Key Applications AI in Insurance

Justin Smith, CIO Review APAC | Wednesday, June 22, 2022

AI and ML give insurers that prioritise AI a competitive advantage over their competitors.

FREMONT, CA: Numerous manual operations in the insurance sector can be automated using artificial intelligence and machine learning. Insurance firms can provide faster services, assuring consumer happiness, as a result of AI advancements.

Insurance companies can employ various artificial intelligence technologies, including document processing, chatbots, and affective computing. They can use these technologies to multiple functions, like claims and appeals processing, customised insurance pricing, and fraud detection, to realise cost savings and a better client experience.

The primary use cases of AI in insurance:

Processing of claims: Multiple tasks are involved in claims processing, including evaluation, inquiry, adjustment, remittance, and denial. Numerous difficulties may arise while doing these tasks:

• Manual/inconsistent processing: Many claim processing procedures require human participation, which is prone to error.
• Varying data formats: Customers submit claims in a variety of data forms.
• Changing rules: Businesses must adapt quickly to the evolving regulations. As a result, these businesses require ongoing personnel training and procedure updates.

Pricing insurance: AI may evaluate a customer's risk profile based on lab testing, biometric data, claims data, and patient-generated health data and recommend the best pricing for the suitable insurance plan. This would reduce workflow in business processes, cost savings, and increased customer satisfaction.

Customer service: Insurance firms can benefit from conversational AI technologies by receiving faster responses to consumer inquiries.

AI technology applicable to the insurance business:

Deep Learning: With the growing volume of consumer data, insurance companies may develop machine learning models to assess customer risk profiles better and offer the most competitive insurance rates. This will result in significant cost savings and a complete grasp of client profiles. Additionally, deep learning algorithms are employed in various technologies, which we shall discuss below.

Chatbots: Chatbots can be crucial in consumer interactions. Since replying to client inquiries can be a tedious activity, chatbots can handle simple queries, freeing employees to focus on higher-value-adding activities. Additionally, chatbots can be utilised for intelligent call routing, which routes consumers to the appropriate agents based on their needs.

Connectivity to the Internet of Things and intelligent devices

With the growing popularity of IoT devices in people's daily lives, insurance companies will have more data to evaluate to assess consumer risk profiles better.

Affective computing: Affective computing, commonly called emotion AI, can better understand customers and take appropriate action based on their emotional states. This technology can be used in the following ways by insurance companies:

• Intelligent call routing: Dissatisfied consumers might be routed to more experienced call operators.
• Fraud detection: Insurance firms can use voice analytics to determine whether consumers better understand customers lying during the claim submission.