How AI Enables Property/Casualty Insurers to Achieve Operational Excellence

As AI technology continues to evolve, we can expect to see even more advancements in the insurance industry, benefiting both insurers and policyholders alike.

(Image credit: Giorgio Trovato/Unsplash.)

Automation has been a huge topic in insurance for more than a decade—with mixed success. However, recent leaps in AI technology have made it easier for organizations to automate basic tasks in the claims process, allowing them to improve operational efficiency, increase accuracy, and enhance customer service.

With AI—augmented intelligence, that is—claims professionals glean insights from large volumes of data which they can then use to quickly process claims with minimal human intervention, freeing up staff to focus on more complex tasks.

Let’s explore how insurers can use AI throughout their business to optimize claims outcomes.

What Is Augmented Intelligence?

While AI generally stands for artificial intelligence, referring to the technology that replicates human intelligence, augmented intelligence is the technology that helps humans to do their jobs more efficiently by handling the simpler tasks that are easy to automate and creating insights from complex data interactions that a human may miss.

Insurance companies are using AI in various processes such as underwriting, risk management, claims processing, and customer service. These companies are using AI-powered algorithms to analyze historical data, identify patterns, and make predictions. They are also using augmented intelligence to automate manual processes such as data entry, claims processing, and underwriting.

Here are six use cases for how property/casualty insurers can benefit from using AI.

Use case 1: Straight-through claims processing

With the help of AI, insurance companies can automate claims processing, enabling them to handle large volumes of data quickly and accurately. By analyzing claims data, AI can accurately determine the validity of claims, reducing the time required to process claims and minimizing errors. The automated straight-through process is efficient, saves time, and enhances customer satisfaction by providing a fast and seamless claims process.

Use case 2: Automated underwriting

Unlike the average human, AI can quickly analyze extensive data such as health records, demographic information, and lifestyle habits to assess the risk level of an individual or business, leading to personalized insurance products. The automated process is quicker, more precise, and cost-effective, providing insurers with a better opportunity to offer competitive pricing.

Use case 3: Fraud protection

In the same way, AI can detect fraudulent behavior by analyzing large amounts of data and identifying unusual patterns or anomalies. It can compare claims data with historical data and flag potentially fraudulent claims for further investigation, helping insurance companies to reduce the risk and save money.

Use case 4: Improved customer experience

Insurance companies can use AI to offer personalized recommendations by analyzing customer data, location and even voice to recommend services and products that cater to their specific needs. Additionally, AI can power convenient and efficient channels for interaction with insurance providers, such as chatbots with generative AI capabilities, enabling customers to resolve queries quickly and efficiently. 

Use case 5: Optimized pricing

That vast data analysis means that AI can also determine the risk level of an individual or business, allowing insurers to price their products accordingly. This capability enables insurers to offer competitive pricing, optimize profitability, and increase their customer base.

 Use case 6: Data sharing

A great way to get industry benchmarking and intelligence is to partner with an AI provider that uses a contributory data model. Sharing claims data with a contributory database improves AI models by providing them with more data to learn from. Contributory databases enable insurers to access a vast amount of data, allowing them to develop better AI models that can improve the industry as a whole. By sharing their claims data, insurers can contribute to developing better models that benefit the entire industry, but more importantly, they can get insights beyond their own data. These insights are valuable in measuring operational performance, managing TPA relationships, and gaining insight into new markets. 

What Is a Contributory Database?

A contributory database is a collection of data provided by participants to a central repository that is anonymized and used to train AI models and benchmark performance. This can prove valuable given the historical claims data provided which enables insights that are difficult for sole carriers to replicate.

Training AI models on millions of closed claims enables unmatched prediction accuracy and a depth in benchmarking that gives the users insights into new markets. A contributory database can provide an operational edge that improves when additional carriers, MGAs/MGUs, reinsurers, or self-insured entities partner together.

Revolutionize P&C Claims Processing

AI has advanced the insurance industry greatly, providing insurers with the ability to automate processes, improve accuracy, and enhance customer experience. As AI technology continues to evolve, we can expect to see even more advancements in the insurance industry, benefiting both insurers and policyholders alike.

Augmented Intelligence is a Second Set of Eyes on Casualty Claims

 

Tyler Jones // Tyler Jones, Chief Marketing Officer at CLARA Analytics, has two decades of experience in the insurance and banking industries. He is responsible for directing CLARA’s complete relationship with its customers and driving efforts to assess and elevate experiences at each touch point across the customer journey. His experience as an innovator and strategist includes roles in which he successfully transformed customer experiences and enterprise operations by harnessing the power of data alongside digital apps, social platforms, and artificial intelligence. Follow CLARA Analytics on LinkedIn, Facebook and X (formerly known as Twitter).

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