Traditional vs. Generative AI: What’s Better for Insurance?

Generative AI has garnered great attention in the insurance industry because of its promising potential, but is it really that much better than Traditional AI? This article compares both to see which one might win out in the insurance world.

From Early Beginnings to Modern Innovations

Although technology in the insurance industry is now primarily defined by Generative AI (GenAI), that was not the case in the early days of automation. In the mid-20th century, traditional models focused on rule-based algorithms shaped how routine tasks in the industry were automated.

By the early 21st century, Machine Learning (ML) had entered the industry. This adoption of ML algorithms allowed insurers to improve fraud detection and personalize services by analyzing data sets to recognize patterns and autonomously make predictions or decisions.

Today, the industry’s excitement comes from more advanced AI techniques, like GenAI, which can generate everything from text and images to music and intricate designs in response to prompts. Through this evolution, we’ve shifted from basic automation to more dynamic and adaptive AI solutions for more sophisticated applications like risk modeling, customer interactions, and real-time data analysis.

AI’s Impact on Insurance

There’s no denying AI’s infinite potential in the insurance industry. The global AI in insurance market, valued at 44.59 billion in 2022, is projected to surge to $79.86 billion by 2032, growing at a compound annual growth rate (CAGR) of 33.06% from 2023 to 2032 (Precedence Research). The rapidly evolving technology has already produced impressive results, improving claim accuracy by 99.99%, boosting operational efficiency by 60%, and enhancing customer experience by 95% in leading U.S. insurance companies (Forbes).

As insurers use AI more frequently, more advanced models, like GenAI, are recognized for having more potential to revolutionize and fundamentally transform the industry than their classic counterparts.

Still, the question remains of whether Traditional AI (TradAI) is just as, if not more, advantageous in the insurance industry.

Comparing the Two

TradAI and GenAI represent two different approaches to artificial intelligence, each with its own set of rules, methods, and uses.

Traditional AI

Often dubbed ruled-based or deterministic AI, TradAI relies on pre-set algorithms and fixed instructions to handle specific tasks. It’s a champion in well-defined areas where it can follow explicit instructions and logical reasoning, making it perfect for repetitive tasks and precise data analysis.

Here’s a rundown of the key aspects of TradAI:

Predefined Rules: Operates based on fixed algorithms and rules.

Task Specific: Performs well in narrow, predefined tasks such as data entry, voice assistants, and recommendation engines.

Limited Adaptability: Struggles with ever-changing or complex environments, requiring manual tweaks to handle new situations.

Applications: Again, think voice assistants (Siri and Alexa), recommendation engines (such as Netflix), and search algorithms (like Google).

Traditional AI is a workhorse for streamlining operations and boosting accuracy in routine insurance tasks. It is a valuable tool for maintaining consistent efficiency and reliability in logical processing. However, while it’s a dependent tool, its rigidity and lack of adaptability are worth noting, especially in an increasingly dynamic industry.

Generative AI

GenAI is a game changer that moves beyond traditional rule-based systems to create new and varied content. Instead of following fixed algorithms, this AI model relies more on user prompts and large datasets. It’s versatile and adaptable, making it suitable for many different applications.

Here’s a quick overview of GenAI:

Content Creation: Based on user prompts, GenAI generates diverse types of content, including text, images, videos, music, and more.

Neural Networks: Utilizes advanced neural networks to create original outputs.

Adaptability: Highly versatile, applicable across various industries and tasks, from marketing materials to creative arts.

Data Analysis: Analyzes large datasets to understand patterns and generate new content that reflects those patterns.

Bias and Accuracy Issues: May produce biased or inaccurate outputs if trained on flawed or unrepresentative data, potentially leading to unreliable predictions or insights.

GenAI opens new possibilities for creating and analyzing data, offering great flexibility and creativity. However, it’s crucial to be mindful of its limitations, especially when ensuring accuracy and avoiding bias– critical factors in industries like insurance.

Benefits and Barriers to Both

A few notable differences pop up when you look at how GenAI and TradAI play out in insurance. Check them out below:

Functionality: TradAI handles specific tasks like fraud detection and claims processing through predefined algorithms, while GenAI generates customized policy recommendations, simulates risk scenarios, and analyzes complex claims

Applications: TradAI automates routine tasks, such as underwriting, policy management, and customer service, whereas GenAI can enhance risk assessment, assist in generating tailored policy advice, and improve decision-making through advanced analysis

Adaptability: TradAI is often rigid and needs updates for new types of fraud or risks. GenAI is more adaptable and capable of addressing evolving challenges and personalizing interactions.

Transparency: TradAI offers clearer decision-making processes and easier auditing, and GenAI can often be less transparent due to complex algorithms, making it harder to explain outputs.

Data Usage: TradAI relies on curated historical data for decisions and predictions. GenAI uses extensive datasets from various sources to generate new content and insights, requiring careful data quality management.

In summary, GenAI focuses on creating new content, while TradAI is all about analyzing and interpreting data. GenAI requires substantial computational power and extensive datasets, whereas TradAI tends to be more resource efficient.

Choosing the Superior AI

Generative AI undoubtedly brings something unique to the insurance world, but sometimes, you just can’t beat the classics.

GenAI is useful for things like assessing risks and personalizing customer interactions. However, its role in insurance is limited by its reliance on high-quality, interpretable data, task-specific applications, and inherent biases, which can lead to skewed risk assessments or unfair treatment of certain groups.

When it comes to the daily operations of insurance, TradAI, particularly pattern recognition models, might be more practical. It excels in key functions like claims processing and regulatory compliance. Its strength in analyzing historical data and making data-driven decisions makes it better-suited for the industry’s complex, data-rich environment, and it is often better at mitigating biases than generative models.

TradAI can enhance efficiency by automating repetitive tasks, reducing errors, and lowering operational costs. While GenAI brings innovative data creation and interaction methods, TradAI’s more stable approach often proves more central to the insurance sector’s core operations.