The insurance industry is cruising through a tech-driven shift driven by artificial intelligence, machine learning, and deep learning. AI can mimic cognitive functions associated with the human mind, including knowledge, perception, problem-solving, and reasoning. 

Machine learning and deep learning work together in association with AI to make data-driven decisions and predictions based on data. Let’s explore how these three technologies are helping insurance service providers to adapt according to the growing industry needs.

Integration of AI in Insurance and Its Outcomes

AI in insurance delivers several benefits from its capabilities, like automation, annotation, and data analysis. 

 

The wide availability of data is feeding the automation cycle in the house, car, life, property, and all types of insurance. Through this, insurance companies promote a user-centric approach to risk management, leading to enhanced decision-making, faster claims processing, and improved application process. 

• Pricing and Underwriting 

AI, which takes care of document reviews and real-time analysis, can guide insurers in providing accurate prices. Where it can streamline the manual tasks, back testing and model validation strategies are useful for real-time risk monitoring. 

 

Trained AI models can assess a property and surrounding areas with satellite imagery to calculate the area accurately. While processing the insurance benefits, it can also identify proximity to trees, water, and all the other factors influencing the property’s risk profile. Using this information, insurers can provide a wholesome estimate. 

 

• Automate Insurance Processes

AI-enabled solutions can be trained to assess risk efficiently and detect fraud. Using this information, they can determine insurance coverage amounts and optimize the process, reducing human errors. 

 

Computer vision systems embedded into the insurance provider’s network can produce damage estimates quickly. Using the instant claims benefit, all operations can be streamlined, reducing errors and optimizing customer experience. 

• AI-based Annotation for Insurance Services

Insurance companies use data annotation to train machine learning models and optimize their performance according to their requirements. These models can be set for each type of insurance, which often requires a varied underwriting, processing, and management process. 

 

Using AI, insurance companies can use data annotation to label and identify specific data. This includes insurance applications, medical records, property information, past history, customer sentiment, claims, etc. The companies can process all sorts of applications by first training machine learning models to identify the required information and fast-track the process. 

• Optimized Claim Processing

Claim processing is a complicated process. Using AI’s automation capabilities, agents can automate the task to speed up the work and reduce errors. AI-based claim processing is facilitated by annotation and data analysis. 

 

Insurance providers are utilizing a wide range of digital devices and connections to gather data. They hook up smart home assistants, wearables, fitness trackers, telematics, and similar devices to dedicated applications. 

 

The insurers can use comprehensive insights to streamline the underwriting process through pre-defined rules and conditions manifested into the AI programs. 

• Fraud Detection and Prevention

According to the FBI data, non-health insurance fraud amounts to over $40 billion in a year. Due to this, the premiums increase by $400 to $700 per family every year. 

 

With AI in insurance, it’s easier to detect fraud committed through padding, inflating actual claims, and misrepresentation of facts. AI can analyze vast amounts of data, pinpoint suspicious patterns, and prevent beneficiaries from seeking unnecessary claims. 

 

Consequently, insurers can mitigate risks and minimize financial losses while maintaining the integrity of their operations. 

• Damage Estimation and Analysis

AI can help with damage estimation of any sort of property through bounding box annotation and semantic segmentation. Here’s what they mean;

 

• Bounding Box Annotation 

It’s the process of labeling images with a box bordering a specific object. The annotated data is fed to machine learning and computer vision algorithms for fast-tracking damage estimation on cars and other insured properties. 

 

The smart programs can precisely examine the property condition and detect dents, damages, scratches, cracks, etc. As a result, it can improve claims processing and premium prices effectively. 

 

• Semantic Segmentation

This technique leverages computer vision and deep learning to categorize every pixel in an image. It creates a pixel-wise segmentation map, enhancing object tracking and identification. 

 

In insurance processes, semantic segmentation is used to filter out precise information and images to make accurate decisions. It can also highlight the specific habits of insurance buyers, thereby helping with setting optimal premiums and settling claims. 

• Efficient Customer Service

Natural language processing (NLP), another subset technology of AI, can streamline communication between contact centers and customers. It can improve after-hours customer service through conversational AI Interactive Voice Response systems and help deploy chatbots. 

 

Chatbots and IVR systems can be trained with company policies, shared knowledge, and past customer interactions to answer their inquiries for faster issue resolution. 

 

Moreover, insurers are using AI to understand their customers better, predict their behavior, and deliver personalized solutions that lead to higher customer retention. It empowers insurers to take proactive steps according to customer sentiment, enhancing customer experience. 

Conclusion

Artificial intelligence has some sweeping advantages for the insurance industries, provided the companies understand its implementation. Where tasks like claim processing, premium setting, and damage detection are streamlined, it can also help with customer service, increasing the overall satisfaction level. 

 

At Shaip, we are helping insurance companies take the first step towards AI in insurance through our data annotation services. Reach out to us if you want data annotation for images, text, videos, and audio files to improve AI and machine learning models.