The Impact of AI on Insurance: 18 Top Use Cases You Must Know
Published: 2024-02-15 12:02:09

## Introduction

The insurance industry is on the brink of a significant paradigm shift.

From risk assessment and insurance underwriting to claims management and customer engagement, disruptive AI applications are transforming the very fabric of the insurance landscape.

But that’s not all. Beneath the surface, profound advancements in AI are underway. Personalized risk evaluation, dynamic pricing models, real-time damage analysis, and automated claims settlement – are crafting a new narrative.

Let’s delve into the statistics -

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McKinsey says AI in insurance is poised to boost productivity and slash operational costs by up to 40%.

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A survey quoted that 21% of insurance companies are preparing their workforce for AI-based systems.

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According to Forbes, AI in insurance has led to a 99.99% enhancement in claims accuracy and a 95% improvement in customer experience.

These statistics paint a compelling picture of the use of AI in the insurance realm. However, as mentioned earlier, we have barely scratched the surface. With the seamless integration of connected devices, telematics, IoT, cognitive computing, and predictive analytics, we are glimpsing into a future ruled by [intelligent workflows in insurance](https://marutitech.com/insurance-workflow-automation/).

This blog contains the top 18 AI use cases and applications every insurer must know in 2024.

### Real-World Applications Of AI In Insurance

The transformative potential of AI is steering the insurance industry away from the age-old "detect and repair" approach towards an adaptive "predict and prevent" strategy. 

These 18 real-world artificial intelligence insurance use cases vividly demonstrate this paradigm shift.


![Real-World Applications Of AI In Insurance](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ryq915sdkz31yo1f5jc2.png)

#### 1. Policy Servicing
The most predominant complaint among insurance consumers revolves around issues tied to policy servicing. Policy servicing refers to a range of tasks and interactions after a policy is issued. For example - policy amendments, premium payments, claims processing, policy renewals, customer inquiries, and more.

Integrating AI into the policy management system can significantly optimize operational efficiency. It can expedite processes, reduce manual labor, enhance accuracy, and elevate the overall customer experience.

By leveraging RPA in insurance, companies can automate the most tedious administrative and transactional tasks, including accounting, settlements, risk assessment, credit control, tax preparation, and regulatory compliance.

One prime example of this is WorkFusion. WorkFusion blends AI and machine learning techniques to analyze various documents and facilitate the automated intake of policy data. This lessens the manual effort needed to discover the pertinent fields for policy endorsements.

#### 2. Insurance Distribution
Gone are the days when insurance distribution was a door-to-door sale.

Consumers are rapidly moving towards online platforms for insurance policy research, comparison, and informed decision-making. Forward-thinking insurtech companies are seizing this opportunity to revolutionize the insurance distribution phase.

**Read the full article**: _[The Impact of AI on Insurance: 18 Top Use Cases You Must Know](https://marutitech.com/top-ai-insurance-use-cases/)_