What is the impact of artificial intelligence in insurance? Well, there are a lot of use cases for artificial intelligence in everyday life, but what about AI in insurance? The effects of artificial intelligence in business heavily include insurance.
Are you scared of AI jargon? We have already created a detailed AI glossary for the most commonly used artificial intelligence terms and explained the basics of artificial intelligence as well as the risks and benefits of artificial intelligence for organizations and others. So, it’s time to explore the role of artificial intelligence in insurance sector.
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Impact of artificial intelligence in insurance industry
One of the most revolutionary advances has been the use of AI in insurance, which has been hailed as having significant economic and societal advantages that eventually boost risk pooling and improve risk reduction, mitigation, and prevention.
Automation enables insurance businesses to quickly respond to requests and guarantee that the customers they pledge to serve will receive high-quality service.
“Machines have instructions; we have a purpose. We will need intelligent machines to help us turn our grandest dreams into reality.”Garry Kasparov
Is Kasparov right? Absolutely. However, the insurance industry has not adapted to AI technologies despite their many benefits. The traditional insurance industry has generally been hesitant to adapt to new technologies. According to a Deloitte survey, while practically every business has found success with AI or has begun investing in it, the insurance sector appears to be far behind, with only 1.33% of insurance companies investing in AI compared to 32% in software and internet technologies.
The good news is that whoever adopts AI early in the insurance sector will be a pioneer and receive the largest piece of the pie.
The environment is currently evolving quickly with the emergence of InsureTech startups and technological incumbents. In addition to requiring less money and resources, they can provide on-demand plans, more transparent pricing, and quicker claim payments.
What is the InsurTech industry?
The term “InsurTech ” describes technical advancements developed and used to increase the effectiveness of the insurance sector. The invention, distribution, and management of the insurance industry are all supported by InsurTech.
The shifting dynamics create global prospects for the AI-enabled insurance sector. So, let’s see the benefits of artificial intelligence in insurance and explore the taste of “the pie.”
Benefits of artificial intelligence in insurance
These are some of the best benefits of artificial intelligence in insurance:
- Expanded data and insight access
- The right information at the right moment to the right people
- Consistent performance from employees
- Better, quicker decisions are driven by data
Let’s take a closer look at the advantages of artificial intelligence in insurance and find out how Artificial intelligence is helping the underwriting process in insurance.
Expanded data and insight access
Building a better, more precise data foundation is a prerequisite for integrating AI into a workflow, and doing so benefits people even before AI is used.
Consider a worker attempting to ascertain whether some clients are spending too much time in the service center, particularly if they have a low estimated lifetime value. The underwriter receives a forecasted lifetime value score and can use it to inform a better price decision thanks to access to customer journey information and insights.
After AI is implemented, any previous activities can be sent to the machine-learning model and the customer’s information. By targeting the most profitable customers and avoiding those who are most likely to be unprofitable, the sales and marketing teams can improve future results.
The right information at the right moment to the right people
A submission forwarded to underwriting is first evaluated in real-time using predictive models for criteria including “broker sincerity” and “projected loss ratio for this class.” To help with issues like “Which risk should I work on next that will be most advantageous for our company?” AI can then develop a scoring system for those inputs.
Given the insights provided, the underwriter can choose the optimal course of action by digitizing the underwriting process with AI. In this instance, AI aids in bridging the gap between the employee’s action based on the recommendation made by the AI engine and the information gained.
Consistent performance from employees
Decisions become more accurate, correct, and consistent thanks to AI’s elimination of a large portion of the guesswork involved in decision-making.
While training is still essential, applying AI enables less experienced employees to pick up new skills much faster because they receive recommendations based on decisions that have already been proven to be correct. This reduces a lot of the risk that comes with hiring a new employee.
An insurance claims adjuster with less expertise might overcompensate a client for a claim. In contrast, an adjuster empowered by AI can be directed through suggested next actions based on prior experiences, all within the same analytics system.
Better, quicker decisions are driven by data
Think about an insurance provider attempting to prevent fraud. Unlike humans, AI can read and depend on vast amounts of historical data based on false claims.
As a result, future fraud is caught considerably more quickly and precisely. This also helps the AI swiftly enhance its grasp of typical fraud behaviors. Much more than a human counterpart could ever calculate or act upon.
Because of these benefits, there are a lot of use cases of artificial intelligence in insurance.
AI in insurance use cases
AI is increasingly important in the insurance industry, from claims processing to compliance to risk reduction and damage analysis. These are some of the best AI in insurance use cases:
- Claims processing
- Claims fraud detection
- Claims adjudication
- Automated underwriting
- Submission intake
- Pricing and risk management
- Policy servicing
- Insurance distribution
- Product recommendation
- Property damage analysis
- Automated inspections
- Customer lifetime value prediction
- Speech analytics
- Customer segmentation
- Workstream balancing for agents
- Self-servicing for policy management
- Claim volume forecasting
How is technology changing the insurance industry? How does AI & ml enable insurers to tackle current challenges? Let’s explore artificial intelligence in insurance use cases and find out!
In order to comply with policy and regulatory requirements, insurers must make sure that claims are valid throughout the whole process cycle.
Handling thousands of claims and client inquiries is a laborious task that takes time. The entire procedure is effective and efficient, thanks to machine learning. Moving claims through the first report, analysis, and contacting the consumers significantly increases the value chain of claims processes.
Check out the 15 real-life examples of machine learning
Employees could concentrate on more complicated claims and one-on-one customer interactions because of the time savings.
Claims fraud detection
According to research by the Federal Bureau of Investigation on US insurance firms, the total cost of insurance fraud (non-health insurance) is nearly $40 billion annually.
In terms of higher premiums, insurance fraud costs the typical US household $400 to $700 annually. These shocking figures highlight the critical need for precise automated theft detection solutions to enable insurance companies to improve their due diligence procedure.
According to the Council for Affordable Quality (CAQH) Index research, automating eligibility and claim verification can save the healthcare insurance industry alone $ 5.2 billion annually. With a chatbot that communicates with consumers and gathers the necessary data, the claim initiation automation process helps insurers save time.
A first-level validation can be done throughout the claim start process using chatbots to capture information in a structured way. According to a World Economic Forum (WEF) report, computers will be used to carry out 62% of an organization’s data processing and storage tasks by 2022. Due to the expanding automation industry, investing in auto-adjudication systems will help firms stay relevant shortly.
Do you know a better love story than AI in insurance underwriting? In the past, insurance underwriting relied mainly on employees to examine historical data and come to wise conclusions. Working with chaotic systems, procedures, and workflows was another challenge as they attempted to reduce risks and provide customer value. Intelligent process automation simplifies the underwriting process by offering Machine Learning algorithms that gather and make sense of enormous volumes of data. It is one of the most used artificial intelligence in insurance use cases.
Additionally, it enhances the performance of rules, controls straight-through acceptance (STA) rates, and guards against application mistakes. Underwriters can concentrate only on complex instances that may need manual attention as most of the procedure has been automated.
When combined with AI and NLP, automation can extract data from structured and unstructured sources, including brokers’ emails, spreadsheets, loss runs, and ACORD forms, facilitating effective teamwork and accelerating and improving risk assessment.
Additionally, automation makes managing various submission queues for new businesses, renewals, and endorsements easier. Machine learning models quickly sift through hundreds of submissions and rank the best entries following the underwriting triage criteria and risk appetite.
Pricing and risk management
Price optimization uses data analytic techniques to determine an organization’s ideal rates while considering its objectives. It is one of the best artificial intelligence in insurance use cases.
It analyzes how customers respond to various pricing strategies for goods and services. GLMs (Generalized Linear Models) are mostly used by insurance companies to optimize prices in industries like auto and life insurance. With this method, insurance businesses may better understand their clients, balance supply and demand, and increase conversion rates.
Automation of risk assessment also improves operational efficiency. Risk assessment automation increases efficiency by fusing RPA with machine learning and cognitive technologies to build intelligent operations. Insurance companies can provide a better client experience and lower turnover because the automated procedure takes much less time.
Check out cyber risk assessments examples
The policy administration system can be integrated to get information about each policy thanks to the automated intake of policy data. This lessens the manual search and location effort needed to discover the pertinent fields for policy endorsements.
Additionally, it enables parallel processing to handle complex circumstances where many requests are made by different clients, which reduces the turnaround time for processing and servicing insurance policies. RPA in the insurance industry helps to efficiently complete various tasks without requiring extensive system navigation. It automates administrative and transactional tasks like accounting, settlements, risk capture, credit control, tax preparation, and regulatory compliance.
In the pre-digital era, insurance customers might visit a local carrier or contact a financial adviser to learn about coverage possibilities. In a specialized market, there would often be a leading carrier for a certain product. The carrier would carry out underwriting tasks and share a quote based on the customer’s submitted data. Digitalized insurance distribution methods flipped this scenario.
Today, almost all carriers have an online site where customers may browse their selection of products and services before making a choice. This change in consumer behavior brought on a significant disruption in the insurance industry. Beyond underwriting and claims clearance, AI has the ability to revolutionize the sales and distribution stage of the insurance value chain by utilizing cutting-edge AI algorithms that are now on the market.
Insurance companies can benefit from a customer’s digital behavior by using digital technologies like optical character recognition (OCR), machine learning (ML), and natural language processing (NLP).
Each day, the insurance industry produces a large amount of transaction data. Automation can help businesses in this situation accurately and effectively propose insurance products to customers, increasing the insurance company’s ability to compete.
Price optimization uses data analytic techniques to determine an organization’s ideal rates while considering its objectives. It is one of the most common artificial intelligence in insurance use cases.
Property damage analysis
The first step in any damage insurance claim process, whether it involves a mobile phone, a car, or a piece of property, is inspection.
With physical intervention, estimating the damages to determine repair costs is difficult for insurance companies. Data analysis and AI-powered object detection compare the level of damage before and after the occurrence. Machine learning algorithms can identify broken auto parts and provide repair cost estimates.
Motor insurance claim assessment has historically been handled manually by surveyors and claim adjusters. Manual inspection is expensive because it necessitates the adjuster or surveyor to contact the policyholder. Each examination costs between $50 to $200. The processing of claims would also take longer because report generation and estimation typically take one to seven days.
Insurance firms can examine car damage with AI-based image processing. The system then produces a thorough assessment report explaining the car parts that can be repaired and replaced and their approximate costs. Insurance companies can lower claim estimation expenses and improve the procedure’s effectiveness. Additionally, it populates reliable data to determine the final settlement sum.
Customer lifetime value prediction
One of the most important technologies that allow businesses to forecast client lifetime value using machine learning is the customer lifetime value (LTV).
According to research by Bain & Co., an improvement in retention of 5% can result in a profit increase of 25% to 95% for a business. A customer’s purchasing history is compared to a huge product inventory by machine learning algorithms to uncover hidden patterns and group products that are similar. It is one of the most important artificial intelligence in insurance use cases.
Customers are then given access to these products, eventually promoting product purchases. Insurance companies can strike the ideal balance between customer acquisition and retention by knowing the lifetime worth of each customer.
Speech recognition is a potent tool for lead call analysis based on customer speech to enhance the personalization. It can detect fraud based on voice analysis of customer calls to increase security measures and identify customer pain points with products using speech analytics of comments to improve future products.
Do you know artificial intelligence customer services are on the rise?
The first step in developing customization is customer segmentation. It improves consumer happiness, product design, marketing, and budgeting. It is one of the most common artificial intelligence in insurance use cases.
Machine learning algorithms examine customer data to uncover trends and insights. Tools with AI assistance accurately identify client categories that are difficult to complete manually or use traditional analytical techniques.
Workstream balancing for agents
Utilizing AI-assisted models that give them access to consumers and enable them to grow their businesses is becoming increasingly popular among insurance agents.
AI will undoubtedly be the cornerstone for increasing consumer happiness and, in turn, expanding the reach of insurance brokers because simplicity is its defining characteristic.
Self-servicing for policy management
Self-service BI technologies allow users to filter, organize, analyze, and visualize data without the help of BI and IT teams in a company. These tools make it simpler for staff members to gain insightful business knowledge from the data gathered in BI systems. Ultimately, this strategy promotes more informed decision-making, which raises revenues, boosts productivity, and improves client happiness.
Check out the role of artificial intelligence in information systems
Claim volume forecasting
Setting the premium at the start of the insurance contract is fundamental to insurance practice. A precise and reliable assessment of the number of claims occurrences and the total claim amounts is crucial to arriving at an insurance company’s precise premium for the upcoming year. It is one of the most critical artificial intelligence in insurance use cases.
The forecasting for individual claims is faster and more accurate, thanks to machine learning. This enhances the effectiveness of an insurer’s pricing.
Insurance companies using artificial intelligence (Top 5)
What insurance companies are using AI? Insurance companies are utilizing artificial intelligence to create customized plans, automate the underwriting process, and give customers worldwide more precise estimates. These are some of the best insurance companies using artificial intelligence:
- Liberty Mutual Insurance
- CCC Intelligent Solutions
- Bold Penguin
Check out these Insurance companies using artificial intelligence to learn more about how AI affects the insurance sector.
Through the Solaria Labs program, Liberty Mutual investigates AI in fields including computer vision and natural language processing. One outcome of their efforts is the Auto Damage Estimator. This AI solution uses comparative studies of anonymous claims images to swiftly evaluate vehicle damage and offer repair estimates after an accident. It is one of the firms that used artificial intelligence in insurance.
Artificial intelligence is used by CCC Intelligent Solutions to digitize and automate the whole claims process. Photos taken at accident scenes are analyzed using AI and guidelines agreed by the insurance. Based on this information, CCC’s AI can determine the extent of the damage and promptly offer estimates that insurers can accept and forward to their clients for confirmation.
Utilizing artificial intelligence, Insurify instantly connects clients with auto and home insurance providers that meet their individual requirements. The business uses RateRank algorithms to identify the insurance that would suit each client, taking into account details like location and desired discount level.
Clearcover uses artificial intelligence to process claims and insure users quickly. Users of Clearcover can receive AI-generated quotations and select the one that best suits their needs after completing a brief questionnaire. Users only need to take a few images and complete a brief form if they are ever in an accident before ClearAI jumpstarts the claims procedure.
With two AI-powered tools, SubmissionLink and ClauseLink, Bold Penguin enables insurance businesses to produce policies that stand out in the sector swiftly. SubmissionLink examines documents that carriers receive from authorities and identifies crucial information for underwriters. While this is going on, ClauseLink examines insurance provisions to assist providers in comparing their plans to those of rivals.
AI in insurance market size
With a predicted CAGR of 32.56% from 2022 to 2031, the global AI in the insurance market, valued at $2.74 billion in 2021, is expected to increase to $45.74 billion by 2031, according to AlliedMarketResearch.
The global AI in the insurance market is expanding due to an increase in investment by insurance companies in AI and machine learning, as well as a rise in demand for personalized insurance services.
AI will drive the future of insurance. Utilizing various AI techniques will quickly automate insurance processing, from claim submission to payment, without human involvement. Saving this money and effort will enable the insurance sector to develop better product categories and customized premium rates based on information gathered from multiple sources.
A wave of homogeneity across various market sectors, industrial verticals, and service providers is brought forth by AI. As a result, procedures for getting insurance and handling claims can be more consistently standardized.
Greater operational excellence, lower costs, and improved client experiences are other advantages that we can anticipate. It is clear that AI-driven insurance has a bright future, and the use of AI in the insurance sector will significantly increase in the years to come.
Is artificial intelligence better than human intelligence? Explore the cons of artificial intelligence before you decide whether artificial intelligence in insurance is good or bad.