Insurance businesses adopt several measures to increase their market size and attract new customers. One consideration that prospective clients often have when selecting an insurance provider is the claim-settlement ratio. However, by leveraging AI in insurance processes, settling claims can become easier for companies that handle a significant number of requests each day.
It is where artificial intelligence (AI) comes into the picture as a savior. The global business related to AI in the insurance segment will touch USD 4.5 Bn in 2026. It will grow at a CAGR of 24% and scale new heights from USD 800 Mn in 2018.
AI is already simplifying claims processing for insurance providers in many ways. This article looks at how exactly all of that is happening.
Table of Contents
What Does AI Mean for the Insurance Sector?
Modern insurance companies are increasingly leveraging data analytics to grow their business. Insurance companies in the segments like life insurance, health, and travel use data to classify their customers. It becomes possible through data related to accidents, the personal information of policyholders, and other third-party data.
These data points also help in optimizing expenses and preventing fraud. Big data can be in several structured and unstructured forms. It aids insurance businesses in pricing, rating, underwriting, claims to handle, and marketing activities.
Machine learning in insurance claims also augments and accelerates the handling process. By training machine learning models, vehicle insurance providers can identify the damage severity and repair costs. It is possible through images, sensors, and historical data.
With our automated vehicle damage detection solution, Gramener assisted an automobile insurance company in automating the damage verification process in order to expedite insurance claim payouts. To save money and time, the firm intended to assess damage from different perspectives rather than rely on a personal visit from an agent. Our Android app categorizes live camera pictures.
We trained models, converted them to TensorFlow light, and then integrated them into the app. The classification model employs an innovative edge deployment technique that removes the requirement for human intervention in determining automobile damage estimates. The model achieves 97 percent accuracy in two seconds of inference time.
How AI is Used in Insurance?
Insurance fraud, excluding health insurance, is more than $40 billion annually in the US alone. With the complete digitization of insurance processes, one might think that it is an open invitation for further trouble. But technologies like AI help prevent risks by detecting fraud.
Insurance providers can spot anomalies in claims data with the help of AI technologies. They can prevent situations where customers add fake information to get bigger claim payouts or reduce their premium amount.
Another area where AI can be effective for insurance companies is claims handling. These businesses have to spend a lot on agents, which they compensate by adding markups on the services offered.
AI aids in the automation of tasks that help insurance businesses reduce the need for human resources. By automating related tasks, insurance providers can also reduce the scope for errors and speed up the entire claims management process.
Research from McKinsey already shows that AI and machine learning in insurance claims can reduce the processing time from weeks to minutes. Insurance providers can also personalize their services for better targeting of customers.
How is AI Impacting the Insurance Industry?
When we consider the insurance businesses that deal in properties and casualties, the time taken for claims processing is quite long. Analyzing and assessing the damage to a vehicle or building is a vital part of the process. Businesses can improve claim processing with AI and ML technologies.
AI technologies like computer vision can help overcome errors, lower fraud risks, and speed up the process. It is possible through big data, drones, satellite images, and computer-assisted inspections.
Computer vision algorithms and geospatial imagery, when used together, can help in flagging potential insurance risks for each property in geography. When users add an address in the system, they will immediately get a list of potential risks, like oversized trees and damaged roofs.
Historical data related to customer claims and satellite imagery data predicts the potential frequency and severity of claims. Both data sets can highlight specific properties that had claims and if any related characteristics match with new properties.
How Do Insurance Companies Use Big Data?
Insurance service providers use both structured and unstructured data in numerous ways. Let’s take a look at the role of big data through AI in insurance:
- The use of historical data helps resolve customer issues quickly and improve the user experience.
- Telematics for vehicle insurance identifies driver behavior to offer insurance based on their usage and handling of vehicles.
- AI and ML algorithms help in customer retention by identifying early signs of customer dissatisfaction and flagging them to the insurance service provider.
- Big data and predictive modeling analyze client data classify them under different segments to reduce risks.
- The online behavior data of helps insurance businesses create targeted marketing campaigns for customer acquisition.
- Historical data of a person can identify fraudulent activities in the past and flag their case for intensive investigation.
- Automation techniques help in reducing labor costs and improve process efficiencies by eliminating the scope for errors.
What are the benefits of AI in insurance?
With AI holding significant importance in the insurance industry, businesses have started leveraging it enough nowadays. Here are the several benefits of AI in insurance and how it is reshaping the industry:
Delivers Better Access to Data
The foundation for any effective customer-oriented system is data, and AI helps lay a solid base for that. Adding AI systems to the workflow benefits the organization and its employees in several ways.
With the help of AI, insurance providers can identify if their resources are investing more time in policies that do not have much of expected lifetime value. This insight helps in better resource management to improve business productivity and profitability by allocating more time on accounts that can yield better returns.
Delivers Appropriate Insights at the Right Time
Insurance companies use predictive models to rank submissions sent for underwriting. It helps them identify projected losses and the sincerity of brokers. AI helps ease this process by creating a system that ranks them. Agents also get the cue on submissions that need more priority based on business profitability. The AI-based insights help underwriters take the best action based on insights and recommendations of the system.
Aids in Data-Driven Decision-Making
Frauds remain one of the biggest concerns in the industry that AI can help overcome. AI technologies can work on historical data to identify fraud patterns as a result it helps in detecting possible fraudulent measures accurately and quickly. The capacity of AI systems is much more than humans in this aspect. Insurance service providers can analyze such cases intensively for further action. The results from such analyses can help in training the models further to become more accurate.
Brings Consistencies in Performances
AI helps eliminate guesswork and promotes data-driven decision-making at all levels, it also leads to lesser-experienced employees making better decisions based on recommendations that hold past validation. Insurance businesses can reduce risks even if their workforce doesn’t have much experience. In the absence of AI, there can be situations where customers get overcompensated for claims. Data analytics helps overcome this problem.
AI use cases in Insurance
AI has wide-ranging applications that simplify complex processes in the insurance segment. Let’s take a look at the use cases of AI in insurance:
Efficient Claims Processing with AI
Manual and undigitized claims processes increase the operational costs by 50-80%. Inefficiencies and errors also remain a concern with manual processes. Insurance companies can improve their underwriting and claims management processes through data from telematics, fitness trackers, and other IoT devices.
Faster Claims Settlement
Both customers and insurance companies want to have reduced claims settlements. AI helps them by automating labor-intensive tasks related to inspections because it has especially proved its worth in times of pandemic through remote inspection. If a vehicle meets with an accident, the insurance provider can assess damages remotely through image sensing. It helps save time and costs behind the entire assessment process.
Improves Document Handling Times
Optical character recognition (OCR) helps recognize texts and digits from paper-based forms. OCR is effective in improving the operational efficiencies of insurance agents. They can avoid retyping the entire information from documents as the system can capture and reconcile data from paper-based forms.
Improves Fraud Detection and Prevention
Detecting frauds in the claims process can be tough and labor-intensive. If done manually, it would require a lot of time and there would be no guarantee of successful outcomes. AI and machine learning in insurance claims help detect fraudulent activities and customers by working on their historical data.
Helps with Underwriting and Insurance Pricing
Data is available in abundant measures today, and ML algorithms can help reap their benefits. They help identify the correct pricing for policies to improve business profitability. By detecting patterns in large datasets, insurance agents also reduce guesswork and make data-driven decisions.
Assists with Damages and Repair Cost Estimations
With automated inspections, AI-driven insurance businesses can also get the benefits of automated repair estimates because computer vision, image datasets, and deep learning algorithms help with automatic vehicle damage inspection. The algorithms can detect damages and their extent to calculate repair costs.
Machine Learning Can Improve Customer Support
Machine learning algorithms help reduce the tasks of insurance agents, leading to saving in time and costs. Natural language processing (NLP) can detect the nature of customer emails and route them to appropriate departments to speed up addressing issues. It also analyzes customer sentiment to improve their satisfaction levels.
AI in Insurance Helps with Churn Prediction
Machine learning algorithms help in identifying the interaction pattern of customers with the insurance provider. Some of the indicators are the usage of the app and reward programs, frequency of interaction with support teams, and life changes like marriages. It helps insurance companies take measures to avoid client churn.
Conclusion
To sum up, the insurance industry and claims settlement process stand to gain a lot from the advances in the world of AI. Besides reducing the time to issue payments to customers, AI technologies also help with fraud detection, pricing, claims to handle, and customer service. Automation has several benefits, and there is no better time for insurance companies to leverage its benefits to the fullest.
The AI-enabled solutions of Gramener are already helping global insurance businesses automate their processes and improve revenue. To get a free product demo, get in touch with us today.
