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How AI Enables Casualty Insurers to Achieve Operational Excellence 

Man in office using computer

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.

Learn more in our white paper>>>

Use cases for AI in casualty insurance

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 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. CLARA customers provide historical claims data to our leading Contributory Database Model that 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. This contributory database model gives CLARA users an operational edge that improves when additional carriers, MGA/MGUs, reinsurers, or self-insured entities partner with CLARA.

Learn more about how it works>>>

Case study: QBE

Several years ago, the Australia Pacific division of QBE faced challenges with their claims adjusters, who were struggling with heavy caseloads and a rudimentary triage system based on a single criterion. 

To improve the process, QBE’s leadership team explored using advanced data analytics and settled on a purpose-built artificial intelligence/machine learning (AI/ML) solution to drive the triage process towards greater efficiency. 

Claims adjusters were initially skeptical, but they have come to appreciate having an intelligent assistant to make recommendations based on highly refined analysis of cases. QBE has estimated achieving a return on its investment in AI/ML tools of at least 500% and has since rolled out the same technology to improve its efficiency and effectiveness in handling auto liability claims.

The success of QBE’s implementation highlights the value of AI/ML in managing large caseloads and is equally applicable to other stages of the claims management value chain. The AI/ML-powered processes included: 

  • Routing high-risk claims to the most experienced adjusters, 
  • Guiding claims managers to the medical providers most likely to produce optimal patient outcomes, and
  • Helping insurers minimize litigation costs by intelligently scoring cases based on overall litigation risk, identifying which attorneys are likely to yield favorable vs. unfavorable results, and alerting claims managers when promptly settling a case is likely to produce the best outcome.

Insurers are increasingly recognizing that AI is a powerful tool that can help them streamline their claims processes, identify and prevent fraud, optimize pricing, and deliver better outcomes for claims professionals—giving them a powerful return on investment. 

Find the full QBE case study in the white paper, Supercharging Claims Management>>>

Revolutionize casualty claims processing with CLARA Analytics

AI has revolutionized the insurance industry, 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.

CLARA Analytics improves claims outcomes in commercial insurance with easy-to-use AI-based products. CLARA’s predictive insight gives adjusters “AI superpowers” that help them reduce claim costs and optimize outcomes for the carrier, customer and claimant.

Learn more about our suite of award-winning Augmented Intelligence products>>>

Team CLARA Analytics

CLARA Analytics is the leading AI as a service (AIaaS) provider that improves casualty claims outcomes for commercial insurance carriers and self-insured organizations.

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