Artificial Intelligence and Insurance
Artificial Intelligence and Insurance
AI is intelligence exhibited by machines. A machine would be considered “intelligent” when it takes into consideration its environment and takes action to maximise the possibility of achieving its given goal. It is widely used when computer programmes are developed to have cognitive functions such as learning and problem solving. AI research is taking place in fields including reasoning, knowledge, planning, learning, natural language processing, perception, and moving/manipulating objects.
AI solves business problems leveraging technologies such as computer vision, natural language processing, machine learning, virtual agents, robotics, and autonomous vehicles.
Artificial Intelligence (AI) is a powerful tool for Insurers that can increase value and growth. According to a recent report from Accenture, AI has the potential to generate $x.y trillion in value for insurers by 2035. AI offers insurers a variety of benefits, including enhanced customer experience, effective risk assessment, quicker claims resolutions, fraud detection and streamlined operations.
Evolution
While most think AI is a novel notion, it has been around for quite long. In 1950’s scientists, mathematicians and philosophers had informally adopted the notion of Artificial Intelligence. It was formally introduced in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence organized by John McCarthy and Marvin Minsky.
AI grew in popularity from 1957 to 1974 as computers became faster, cheaper and more accessible while at the same time machine learning algorithms advanced. However, the real benefits were not seen due to the challenges such as the need for the large amounts of data and the high storage prices. In the early 80s, the funding for artificial intelligence research was reduced by the U.S. and British governments. This was called “AI winter,” and it was a very difficult time for researchers. However, the Japanese initiative inspired governments and industry to pump billions of dollars into AI research. At that point, it became easier for students and ordinary people to learn about AI and apply it in their daily lives.Since then, AI research has seen a resurgence due to an expansion of algorithmic tools and increased funding. During this time, researchers developed and popularized techniques like “deep learning,” which allows computers to learn from experience.
Configuration
Integration
Enhancements
Current Trends
Fast forward to 21st century, the convergence of algorithmic advances, exponential increase in computer processing power and storage capacity, availability of large data has transformed AI hype into reality. AI solutions are being used in many fields, including autonomous cars, manufacturing, healthcare, media / entertainment, banking / financial services, and insurance.
There are numerous use cases of AI such as process automation, pattern recognition, predictive analytics, language processing, image processing, etc. AI applications can also be used to replace human workers in customer service. Companies are increasingly using AI solutions such as chatbots, virtual assistants, robots, etc.to enhance their businesses and provide better customer service.
AI can analyse online activity of individuals and generating useful data for businesses. For example, restaurants may need to know the demographics of customers in their area to better target their advertising. AI can give businesses real-time updates about their target audience. Netflix, for example, uses AI to understand the needs of its loyal customers and provide them with more relevant content.
Types of AI
AI can be categorized into various types based, primarily, on their capabilities and functionality.
AI type-1: Based on Capabilities
1. Weak AI or Narrow AI:
Narrow AI is a type of AI which can perform a dedicated task with intelligence. The most common and currently available AI is Narrow AI in the world of Artificial Intelligence.Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed as weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits.
Apple Siri is a good example of Narrow AI, but it operates with a limited pre-defined range of functions.IBM’s Watson supercomputer also comes under Narrow AI, as it uses an Expert system approach combined with Machine learning and natural language processing. Other examples include playing chess, purchasing suggestions on e-commerce site, self-driving cars, speech recognition, and image recognition.
2. General AI:
General AI is a type of intelligence which could perform any intellectual task with efficiency like a humanand think like a human by its own.
Currently, there is no such system in existence which could come perform any task as perfect as a human.The worldwide researchers are now focused on developing machines with General AI.As systems with general AI are still under research, it will take lots of efforts and time to see their usage in practice.
3. Super AI:
Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence and can perform any task better than human with cognitive properties. It is an outcome of general AI.Some key characteristics of strong AI include the ability to think, to reason,solve the puzzles, make judgments, plan, learn, and communicate by its own.
Super AI is still a hypothetical concept of Artificial Intelligence. Development of such systems in real is still world changing task.
Artificial Intelligence type-2: Based on functionality
1. Reactive Machines
Purely reactive machines are the most basic types of Artificial Intelligence.Such AI systems do not store memories or past experiences for future actions.These machines only focus on current scenarios and react on it as per possible best action.
IBM’s Deep Blue system is an example of reactive machines.Google’s AlphaGo is also an example of reactive machines.
2. Limited Memory
Limited memory machines can store past experiences or some data for a short period of time.These machines can use stored data for a limited time period only.
Self-driving cars are one of the best examples of Limited Memory systems. These cars can store recent speed of nearby cars, the distance of other cars, speed limit, and other information to navigate the road.
3. Theory of Mind
Theory of Mind AI should understand the human emotions, people, beliefs, and be able to interact socially like humans.This type of AI machines is still not developed, but researchers are making lots of efforts and improvement for developing such AI machines.
4. Self-Awareness
Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent, and will have their own consciousness, sentiments, and self-awareness.These machines will be smarter than human mind.
Self-Awareness AI does not exist yet and it is a hypothetical concept.
AI projections
AI market size has grown astronomically in the past few years. It has reached a market size of ~50 Billion $s in 2021 and is projected to cross 60 Billion $ in 2022. Artificial Intelligence is pervading through all industries and across all business processes. The adoption rates are expected to increase with the maturity of the technologies and their adoption. By 2025, the market size is projected to grow to a whopping size of 100+ Billion $ (266B$ by 2027).
Key technology and service providers
Leading companies providing AI technology include Google, Microsoft, and Amazon. These tech giants provide AI through popular cloud platforms that make it easy to integrate into AI solutions. By utilizing these platforms, businesses can cut down on in-house development costs. For example, Amazon Web Services, which has dominated the cloud computing space, offers several AI products for business and consumer use. AWS’s “Alexa” intelligent voice service is available to anyone, and the company also offers image-recognition solutions.Some of the other leading AI technology providers include IBM, Apple, C3.ai, DeepMind, Metaverse platforms, H2O.ai, Salesforce and Cloudera.
Leading AI consultancy firms include IBM, TCS, Accenture, PwC, Deloitte, etc. According to a 2021 survey by IBM and Morning Consult, there are still barriers to AI adoption in terms of limited expertise, data complexity and lack of tools for AI development. These consulting companies are bridging the gap by providing the insights and correct tools for AI development.
Leading AI development firms include Accubits and DataRobot. These companies work with customers to develop intelligent AI applications for big data and complex data analytics problems. Using AI, they can automate tasks previously performed by humans.
Adoption in the Insurance industry
Insurers are beginning to explore the potential of artificial intelligence.AI can be used in a number of ways, from personalization of products and figuring out how to get clients for insurance business to streamline claims processing and enhancing customer experience. AI is helping underwriters by providing valuable insights, enabling them to assess the risk effectively and price the policy appropriately. AI is also helping Insurers in improving employee productivity, identifying fraudulent claims and making businesses resilient. By harnessing the power of AI, insurers can gain a competitive edge and position themselves for profitable growth.
Marketing and Customer acquisition
AI can be used to personalize the insurance experience for consumers. Insurers can analyse social media data, learn about consumer habits and tailor product categories and promotions to improve customer experience and increase productivity. Companies are even investing in chatbots, virtual assistants that offer digital services to clients. AI-based chatbots can hold natural-sounding conversations with humans and can even provide answers to common policyholder questions.
AI can also help insurance companies compete more effectively in the market. It can sift through massive amounts of data to identify trends, predict risks, and even highlight sales opportunities. Data from AI can be used to develop detailed profiles of customers, which will help them better target their marketing efforts.AI is also creating new opportunities for insurers to develop cross-sell opportunities. AI can also monitor leads’ progress through the sales funnel, directing targeted marketing efforts at each stage.
It can also help reduce attrition by focusing re-engagement campaigns on customers in need of attention. In addition to building loyalty, AI-based systems can also help improve workflows and improve the customer experience at the sales stage.
Underwriting processes
Using artificial intelligence in insurance underwriting processes is changing the industry. AI is enabling insurers to process data faster and more accurately. It can also help carriers build loyalty from the first point of contact with a customer.
Insurance underwriting involves evaluating risks and determining whether to issue a policy. The process involves a large amount of data collection. The data is usually gathered through manual, time-consuming processes. Often, the process is hampered by errors or inaccurate information. By using AI, insurers can process data more accurately and quickly, freeing up time for more valuable tasks.
AI-driven systems can also provide real-time data-based dynamic pricing. AI can also provide insurers with predictive insights into policyholder behaviour. These insights can then be used to identify emerging risks and new revenue sources. This also helps insurers strategize effective loss control and long-term retention roadmaps.
It’s also important for carriers to evaluate the impact of AI on customer-facing processes. It’s estimated that front-line underwriters spend about 30% to 40% of their time on manual tasks.
Insurers can also use AI to help prevent underwriting leakage. The system can quickly scan new business submissions and other documents for useful information. This can help carriers better understand their customers’ needs and improve the quality of underwriting.
AI can also help insurers strategize profitable pricing models based on risk sharing. Insurers can also use machine-learning models to help price risk more accurately. Machine learning algorithms can detect patterns, assess errors, and recommend data standards.
Insurers can also use data to assess risk and adjust premiums. This is especially beneficial during a time of increased natural disasters. These types of disasters are increasing every year, and insurance providers need to prepare for heavier losses.
The insurance industry has not yet fully embraced AI technology. However, many insurers are exploring innovative approaches to using the technology. They are also taking steps to ensure the technology is fully integrated into their underwriting processes.
Claims processing
Streamlining the insurance claims processing process will help to reduce costs, improve customer satisfaction, and provide better service. However, the process can also be time-consuming and complicated. The use of artificial intelligence software is one way to streamline the process.
Machine learning can be used to analyze customer data, spot patterns of fraud, and assess the likelihood of a claim.
Using artificial intelligence can be a smart move for insurers. As long as there is enough data to power it, it is likely to be the best way to automate repetitive tasks.Automating the process with AI will help reduce human error and the cost of doing business. Insurers can also gain a competitive edge by providing a more personalized service. In addition, automated claims systems can improve customer satisfaction and improve the overall consumer experience.
Insurers can automate claims processing in a variety of ways. This includes using Natural Language Processing to scan documents for relevant information. Optical Character Recognition is also used for this purpose. Lastly, Natural Language Processing can be used to identify and solve liability issues.
There are a number of AI-powered platforms that can automate the claims processing process. They can transfer all necessary claim documents in real time, validate the eligibility of customers, and assess their diagnostic data. They can also identify data patterns in claim reports and liberate companies from fraudulent claims.
AI is a good fit for claims processing because of the amount of data insurers have available. However, there are still limitations to its implementation. For instance, obtaining third-party data is often difficult due to privacy concerns.
A recent study by McKinsey estimates that AI implementations could improve productivity in insurance processes and reduce operational costs by 40% by 2030. The benefits of AI in insurance include reducing costs, facilitating faster workflows, and generating more revenue streams.AI can also help insurers expand partner networks, modernize their legacy systems, and create new types of policies that appeal to consumers’ specific needs
Conclusion
AI can be programmed to understand large amounts of data and learn to perform specific tasks. These machines can also be taught to adapt to new conditions and climates. These changes can include instructions to take safety measures when necessary. They can be used to assist in customer service or even upsell. However, they will need to be trained properly to do so.
The public discussion of AI must move beyond pessimisticview of robots taking over jobs and idealistic view of AI taking over complete human tasks. It should take a pragmatic approach and take into account the many implications of AI. AI is already impacting our everyday lives, in ways both pleasant and unpleasant. Understanding what these implications will be for society is essential.
AI tools are emerging, but the use cases for these tools are still up for debate. Intelligent agents and chatbots can’t match human problem-solving capabilities beyond simple scripted cases, and robotic process automation can be a slow way to automate complex production processes. Meanwhile, deep learning visual recognition systems can recognize images in photos and videos, but they require huge quantities of labelled data to learn how to make sense of complex visual fields.
Artificial intelligence in the insurance industry is a promising innovation for the future of the insurance industry.By leveraging AI in insurance, insurers can improve policy administration, underwriting, billing, and customer relations.