Insuranceciooutlook

Key AI and Machine Learning Use Cases in Insurance World

Insurance CIO Outlook | Wednesday, November 24, 2021

Insurers must ensure that claims meet requisite criteria throughout the process cycle, as dictated by policy and legal requirements. Dealing with thousands of claims and customer inquiries is understandably a difficult and time-consuming task.

Fremont, CA: The application of artificial intelligence (AI) in insurance has been hailed as one of the most groundbreaking developments, resulting in significant economic and societal benefits that ultimately improve risk pooling and risk reduction, mitigation, and prevention. Through automation, insurance companies can respond to requirements on time and make sure they can deliver high-quality service to the customers they promise.

Claims Fraud Detection

According to a Federal Bureau of Investigation study of US insurance companies, the total cost of insurance fraud (non-health insurance) is close to $40 billion per year. That means that insurance fraud costs the average American family between $400 and $700 per year in increased premiums. These startling statistics highlight the critical need for highly accurate automated theft detection tools to help insurance companies improve their due diligence process.

Claims Adjudication

According to the Council for Affordable Quality (CAQH) Index, automating eligibility and claim verification can result in an annual savings of $ 5.2 billion in healthcare insurance alone. With the help of a chatbot that interacts with customers and collects the necessary information, the claim initiation automation process saves time for insurers. Information can be captured in a structured format using chatbots, and a first-level validation can be performed during the claim initiation process. Computers will handle 62 percent of an organization's data storage and data processing by 2022, as per a World Economic Forum (WEF) study. With the expansion of automation, investing in auto-adjudication systems will help organizations stay relevant in the near future.

Claims Processing

Insurers must ensure that claims meet requisite criteria throughout the process cycle, as dictated by policy and legal requirements. Dealing with thousands of claims and customer inquiries is understandably a difficult and time-consuming task. Machine Learning improves the overall efficiency and effectiveness of the process. It significantly improves the claims process value chain by moving claims through the initial report, analysis, and finally contacting customers. The procedure saves time and allows employees to devote their attention to more complex claims and direct customer contact.

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