Ai Insurance Industry Starts With Gold Standard Labeled Data

Artificial Intelligence has permeated every industry and impacted the future of almost all businesses. The ability of the technology to replicate human intelligence quickly and reliably has opened up several new doors: right from automating business processes to making evidence-based decisions for long-term growth and efficiency. 

In the insurance industry especially, AI plays a huge role in cutting costs, enhancing the customer experience, and boosting sales. But if you want to reap maximum gains on your AI initiatives, you need to tick all the right boxes, especially with respect to data quality.

Why Are More And More Insurers Embracing AI Innovations?

As insurers around the world come face-to-face with countless operational challenges, many are turning to AI to optimize costs, improve service efficiency, and boost sales. According to IDC, global spending on cognitive and AI systems will reach $77.6 billion in 2022, with a significant amount directed to conversational AI applications such as chatbots and deep learning.

These investments are also expected to save auto, property, life, and health insurers almost $1.3 billion, drastically bringing down the time needed to settle claims while improving customer loyalty.  In the insurance industry, AI is enabling companies to:

  • Automate claims processing tasks, standardize data formats, and ensure alignment with evolving regulations. 
  • Accelerate document processing by streamlining mundane and error-prone tasks while also mitigating risks and challenges.   
  • Proactively identify fraudulent activities, and thwart fraud attempts in real-time, while also predicting claim values. 
  • Drive effective lead generation, automate marketing campaigns, and identify products and services for long-term profitability.
  • Provide suggestions and recommendations on strategies to adopt to optimize business, target leads, and model workflows. 
  • Improve the efficiency and accuracy of customer interactions using chatbots, and allow employees to focus on value-adding activities.

Why Does Gold-standard Labeled Data Matter?

AI, with its far-reaching capabilities, is set to transform the insurance sector with continuous advances in analytics and deep learning. However, insurers looking to introduce AI into their business have to be extremely cautious about the quality of data.

Since AI algorithms are only as good as the data that is fed to them, relying on the right data collection, cleansing, and storage mechanisms is critical to the success of AI in the business. If you expect AI models to be extremely efficient with the results they produce, you need to provide them the data that is accurate, truthful, and reliable. 

Gold-standard labeled data refers to data that has been carefully prepared and verified and represents the objective truth as closely as possible. Such unambiguous and correct data is essential for AI models to recognize patterns, identify trends, and uncover insights. 

In today’s era where data is being generated at a tremendous pace, a lot of this data often covers different aspects of the same real phenomena or presents data in different formats for relatively similar things. For AI algorithms to understand that the data represented by two different applications or systems is the same, it needs to have the capability to make that conclusion. 

For example, consider that the data from source A states that an accident insurance claim by a customer on 11 February 2022 amounts to $2.5k. At the same time, another dataset for the same claim from source B states that a customer made an insurance claim for a motor accident in February 2022.

Notice that while the first data set mentions the date and the amount; the second mentions neither – but both refer to the same claim. If the AI algorithm is not trained to refer to both these data sets as the same transaction, it can lead to several losses for the company. To help AI systems make sense of all such similarities (or ambiguities), it is important insurers rely on gold-standard labeled data. 

Here’s why gold-standard labeled data is the only way insurance companies can get maximum returns from their AI initiatives:

  • Infuses Real-World Logic:

    Gold-standard labeled data uses numerous advanced techniques to identify, match, and merge data records referring to the same or similar entity in multiple datasets. By merging information from different sources and linking it to the same transaction, gold-standard labeled data helps infuse real-world business logic into data-linking rules. For example, gold-standard data helps in creating training and evaluation sets for AI platforms, especially when insurers want to extract information from free text, allowing them to link data based on individual contexts.

  • Avoids Bias

    Gold-standard labeled data often goes through several rounds of verification, thus avoiding any biases and points of view. Since the data ensures a sufficient level of agreement between AI algorithms and human experts, it aids in setting clear guidelines for AI algorithms while also constantly calibrating data for higher accuracy. For instance, in the realm of claims processing, gold-standard data can help avoid insurers from segmenting people based on their race or gender, and approve claims solely based on their credit scores. 

  • Helps Specific Degree Of Similarity

    Another major benefit of using gold-standard labeled data is enabling insurers to determine how similar two data sets generated by two different data sources are. Say, for example, during the customer onboarding process, customer-related information might be stored in disparate systems. If this data is the gold standard, companies will be able to specify the degree of similarity between two or more data sets – helping them fine-tune the algorithms even further to build a clear, accurate, and holistic view of customers and accelerate the onboarding process. 


In the world of AI, gold-standard data is quickly becoming the building block on which insurance companies can base their decisions. By continuously preparing and verifying data for accuracy, reliability, and completeness, such trustworthy data sets can allow insurers to unearth the right patterns, find the right links, and make the right decisions. Want to explore how EnFuse Solutions can assist in your next project? Contact us today.