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June 9, 2023

Insurance Automation: AI Implementation Use Cases

June 9, 2023
Read 6 min

AI-based software is ideal for menial, repetitive tasks that consume a lot of production time and take a toll on human workers. The insurance industry, for instance, has numerous manual routine tasks that are prone to human errors. That’s why insurance processes should be automated with the help of AI and machine learning.

AI in Insurance

AI-Based Software Benefits for Insurance

Streamlined Processes = Time and Cost Reductions

Insurance processes involve a lot of data and have a high potential for errors. Insurance automation is an efficient way to collate and process all that data, and can eliminate repetitive tasks like manually inputting customers’ information into different systems and performing manual calculations. Using AI insurance software for such routine tasks guarantees accurate results in significantly less time than it would take an employee. This means that you need to hire fewer employees to perform these activities.

Improved Customer Experience

AI in insurance is widely used in conversational bots, or chatbots, that can already understand customers better than humans. This software automates the most popular customer service interactions, helping businesses provide better service and support. The market is full of different chatbots, but it is essential to choose or create one that is insurance-focused. This will create more value for both customers and insurance business owners.

High Performance

High performance is a direct result of increased quality and speed of Insurance claims processing and reduced fraudulent claims because it’s tracked and alerted by the AI-based claims management system. And again as one more result here we get cost and time-saving for the Insurance businesses — automating menial tasks, claims processing, and fraud detection.

Secure Business Management

Developing an insurance agency management system is essential for those Insurance companies that have big plans. All insurance businesses deal with high security and accurate data that is the cornerstone of the whole industry. Ready-made and custom insurance management agency platforms are able to cover essential security and data accuracy needs.

AI Implementation Use Cases in Insurance

Claims registration and processing

When clients submit insurance claims, a lengthy process ensues. From the clients’ perspective, they want fast and uncomplicated service. However, providing fast and uncomplicated service can be a tall task if everything is done manually.

The process of supporting insurance claims involves processing large volumes of data. Further review, research, and investigation are needed to substantiate filed claims. These tasks contribute to the exhaustive amount of data involved in this area of insurance. AI can automate this repetitive and error-prone work, saving companies time and costs.

It’s worth noting the possibility of fraudulent insurance claims. While most claims will be legitimate, some may be questionable or even fraudulent. Fortunately, various insurance software is available that can detect fraud, process data quickly, and ensure a smoother transaction that satisfies both the insurance company and its clients. If you’d like to learn more about the cost of implementing an insurance claims management system, you can check out this article.


Underwriting involves evaluating a client’s risk profile in relation to the policy they want or have applied for. This is the stage where an insurance company will examine a client’s risk profile, considering factors such as their health, smoking and drinking habits, and financial situation. They will consider the client’s current income and their potential benefits to calculate insurance premiums.

Automating insurance underwriting can save time spent on collecting and entering data into the various fields in underwriting forms. These fields will auto-populate, and automated systems can produce reports and make recommendations based on previous claims or losses by the client. This can significantly shorten the time involved in completing the underwriting process.

Policy management (admin and servicing)

The entire process, from pre-underwriting to underwriting to servicing, is ripe for automation. Currently, the process involves a lot of manual, labor-intensive, and repetitive tasks.

Loads of documents that include particular information about the insurer are generated by insurance companies on a regular basis, and creating them manually gives it so much room for errors. Document management systems enriched with machine learning help extract data needed from various sources and automatically update forms — and therefore create statements and documents without mistakes.

Fraud detection

AI’s ability to analyze massive amounts of data makes it a valuable tool for fraud detection. AI insurance software can identify patterns and unusual behavior that humans might miss, which helps insurance companies reduce fraudulent claims. However, artificial intelligence should not be the sole method used. Manual audits and investigations should also be employed to ensure accuracy. Additionally, AI algorithms may produce false positives or false negatives if not monitored. Despite these limitations, artificial intelligence insurance software is widely used for detecting and preventing fraud.

Customer service

AI-powered chatbots are commonly used for customer service. These bots can provide basic assistance and answer frequently asked questions in a conversational manner, mimicking human interactions. AI is also used for customer data analysis to enable data-driven decisions and provide personalized recommendations for customers.

By collecting data on customer behavior (e.g. past purchases, search history, demographic data, etc.), identifying patterns and trends, and enabling insurers to offer tailored products and services to their clients. Businesses can utilize AI to predict customer behavior and address any potential issues before they arise, which helps improve customer experience and avoid any drawbacks.

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AI Models for Insurance

Usually, AI-based software can be trained from scratch using a Machine Learning framework or purchased as a pre-trained model. Pre-trained models are typically specialized in certain areas, such as voice or image recognition, text analytics, biometrics, sentiment detection, decision management, or document processing.

Recommendation Engine

The system analyses the records and results and then suggests the appropriate options. For example, we have developed a recommender engine that assessed clients’ information and suggested the best-fitting insurance plan.

Text Analytics and NLP

Text analytics techniques allow analyzing the text of insurance claims, settlement notes, etc. NLP can be of help in detecting claims that are potentially liable to subrogation, social media analysis in order to get early insights on claims from the company’s portfolio (especially useful for corporate insurance and private health insurance), and many other tasks involving various forms of text as an object of analysis.

Pattern/Anomaly Detection

Machine learning can help classify data points as either normal or anomalous. For instance, common patterns may be detected in claims from multiple accidents, which could indicate organized fraud.

Automated Decision Management

Real-time automated decision-making is now a reality due to advances in artificial intelligence and machine learning. Consumer demand for instant services has also played a role in this development. This means that insurers have no choice but to use automated means to rapidly process information for decision-making.

Natural Language Generation (NLG)

Natural Language Generation (NLG) has great potential for automating the generation of reports and contracts in the insurance field. The use of AI-based software for these tasks offers great scaling potential and enables the maintenance of consistently high quality.

Intelligent Document Processing (IDP)

Insurance companies can greatly benefit from document automation. Intelligent document processing can help efficiently handle the repositories of day-to-day paperwork and the unstructured data they contain, as well as validate the received data.


Insurance companies can now obtain a more accurate picture of their clients’ risk profiles, thanks to the vast amount of data collected from smart devices such as phones, fitness trackers, and GPS systems in cars. This leads to more precise insurance premiums.

To Sum It Up

AI in the insurance industry improves customer service and performance, reduces operational costs, enhances employee performance, and speeds up processes, ultimately helping insurers attract more clients. As technology continues to advance, particularly in the field of artificial intelligence, the landscape of the insurance industry will continue to evolve. Consider selecting the right technology partner, such as Itexus, to incorporate AI process automation into your insurance business.

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