Artificial Intelligence in Insurance
The world is a dangerous and unpredictable place. You can never know what will happen. Car accidents, illnesses, natural disasters are only a few of the hazards that lurk around the corner. Luckily, there is insurance that protects us from the unsure future. Unfortunately, the insurance industry is full of complex and time-consuming processes, which makes it less convenient, but those very same processes can be streamlined with the help of artificial intelligence. AI is transforming the insurance industry, with the most prominent examples including:
AI TRANSFORMS THE INSURANCE INDUSTRY
How AI is used in insurance – potential use cases
Description: It is said that there is no limit to perfection. Indeed, there is always a way to make processes more efficient, thereby decreasing costs. For example, automation always leads to optimization, as processes require less manual work and can operate independently.
Influence on business: Machine learning allows companies to plan their budget more efficiently through a predictive mechanism. It also helps in the automation of business processes, which reduces costs and saves time.
Benefits: Decreased costs, increased productivity.
Complexity: 2 (below average)
Data needed: All kinds of financial data available, a history of financial operations.
Examples: Process automation, predictions of costs.
Description: Claim processing is the backbone of insurance. It is the way clients receive compensation which is the point of any insurance. It is, therefore, crucial to process the claims as quickly and accurately as possible. For each claim processing stage, insurers need to process a large volume of documents, which requires a lot of time and attention.
Influence on business: Claims management can be automated using machine learning techniques in different stages of the claim handling process. Insurers can automatically extract data from documents, identify fraudulent claims and validate claims that are in line with policies. Machine learning models can help quickly assess the severity of damages and predict the repair costs based on historical data, sensors, and images.
Benefits: Enhanced customer experience, reduced costs, and claims settlement time.
Complexity: 2 (below average)
Data needed: Previous claims and decisions.
Examples: Checking the claim requests for adequate information, validation, justification, and authenticity.
Underwriting and Pricing
Description: Insurance companies set their prices basing on underwriting processes, i.e., calculating the probability of an accident and risk assessment. It usually takes a lot of time and effort to do this manually.
Influence on business: Artificial intelligence allows companies to speed up this process. Moreover, the result is often even more accurate, as it eliminates human errors. The underwriting process is reduced to a few seconds as most underwriting is automated and supported by a combination of machine and deep learning models built within the technology stack. Individual pricing can then be applied.
Benefits: Reduced time, increased accuracy, optimized pricing.
Complexity: 3 (average)
Data needed: Clients’ personal information, history of claims, history of accidents.
Examples: Accurate insurance risk probability calculation, price setting.
Description: Good customer service is a key to client satisfaction, which, in turn, leads to the company’s growth. It is vital to provide quality support to prevent clients from switching to another company. Personalization is another side of customer service. It is said that almost 75% of customers feel frustrated when their shopping experience is impersonal, which relates to insurance as well.
Influence on business: Properly organized customer support can increase client satisfaction and decrease churn, while personalized recommendations can increase sales. Chatbots, for instance, can effectively manage most of the conversations, leaving only the most complicated ones for the operators, thus, saving time and money.
Benefits: Increased sales, client satisfaction, and loyalty, reduced time.
Complexity: 4 (above average)
Data needed: Previous conversations with clients, customers’ feedback, clients’ personal information, a history of clients’ purchases.
Examples: Chatbots, products and plans recommendations, immediate compensations, customer segmentation.
Description: According to this study, there is a massive money loss from fraud in the insurance industry. It leads to higher prices and more complicated claim approvals, as insurers are cautious not to get deceived.
Influence on business: Machine learning is used as a protective mechanism. It can detect fraudulent activities with more accuracy which reduces money loss. Machine learning can also help in investigating fraud and revealing its source.
Benefits: Reduced money loss, increased security.
Complexity: 5 (high)
Data needed: A history of claims, clients’ personal information, legal documents (laws, rules, regulations).
Examples: The detection of fraudulent claims and exaggerated facts, the detection of fraudsters.
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How to start?
Artificial Intelligence can change the industry on many levels. It challenges conventional cost structures and allows forming new relationships with end customers. AI insurance is becoming more and more real. However, the full potential of this technology is yet to be harnessed. Companies that want to use AI for insurance in the future have to start acting now.
The leaders looking for ways to employ Artificial Intelligence in insurance should pay their attention to the following principles:
- Treat data as the cornerstone. Building AI insurance starts with determining the quantity, type, and quality of available data. Thus you will come up with achievable and relevant use cases. However, if your organization doesn’t have all the necessary data for some AI use cases in insurance, that is not the end. Look for available public data sources or partners with relevant data. Besides, you can start gathering valuable data yourself. For instance, the information from some analog processes may be directed to digital databases. Also, your business can use a consumer-facing digital product to harvest user data.
- Start small. Conjuring up the most complex AI ML use cases in insurance to squeeze as much value as possible. A small solution is easier to build and starts bringing you to value faster. Later this application can be scaled to expand capabilities and get more benefits.
- Don’t let failure stop you. Using data science in insurance may be challenging. To start with, there is certain social stigmatization of the technology to overcome. Besides, AI insurance projects cost much in both time and money. On top of it, they require interdisciplinary collaboration. Therefore, accepting failure as indispensable from innovation is vital for success. Each attempt to employ AI for insurance contributes to organizational learning. The “fail fast, fail early” strategy can save time and costs.
The role of AI in insurance: summary
Businesses are already getting value from AI. Therefore, Artificial Intelligence in the insurance industry will arm insurers with useful tools for future challenges and opportunities. Leveraging the new technology may help to cope with regulatory requirements and soaring labor costs. Besides that, AI insurance is better equipped to meet evolving customer expectations and compete with InsurTech startups.
Artificial Intelligence can make insurance more pleasant and affordable. New specially tailored products will attract more customers at lower prices. AI in the insurance industry can use a mountain of accumulated data to create more flexible solutions. The result may be on-demand insurance and premiums which adjust to customer health, accidents, etc.
Thanks to AI, insurance is likely to become more personalized. With the help of new tech, insurers will understand the needs of their customers better. Moreover, the use of AI in the insurance industry will speed up workflows and reduce costs. With the help of AI-driven analysis, insurers will open up new business opportunities and find more revenue streams.
To crown it all, AI for insurance can make interactions with companies much easier. As a result, the whole industry will benefit from the flow of new customers.
What does AI mean in insurance?
Most insurance executives expect AI to become an advisor and partner in their organizations. Insurers employ it to provide better and faster services for a lesser cost.
How is AI used in insurance?
Artificial Intelligence helps insurers to process claims, speed up underwriting, detect fraud and optimize budgets. Besides, it improves customer experience, making the insurance process less frustrating and bureaucratic.
How is artificial intelligence changing the insurance industry?
AI allows insurers to manage their work better and learn more about the preferences of their customers. It facilitates the creation of personalized products and the exploration of new revenue sources.