Guide to AI for Insurance

With the explosion of AI across every industry, the hyper-competitive business space within the insurance industry is making the adoption of AI a foundational necessity. Improved operational efficiency and enhanced customer experience are the key outcomes of this technology. Today, 87% of surveyed insurers already see their companies invest $5 million or more in AI technology each year, and 74% of insurance executives plan to increase their investment in AI. However, most (78%) of these insurers lack a clear, documented strategy and the in-house capabilities to operationalize AI at production levels.

Insurance companies worldwide seek to leverage AI for claims processing, fraud detection and prevention, insurance pricing, and overall operations management. With this technology and a well-defined strategy, insurance companies can scale their services, provide enhanced customer experiences, and capture game-changing improvements.

McKinsey estimates that AI can deliver $1.1 trillion in potential annual value for the insurance industry across various functions and use cases. With recent advances in the field, the potential applications for AI are enormous. For example, large language models like ChatGPT have broken into the mainstream, making it possible to automatically identify the semantic intent of conversation and generate accurate, effective human-like responses through language-based applications. Additionally, the increased amount of raw data available through Internet of Things (IoT) devices and autonomous vehicles has promoted the development of even more complex models. Applying these models, companies have been able to develop improved safety features to reduce accident frequency, as well as to better assess and adjust rates following insurance events. Additionally, early adopters have been leveraging AI to provide personalized offers to individual customers.

Insurers must be able to process immense volumes of data from disparate sources and make complex predictions every day. As such, the industry has virtually unlimited opportunities to leverage these new technological developments. Insurers can significantly accelerate processes by using AI to aid in ingesting raw data and generate predictions at a new level of speed and volume.

Strategically, adopting AI into their business can be highly beneficial to insurance companies. These benefits include:

1. Increasing efficiency: AI increases the efficiency of time-consuming processes such as underwriting, claims management, and customer service. 
2. Improving accuracy: By automating tasks, AI reduces human errors tied to manual processes.
3. Enhancing fraud detection: AI allows companies to stay current with advancements in fraud prevention and in detecting sophisticated fraud patterns.
4. Improving customer experience: By leveraging consumer data, insurers can use AI to provide customers with customized, accurate coverages and pricing.

Conversely, deciding not to apply AI poses a significant risk to insurers, preventing them from keeping up with changing customer needs and allowing for uncompetitive operational inefficiencies. These risks and challenges include: 

1. Manual processes are too slow: Many insurance systems are manual, paper-based, and require much human involvement, leading to long wait times and expensive delays. 
2. Premium rates and coverage offerings are not effectively customized: Without providing customers with customized premium rates, the premiums they are charged may not be accurate or competitive. Customizing these policies allows insurance companies to adapt to the specific customer and their needs.
3. Fraud is prevalent: As fraud becomes even more sophisticated, it is more complex and costly for insurance companies to combat. Manually identifying fraud simply isn’t scalable, and it drives an uncompetitive rate structure.
4. Compliance with regulations is difficult: Insurance companies must comply with myriad regulations to adequately protect customer data, including personally identifiable information and health data.

In this guide, we’ll explain how the insurance industry can use AI to solve these challenges. We’ll discuss the top uses for AI and provide a detailed overview for implementing AI within your organization.

There are numerous applications for AI in insurance. In this guide, we will focus on the top three use cases as identified in our 2023 Zeitgeist AI Readiness Report:

1. Accelerated claims processing
2. Claim fraud detection and prevention
3. Risk assessment and underwriting

AI-Accelerated Claims Processing

During claims processing, insurers must check a claim for information, validation, and justification. Once that claim is approved, the insurance company then proceeds to process the payment. However, there are challenges to performing claims processing efficiently. Claims processing is often performed manually, making it prone to errors and inefficiencies. This drives up operating costs and creates regulatory and competitive challenges. Because of the complexity and volume of data involved in processing claims, this is a key area of opportunity for AI innovation.

Recent advancements in Generative AI technology have made it possible to democratize internal access to insurance companies' policies, documentation, and claims information. By putting a comprehensive knowledge base at the fingertips of claims adjusters to query for case details, company guidelines, and more, insurers are accelerating settlement times and improving adjuster decision-making.

Deploying cutting-edge AI tools like Scale’s Enterprise Copilot helps deploy these Generative AI tools quickly and securely, customized on an insurer's proprietary and sensitive data. Insurers can stand up multiple versions of these copilots, with enterprise-grade security and role-based access controls to ensure the right stakeholders can access the right data. Further customizing these solutions with domain-specific fine-tuned models helps insurers build a proprietary and competitive asset that enables significant operational and settlement efficiencies.

There are several other areas of the claims process where insurers can leverage AI, including initial claims routing, claims triage, and claims management audits. Examples include:

1. Accelerating administrative processes: By using AI to automatically route claims, these claims can be resolved quickly, providing optimal value. Additionally, damage severity can evaluated programmatically from claims reports. Claims can even be validated against external data sources, such as weather reports.
2. Claim and customer segmentation: Claims and customer information can be automatically segmented and loaded into an intelligent search engine, making information easier to organize and find, and can be utilized towards pricing and growth efforts.
3. Create new insurance policies: Insurers can use AI to automatically create new, customized insurance policies based on internal, customer, third-party, and public data. This allows insurers to deliver a tailored range of insurance products.
4. Unlock insights: By using AI to analyze claims data and attributes, managers can better understand claims patterns, guiding managers to take appropriate actions.

This solution involves implementing AI-powered intelligent document processing to review the claim, verify policy details, and perform fraud detection. Computer vision can also be used to assess the cost of damage by analyzing input data such as images and videos. These claims can then be stored, allowing insurers to easily search through historical claims using large language model-based search. After the claim is approved, the process can automatically issue electronic payments.

AI solutions increase the operational efficiency of an insurer, resulting in a significantly improved competitive position. To help implement improved predictions of claims outcomes, we offer the industry Scale’s Claim Intelligence. This platform can effectively manage the complexity of claim breakdown, ingest and process data from multiple models, and provide detailed data and analysis through its intelligence engine.

Claims Fraud Detection and Prevention AI

Preventing and detecting claims fraud is another difficult and time-consuming process. Data-based deception and malicious agents are increasing across the industry, and malicious content is becoming more sophisticated and very hard to detect.

Insurance companies often employ risk modeling for fraud detection to address this challenge. With AI, insurers can rapidly identify the variables and factors that pose the most risk and more effectively prevent fraud. Accurate fraud detection and prevention improves insurer competitiveness and ensures accurate, legitimate payouts. This risk-modeling technology often involves various techniques, including text analysis, logistic regression, and predictive analytics.

With this advanced technology, companies can detect unseen patterns and markers of fraudulent claims. This enables these companies to become a more challenging target and increase their efficiencies while helping to manage loss ratios. 

Risk Assessment and Underwriting with AI

Insurers must evaluate and analyze the risks involved in insuring people and assets when providing insurance pricing. By determining the risk of issuing coverage to a person or business, the insurer can set the insurance premium they charge.

Without AI, this process is historically inefficient since application processing requires extracting information from detailed documents. When insurance companies rely on optical character recognition, this process is manual, time-consuming, and error-prone. Customer documents often include different data formats, requiring manual review. Additionally, because regulations for document processing change frequently, insurers often need to update their processes.

Scale’s AI technology can manage a variety of tasks such as determining optimal rates for customers for risk management, reducing the time needed to introduce new pricing frameworks, and building data-informed insurance policies. AI can automate the process of insurance underwriting using machine and deep learning models. Companies will extract relevant data from insurance documents with intelligent document processing. AI is also applicable to improve customer service – Automated customer service apps, also called conversational AI, can handle policyholder interactions and create personalized quotes. AI solutions can predict premiums from previous risk assessments to make risk assessment more precise and enable predictive modeling for dynamic pricing.

When companies implement AI for any use case, it’s important to follow a framework based around AI strategy. This framework involves identifying use cases, prioritizing approaches based on their impact, and understanding the necessary technology. Most importantly, insurance companies should tie their AI strategy to corporate strategy. Our annual Zeitgeist: AI Readiness Report, which surveyed over 1,600 ML practitioners and business leaders, found that an organization’s goals shape the effectiveness of its AI implementation. Insurers must ensure the goals of an AI implementation, such as reducing lost costs from claims payout, improving top-line growth, or enhancing customer experiences, are aligned with company priorities. 

By building an AI solution incrementally, your company can design AI that serves its specific objectives. We suggest adhering to the following steps throughout the implementation process:

1. Outline key challenges: What are your company's challenges? Are they claims-related, top-line growth or growth against other insurance players?
2. Develop a business strategy for implementation: What do you specifically need to accomplish with the AI solution, and how will you measure its success? What are the key performance indicators you intend to monitor? Who is accountable for the success of the program?
3. Understand the available technology: What are the best and most current AI-related technologies that address these challenges and business strategies?
4. Start with a strong data strategy: Before diving into building a solution, consider the data you have available, as well as any data collection you may need to perform. Determine the type, quantity, and quality of data that you have. If you don't have strong in-house data science or AI expertise in-house, consider working with an experienced third party to help you define and execute your data strategy.
5. Build a “crawl, walk, run” methodology: Start small by addressing a specific challenge or customer need when building an AI solution. Then, move quickly and conduct short-term tests on various solutions using proof of concept implementations or product pilots. From there, you can expand the mission to incorporate additional use cases that align with company priorities.

Read the guide Generative AI for the Enterprise: From Experimentation to Production for more detailed steps on implementing Generative AI.

This guide covered the most prominent use cases and applications for AI in insurance. The AI revolution is underway, and it’s already changing the competitive landscape. It’s arguably essential that insurance carriers meet this moment by rapidly investing in AI innovation and process-model evolution. Achieving key corporate objectives will require a well-planned strategic investment in cutting-edge AI innovation.

If you have found this guide informative and want to learn more about how to rapidly and effectively apply AI, Scale AI stands ready to support your efforts. Scale EGP (Enterprise Generative AI Platform) is a proven product that provides cutting-edge Generative AI solutions to generate enormous business value. Scale Spellbook is a great way to get started with building, comparing, and deploying large language model apps. Scale Claims Intelligence is designed to help you predict the future outcome of claims to help you streamline claims management.

Most importantly, start learning more about how AI can help your company and build an AI strategy today.

1. Scale Claims Intelligence AI
2. Generative AI for the Enterprise: From Experimentation to Production
3. Scale Zeitgeist: AI Readiness Report
4. Scale Spellbook
5. Guide to Data Labeling