ethical and transparent AI with more guardrails and government regulations to ensure less bias

^{_{Writers were aided by AI to draft these FAQ questions}}