Insurance Article, The Insurance Times 2021, The Insurance Times March 2021

HOW ARTIFICIAL INTELLIGENCE IS PROGRESSING IN INDIAN INSURANCE INDUSTRY

 

Technology has led to many positive changes around us and insurance industry is not staying behind in making the most of the technology to improve their functioning and services to customers. Traditionally, the insurance industry’s focus has been around the policy and product but there’s now a shift in the trend.

The insurers are more and more leaning towards becoming a more customer-centric organizations and Artificial Intelligence (AI) has been at the forefront of this mission. Artificial Intelligence (AI) is concerned with understanding the nature of human intelligence and designing intelligent artifacts’ which can perform the tasks which, when performed by humans, are said to require intelligence.

Any major advancement in technology brings with it a range of opportunities and challenges. While AI is likely to bring substantial economic growth, it is being predicted that a number of jobs would be lost due to the automation. Therefore, it is necessary to put required policy and infrastructure in place.

Though the field of AI has been an area of extensive research since the term was coined in 1956, it has recently only led to large-scale deployment of intelligent applications for different domains and tasks. The works in the late fifties and early sixties were in the direction of development of general techniques, which could be applied in several domains. The results were not very encouraging and it led to the first winter of the field, which started in the late sixties and continued till the late seventies.

 

Using AI in insurance has enhanced customer experience by understanding customer behaviour, streamlining the claim process, improving underwriting and preventing fraud. Embracing AI will help the insurance industry in effective customer engagement and in the long run a deeper penetration of the market.

The present sharp rise in the number of AI applications is due to several factors. Firstly, during the last few decades, the computational power and storage capacity of computers has been increasing while the cost has been decreasing due to the advances in the electronics used in complex digital circuits. This has made computing more and more affordable.

One of the main requirements for application of AI is the availability of high computing power, large memory and storage, which have now become affordable. The cloud technology has supported it further. It has made it possible to deploy AI applications without high upfront cost. The second important factor is the advances in the AI techniques, such as deep neural networks, etc.

Though the concepts such as machine learning, neural networks, etc, have been there since the sixties, the sophistication of algorithms has increased substantially after several decades of research. The progress in machine learning has made significant impact in the practical applications of AI.

Another area of AI where significant progress has been made, is natural languages processing used in machine translation, user-interface, etc. Using these advances in the technology, a number of applications were developed to assist people in natural language. These were called chatbots and were used for the tasks such as answering the questions of customers.

Some companies such as Google, Microsoft developed chatbots to perform several other tasks on behalf of the users. These are known as virtual assistants. AI has also been playing a major role in helping the insurers tackle its two biggest challenges — penetration and simplifying the customer servicing at various touch points.

IMAGE 1- Insurance Sector Changes

The insurance continuum starting with marketing to lead generation to quotes to underwriting and the various aspects of servicing a customer has many touch points and this where AI can simplify and address routine tasks thus taking the insurer’s reach deeper in the market.

The industry is using AI to rationalize its operations and connect with the audience in an effective manner. Yet these are still early days for AI in insurance industry in India. Insurers have just started experimenting. Several insurance companies are using AI/Big Data only for their marketing or sales campaign or simple claims handling tasks.

In India, AI is yet to be explored in many aspects of insurance like customer service, pricing, risk assessment, fraud, and customer demand. To get the right insight from AI, we need a larger set of quality of data that can be used efficiently. Some of the most common usage of AI in India is usage of chat boxes that helps in faster claim processes, providing policy informations, documentations and others.

AI in India has a lot of potential. Greater adoption of AI in the sector will help streamline the customer acquisition process as well as servicing process. Insurers will be forced to redefine age old processes while customers would find things much easier – like faster underwriting or claims settlement. The advent of technology and growing adaptation of digital usage will lead to a lot of online interactions and claims and AI seems to be the right answer for insurers to provide the best customer service.

 

AI can be divided into two high level categories:

Recently there was a tweet from Mat Velloso – “If it is written in Python, it’s probably machine learning. If it is written in PowerPoint, it’s probably AI.” This quote is probably the most accurate summarization of what has happened in AI over the past couple of years. Even Vladimir Putin said: “The nation that leads in AI ‘will be the ruler of the world.” Beyond all this hype, there is a lot of real technology that is being built.

AI is coming to the enterprise world – where the rubber meets the road Image recognition, speech recognition, language translation and sentiment analysis have already been deployed in the consumer world by Google, Facebook, Amazon, and others. These technologies will move into the insurance world to solve sharp, focused problem areas such as:

 

  • Knowing the sentiment of the customer
  • Identifying fraudulent claims
  • Identifying risk profiles to drive positive selection bias
  • Predicting customer behavior
  • Reducing customer churn

 

While some of these have already been deployed or piloted by some carriers, the technology will now be available “at scale.” Thus, enabling not just the larger carriers, but carriers of all sizes.AI is nowhere yet close to the level where it can entirely replace humans…except in movies.

However, AI has now reached a level where it can be the best tool that humans can use to deliver their services better. Insurance has the unique challenge of very low customer engagement and customer loyalty. AI can be a great asset to enable insurers to engage with every single customer at a personalized level and create the much needed connection – financially and emotionally.

IMAGE 2- How Companies around the world are using AI?

Machine learning (ML): Techniques that automatically learn from the data. All predictive models fall in this category. Generally, this is what business users understand when they hear the term AI. ML based solutions can add value to insurers – irrespective of the mode of delivery – delivered as a standalone model (standalone AI), or delivered as a part of a process, service or product (embedded AI).

 

Symbolic AI (SAI): Techniques that don’t automatically learn from the data. Human experts are needed to create the business rules. Underwriting or claim rules coded in IT systems are examples of this category. Insurers already have in-house capabilities for creating and implementing complex business rules. Hence, SAI packaged as ML and delivered in standalone AI mode is highly unlikely to survive through the later stages of the AI hype cycle. Real value can only be added through embedded AI mode.

 

Let’s categorise the gaps in four high level categories and see how AI is enabling start-ups to address these gaps:

 

Data Gaps: A data gap is created when some data fields are needed for data analytics-based decisions but the insurer is not able to capture them. Players are attempting to provide external data about the customers. They are leveraging machine learning-based de-duplication and linking technologies to identify a unique customer and then provide additional data about the data subject from external data sources.

Some players are helping insurers digitize their internal data by improving data capture at each stage of insurance operations. For example, optical character recognition (OCR) and then natural language processing (NLP) are used to capture and logically store data from existing physical documents.

 

Process gaps: A process gap is created when new technologies having the potential to transform one or more steps in insurance value chain become available, but the insurer is not able to adopt it. Building standalone machine-learning based predictive models for different stages of the insurance value chain to predict propensities related to fraud, cross-sell, up-sell, retention, claims, and so on, is one of the quickest ways to enter the insurtech space and hence is one of the most crowded areas.

In the last couple of years embedding AI in processes, services and products, to deliver an ‘intelligent’ or customized package has become an area which is attracting a lot of attention and it’s expected to continue this year. Robotic process automation (RPA) players are using SAI to create a large set of complex rules to improve degree of automation in insurance processes.

Blockchain players are primarily relying on a different IT technology (the distributed ledger) and aspects related to smart contracts – in reality they are simplified contracts, based on federated rules– which are handled through SAI. For reasons of speed, efficiency and customer satisfaction there’s a growing appetite among insurers to accept automated analytics in claims images or video, customer voice, claims summary reports and so on. Cloud-based predictive services leverage deep learning to train machine learning-models on unstructured data sources like images, texts, videos, and voice. These pre-trained models are then offered to insurers in an off the shelf package.

 

Product Gaps: A product gap is created when new technologies, changing lifestyles and changing business models create new risks or new ways of addressing old risks. Start-ups in the wider world of IoT (the Internet of Things) are offering usage based insurance (UBI) solutions such as telematics for motor and health insurance leveraging a wide range of machine learning algorithms to normalize and analyze the big data which is generated every second.

Start-ups supporting agricultural insurance operations use weather and crop data collected through satellites, drones and weather monitoring stations. ML algorithms are used to normalize and analyze this data.

 

Customer Interaction Gaps: Emerging technologies have changed customer behaviours and expectations. This creates a gap in customer-facing insurance operations such as distribution, policy servicing, and claim settlement. NLP based machine learning techniques are enabling chatbots to understand customers’ queries.

Then, SAI based rules are employed to find appropriate answers to their queries. SAI is enabling online or app-based distribution platforms to recommend the most suitable insurance products quickly by asking an intelligently-ordered minimum set of questions. Machine learning-based algorithms then predict the purchase preferences of the given customer and appropriately customize the insurance offering.

 

AI in Insurance:

Insurance is an old and highly regulated industry. Perhaps because of this, insurance companies have been slower to embrace technological change compared to other industries. Insurance is still steeped in manual, paper-based processes that are slow and require human intervention.

Even today, customers are faced with time-consuming paperwork and bureaucracy when getting a claim reimbursed or signing up for a new insurance policy. Customers may also end up paying more for insurance because policies are not tailored for their unique needs.

In an age when most of our daily activities are online, digitized and convenient, insurance is not always a happy customer experience. There would be a global push by insurance companies to augment their technological capabilities so that they can do business faster, cheaper and more securely. In the past few years, there have been some prominent examples of insurers investing heavily in Artificial Intelligence solutions.

IMAGE 3- Reasons for adopting AI.

Artificial Intelligence has shown its substance in various business verticals by rapidly creating controlled, digitally enhanced automated environments for maximum productivity. Apparently, Insurance companies, in particular, have a lot to gain from investing in AI-enabled technology that can not only automate the scheduling of executive-level tasks but can also enrich service quality by helping agents make right decisions and irrefutable judgments.

Insurance companies are striving for a technologically advanced system that helps keep all their employees synchronized. These employees vary from agents, brokers, claim investigators to market and support team. These group of employees coupled with redundant processes create layers of confusion in Insurance ecosystem. To make the system more refined and efficient, they should opt for stable and consistent AI-powered solutions that can penetrate the layers of confusion and propel clear value proposition towards customers.

 

How Are Insurance Companies Implementing Artificial Intelligence (AI)?

Insurers are using AI to provide better, faster and cheaper services to customers. Artificial Intelligence (AI) has become a buzzword in the insurance industry. Still, the industry has made significant progress in AI implementation, although we are still in the early days.

At its simplest, Artificial Intelligence (AI) is a set of computerized tools designed to achieve objectives that usually require human intelligence. From a business perspective, AI can be used to conduct operations in a faster, cheaper and more accurate way. AI can help automate labor intensive processes, leading to lower costs and saved time.

AI can also be used to understand customers better — companies can use AI to analyze the data they have on customers to predict customer behavior, understand preferences and optimize price and product offerings. AI is comprised of many related technologies, some of which are:

 

Machine learning: involves training computers to identify patterns in data and/or predict outcomes. Other AI technologies are applications of machine learning. Machine learning is often used to develop quantitative trading strategies.

Deep learning: an application of machine learning where a model can analyze and draw conclusions from data, and solve problems without being trained or given explicit instructions or frameworks. These models learn by themselves.

Neural networks: algorithms designed to mimic the human brain and recognize patterns in data. They can identify, classify and analyze diverse data, and can find patterns that are too complex for human programmers to write code for. A fun example of deep learning and neural network is Goolge’s QuickDraw, a sketching game which uses a massive database of user sketches to accurately guess what you’re drawing.

Natural language processing: helps computers understand, interpret, and respond in written text or speech. This tech is commonly used by chat bots. AI algorithms are used to classify and study data, and identify relationships When applied to data sets, AI can be used for pattern recognition, optimization and prediction AI can classify and analyze data in different formats: text, speech, image, video, etc. It can also work with structured (i.e. labelled data) and unstructured data. Machine learning algorithms learn by being fed large data sets of labelled data. Once they can identify the correct conclusions from known data set, they can be applied to real-world problems.

 

Experts estimate a potential annual value of up to $1.1 trillion if AI tech is fully applied to the Insurance industry. Of this, the business areas that can benefit the most are:

IMAGE 4- AI offers several promising technology-enabled solutions:

The Need for Sales and marketing: machine learning can be used to price insurance policies more competitively and relevantly and recommend useful products to customers. Insurers can price products based on individual needs and lifestyle so that customers only pay for the coverage they need. This increases the appeal of insurance to a wider range of customers, some of whom may then purchase insurance for the first time.

Risk: Neural networks can be used to recognize fraud patterns and reduce fraudulent claims. According to the FBI, non-health insurance fraud in the US is estimated at over $40 billion per year, which can cost families between $400–700 per year in extra premiums. Machine learning can also be used to improve insurance companies’ risks and actuarial models, which can potentially lead to more profitable products.

Operations: Chat bots using neural networks can be developed to understand and answer the bulk of customer queries over email, chat and phone calls. This can free up significant time and resources for insurers, which they can deploy towards more profitable activities.

 

Four areas where AI can help the Insurance industry:

There are many examples of how insurers around the world are implementing AI to improve their bottom line as well as the customer experience. There are also numerous startups that are providing AI solutions for insurers and customers. I will cover a few interesting cases here.

Health Insurance:

Over the years, Artificial Intelligence (AI) tools have been used to fill gaps in mental health care: be it the diagnosis or detection of the early signs of mental health issues. In a world where the cost and complexity of health insurance is increasing, Accolade Inc’s Maya Intelligence platform uses machine learning to help patients and employers select the most relevant and cost effective health insurance coverage.

In 2018, SwissRe and Max Bupa Health entered into a partnership with Indian fitness tech startup GOQii Health. GOQii uses data from wearable devices and their own AI-driven ‘wellness engine’ to track health vitals and provide healthy living advice and risk reports to individual users. When partnering with or acquiring these AI and tech-driven startups, insurers are betting that it will lead to fewer claim payouts and more attractive premiums for health insurance customers down the line.

The ability of AI algorithms to locate the relevant information has been found to be of great use. Watson for Oncologists developed by IBM helps a doctor in finding the relevant material from a large number of papers/documents which could be of use in the case at hand.

It analyzes both structured and unstructured data. AI is helping in providing personalized treatment to the patients. Every patient is a different individual and may need a different treatment. Further, a disease may have thousands of subtypes requiring different treatments consisting of a combination of drugs.

For instance, it is being realized that cancer has thousands of subtypes and each subtype requires different combination of drugs for effective treatment. On the other hand, pharmaceutical companies rely on large-scale randomized clinical trials for testing new drugs. This limits the number of cases, it would be effective. This is why treatment often requires a trial and error approach.

Once we have sufficiently large database of cancer cases, it becomes possible to find cases similar to the case in hand and there is a good probability that the treatment found to be effective in the earlier cases would be effective in the present case too Some companies are developing AI-based systems which can provide consultation. It provides consultation to the user based on the symptoms reported.

It asks the user few simple questions in spoken natural language and the user can answer in natural language. It searches a large database of symptoms and provides the appropriate medical advice. In case it finds necessary, it advises the patient to approach the doctor immediately.

 

Auto Insurance:

As far back as 2017, US insurer Liberty Mutual unveiled a new developer portal through its innovation incubator Solaria Labs. This open API portal combines public data with proprietary insurance data to enable the creation of better insurance products for customers.

One such product was reportedly a mobile app that allows drivers involved in accidents to assess damage to their car in real-time using their Smartphone camera. The app would also provide repair cost estimates. The AI powering the app will be trained using thousands of images of car accidents.

Ant Financial, the Chinese fintech firm part of the Chinese giant Alibaba Group, released software called Ding Sun Bao to analyze car accident damage and process claims. Ding Sun Bao uses machine vision, enabling drivers to take pictures of their damaged car using their Smartphone camera.

 

Self-driving car is a high-potential application of AI. Several companies including Google, Uber, and Tesla are testing their models on the roads. In Singapore, driver-less bus is being run under trial. So far very few accidents have been reported and the analysis shows that probability of accident with driverless cars is less than human driven cars.

It is expected that the number of accidents by self-driving cars will be much smaller than the human-driven cars Self-driving cars use light detection and ranging (LiDar) technique which uses laser beams to create 3D image of the physical world around the car. It uses laser beams to calculate the distance, speed and shape of the moving objects like another car, pedestrians, etc. Apart from roads, the technology can be used by those who can’t walk due to physical limitations. Several companies are competing with each other in this area of technology.

 

Fraud Detection & Claims:

To combat fraud, insurers are using AI-driven predictive analytics software to process thousands of claims each month. By analyzing the claims in milliseconds based on set rules and indicators, AI is able to identify which may not be legitimate, reducing the number of fraudulent claims slipping through.

These indicators include things such as frequency of claims, past behavior and credit score. By leveraging machine learning, Chinese Insurer Ping An saved itself US$302 million from fraudulent claims in one year. It also achieved a 57 percent increase in accuracy in fraud detection from the previous year. From fraud detection to underwriting, AI technologies are reimagining every facet of APAC’s booming insurance industry. By reducing the risks and streamlining processes, it can help companies drive efficiencies and deliver more personalized products and services – the key to future success.

 

From smart chatbots that offer quick customer service round the clock to the array of machine learning technologies that spruce up the functioning of any workplace through its automation power, the expanding potential of Artificial Intelligence in Insurance is already being used in many ways, as covered in our last blog.

With increased awareness and resources about the game-changing influence of AI in the Insurance industry, the initial hesitations and shallow discomfort around the its implementation are now fading quickly as it begins to trust in the caliber and numerous opportunities brought forward by Artificial Intelligence and Machine Learning.

The only question that remains is – how far can we push its capabilities? AI-based chatbots can be implemented to improve the current status of claim process run by multiple employees. Driven by Artificial Intelligence, touchless insurance claim process can remove excessive human intervention and can report the claim, capture damage, update the system and communicate with the customer all by itself.

Such effortless process will have clients filing their claims without much hassle for e.g. an AI-powered claims bot can review the claim, verify policy details and pass it through a fraud detection algorithm before sending wire instructions to the bank to pay for the claim settlement.

IMAGE 5- Application of  Artificial Intelligence

Advanced underwriting:

IoT and tracking devices yield an explosion of valuable data which can be utilized to make the process of determining insurance premium upright and regulated. Fitness and vehicle tracking system in both health and auto insurance sector give rise to the dynamic, intelligent underwriting algorithms that cleverly control the way premium is dictated.

Using Artificial Intelligence and Machine Learning, insurers can save a lot of time and resources involved in underwriting process and tedious questions and surveys, and automate the process. Insurance bots can automatically explore a customer’s general economy and social profile to determine their living patterns, lifestyle, risk factors and financial stability.

Customers who are more regular in their financial patterns are qualified to feel safe through low premiums. Since AI is more capable of strict scrutiny of gathered data, it can predict the amount of risk involved, protect companies from frauds and give justified insurance amount to customers. MetroMile, a US-based start-up, has established such dynamic underwriting system known as ‘pay-per-mile’ where usage of a car determines insurance premium.

Here, an AI-based device installed on the vehicle by the company uses a special algorithm to monitor miles, jerks, collisions and frictions, speed patterns and other car struggles on the road, and it collects detailed data essential to decide whether or not drivers deserve low premiums.

 

The process of underwriting is often viewed as an art based on personal judgment, but AI technologies have also worked their way into this area of insurance, making the process increasingly scientific. Insurers are now using advanced analytics and machine learning, as well as additional sources such as satellites and the Internet of Things devices, to help get a more holistic view of risk, as well as to determine which submissions to review in the first place.

Japanese insurance firm Fukuoka Mutual, for instance, has been using a cognitive machine learning based system to scan medical records and data on surgeries and hospital stays to calculate payout. Meanwhile, Indian company ICICI Lombard has created an AI-based cashless claims settlement process, which can be completed in just a minute.

 

 

Development of insurance product according to needs:

At the heart of artificial intelligence lie data, and the availability of data in the insurance workflow. With good quality data and machine learning for helping to define algorithms and business processes, insurers can be in a better position to know when and how to communicate with the consumer.

The industry can begin to gain a better insight into individual consumer habits, their needs according to life stages – such as home, location, family and social activities – as well as preferences. This puts insurers on the road to creating a more seamless way of selling insurance, towards an optimum mix of insurance products for a particular customer, at the most appropriate time and across the right channels.

Changes are coming and creating a less invasive, more responsive experience for policyholders. Using public records data and other risk attributes where applicable (such as medical history, prescription history, contributory databases) consumers can experience quicker, better processes and allowing insurers to set appropriate life insurance premiums. We refer to it as the use of social determinants for healthcare risk stratification.

 

Another area insurance companies are using AI is to inform their product and policy design, By streamlining and speeding up the collection and analysis of massive data from owned channels, third-party sources and agents, insurers can use machine learning to discover customer trends and interests in real time.

These insights are then being used to develop and improve product and policy design. Chinese online-only insurance company, ZhongAn, is a company that continually releases innovative products and policies, many of which are developed with the help of advanced AI techniques such as machine learning and image recognition. For example, they came up with niche policies to insure against cracked mobile screens and shipping return products.

 

Predictive Analytics for proactive measures:

Predictive Analytics backed by Machine Learning is now perhaps the heart of intelligent services across many business verticals that have adopted AI-powered solutions. However, this smart capability is not just aimed at driving future insight about customer’s preferences and tailoring relevant products.

Health insurance companies are coming up with rewarding pre-emptive care that is focused on encouraging customers to look after their personal well being. If a person remains healthy, companies don’t need to invest in claim payment and management process. For instance, Aditya Birla Health Insurance has planned wellness benefits to encourage customers to stay healthy. AI’s predictive algorithms scan past year’s claim activities and hospitalization data to provide incentives to customers to improve health & wellness.

This way, health risks will be minimized and so will be the company’s resources. Thus, nowadays, start-ups leverage AI’s unique potential to scour through piles of claim data and coverage patterns to be more proactive and anticipate health risks at individual level before they actually transpire.

 

By automating and applying cognitive learning to their data collection processes, forward-thinking insurance companies, including AIA Singapore, are also advancing their customer profiling capabilities. Equipped with the power to unify and derive insights from their internal and external customer data, insurers are able to build a more comprehensive picture of their customers, such as their insurance needs, interests and life stages, for more effective targeting.

Insurers can segment their audience based on these attributes, and use deep learning to predict the conversion rate of these segments. With such insight, insurers can then decide the relevant product recommendations for each customer segment. Insurance companies are also enhancing customer profiling with AI-enabled voice and facial recognition, which helps create biological customer profiles for fast and accurate verification, as well as the tracking of behaviors and attributes.

 

Marketing and relevant products:

Being a part of the competitive market, insurers need to capitalize on a vital marketing strategy which goes beyond the traditional cold calling approach. The old blanket methods are on the verge of extinction since digital disruption has already shaken the grounds of insurance field. Customers today seek sophisticated, luxurious and extremely personalized services with custom sales tactics.

Using the combined power of predictive analytics, NLP and AI in the insurance industry, agents can gain access to the full profile of customers and prospects. This data can be further analyzed to generate mature insight, accurate predictions on customer preferences and what exact products or offers should be added in their marketing activities.

Artificial intelligence is increasingly going to allow consumers to derive better value from their communication with the insurer’s interface. AI-powered service executives and advisor bots, for instance, can be leveraged to offer consistent counseling, recommendation and post-sales services to customers. It’s all about using automation efficiently for simple, repetitive tasks and simple questions, whilst bringing in human help, such as a human claims handler for more complex tasks, where they can add more value.

 

Faster settlement of claims:

The world is changing and insurance is changing with it. This change is being driven by customer expectation and technological advancement. Filing a claim has traditionally been time consuming, usually requiring human intervention and manual form filling. It is a part of the workflow that is ripe for generating efficiencies through greater digitization of processes.

It is also the element of contact with the consumer where the insurer can really enhance their brand and create a positive experience through data knowledge. This potential to create productivity gains through automated processing is a global phenomenon and it’s an area where insurance has tended to fall behind other industries such as telecoms or airlines.

With touchless claims or low-touch claims requiring minimal human intervention a new claim can in many cases be stratified and scored instantly and respective damages validated. Customers benefit from a better and faster experience without having to go through the proverbial ‘red tape’. The elements of fraud, waste or abuse can be made more visible by such data enrichment in the claims process.

 

AI and algorithms can be used to sort claims, dissect aberrations in data patterns and single out spurious claims. The machine learns from past patterns of fraud, across a claims database of the market that is as wide as possible, and is able to apply this predictive analytics to current claims.

Once the claims are sorted and stratified, the human resources of the claims handler can be applied to those cases where the benefit is greatest, such as the larger complex claims and those requiring a challenge or most likely to result in costly legal action. In conclusion, when leveraged at the right points in the insurance workflow, and powered by enough data, AI can bring many efficiencies and process improvements.

The bulk of these solutions are fuelled by use of artificial intelligence. It’s not wrong to say that AI is beginning to play a key role in enabling insurtech start-ups to bring ‘smartness’ to insurance. However, not all types of AI techniques can add value to insurance processes in the same way. In order to understand the role of AI, we need to understand what AI is and what it is not. Contrary to general perception, all AI techniques don’t automatically learn from the data.

 

How is Artificial Intelligence in Insurance addressing key challenges?

To be competitive, insurance companies need more customer insights, and the ability to turn these insights into actions, which requires focused effort and expertise. Many insurance companies struggle in this area which is why insurtech start-ups play a key role. They are able to move faster and identify these gaps and provide solutions.

The insurance industry, after the trade market, is another sector where it is hard to predict the next big paradigm shift. Given the tentative stability and natural catastrophes, insurance companies often stand on a trembling ground and confront massive challenges, even when it comes to adopting seamless and intuitive digital solutions such as Artificial Intelligence in Insurance.

The greatest concerns that loom over the insurance sector today are the subdued premium rate, mild interest rates, shifting consumer behavior, slow economic growth, need for regulations and technological innovations and blazing market competition. What boosts optimism, though, for these insurance companies amid much disruptive chaos is the fact that digital revolution is everywhere, increasingly transforming conventional business models and adopting digital solutions such as:

  • Bot-based advice system
  • Pay-as-you-go commerce
  • GPS-sensing software
  • Automated business processes

Over the last two years, there has been the widespread advent and adoption of AI across multiple industries). Many global financial companies and banks in those days relied on punch card and basic computing system to monitor customer activities. Hence, the concept of Artificial Intelligence in the Insurance industry analyzing data, anticipating results and helping with decision making is not so far fetched after all. AI could help insurance companies deliver service with efficiency and quality as it has done for major leaders in other industries such as Hospitality, healthcare, and customer care processes. The adoption of AI can facilitate:

  • Sudden disaster-caused damage analysis
  • Risk tolerance calculation and assessment for trading desks
  • Transaction analysis for banks and financial organizations
  • Selection of better investments based on preferences, risks and spending patterns
  • Consistent optimization of customer investments and insurance coverage
  • Claims analysis, asset management, risk calculation and prevention

IMAGE 6- Examples of AI

AI-powered opportunities for the Insurance market:

A promising study by Accenture and Frontier Economics has claimed that AI will increase 10-40% labor productivity in 11 western industrialized countries and Japan by 2035. If this optimistic projection is true, the economic growth is likely to double by 2035. Considering the current scenario, AI-based products will include insurance coverage for smart driverless cars, smart sensors and factories and cybercrime damages.

Furthermore, AI will also empower important processes like claims analysis, asset management, risk calculation, and prevention. For instance, property damage analysis can be conducted through image processing part of AI in Insurance. The same machine can be used to make an informed decision about investments based on intelligent algorithms. As financial sectors are brimming with the unprecedented bounty of financial, insurance and investment data, the need for integrating Artificial Intelligence in the Insurance industry can drive whole new growth for this industry.

Powerful data management tools of AI can help people size up and navigate through pyramids of data while also helping businesses create intuitive and interactive customer experiences. In a nutshell, here are a few good target areas where AI can achieve significant impact and emerge transformative for the insurance industry as a whole. The more AI is used in ever-increasing aspects of business, the more discussions there will be around privacy and security. There will be a need for laws and regulations to define how we use all this data.

 

Artificial Intelligence Can Enhance Customer Satisfaction in Insurance Industry:

In today’s digital age, consumers have begun to expect seamless interaction and personalized services from their insurance providers across any channels they wish to utilize. No matter the brand, customer satisfaction is king and insurers have started upping their game when it comes to speed of service and underwriting, increasingly delivering smaller, bit-sized policies that fit in with everyday needs. The buck doesn’t stop with personalization.

With vast reserves of data, a sea of customer queries and thousands of claims to process regularly, the insurance industry faces challenges associated with reaching out to customers in a timely manner with the right mix of products, that are ideally tailored to their needs and facilitating faster claims settlement.

In a situation such as this, artificial intelligence is proving to be a game changer with its ability to support the insurance industry, playing a role in the R&D process of data modeling, helping to shape custom-fit services and improved customer satisfaction.

In order to assess the current level of adoption of AI technology, there is a need to look into the applications which have been developed across the world. Applications have been developed in almost every walk of life. AI can help spearhead efforts towards increasing customer satisfaction by helping insurers understand the needs of customers better and deliver products that fit their risk profile and preferences.

 

IMAGE 7- Pros and Cons of Artificial Intelligence

 

In a country with low insurance penetration, AI could be the solution to improve the reach and profitability of insurance companies in India. The last three years have seen a massive upswing in the use of AI across different business verticals. AI is and will be disrupting the insurance industry.

Artificial Intelligence refers to intelligent software that can draw on data in order to autonomously control machines, produce forecasts, or derive actions. People also confuse Artificial Intelligence with Machine Learning (ML). Although both fields are similar, they are not the same. While Machine Learning analyses data and identifies useful patterns, AI goes a step further by learning from existing data and applying that learning to new situations. Most business applications use a mix of AI and machine learning tools. This is a rapidly growing field with a lot of promise that can impact many industry verticals.

Regulations and Policy

As AI applications touch several aspects of human life, regulations are needed to ensure safety of the people, protection of privacy, etc. For instance, in the area of transport, if autonomous vehicles are to be permitted on the roads or air, regulations are needed to ensure public safety.

A self-driving car must take care of enormous number of possible situations on the road. While deciding the permission to use the autonomous vehicles, it is necessary to assess the potential risks in both the situations i.e. when conventional vehicles are used and when AVs are used.

Regulations may be linked to the performance of the products. In this case, further use depends on the performance. If AI-based applications / services are found to be safer than human-based applications/ services, more use may be permitted. If it is found to be less safe, the use should be restricted till the further development of technology.

 

Regulations are needed to permit the use of AI in the critical domains like healthcare where the autonomous systems are expected to advice on the diagnosis and treatment which may affect the recovery of the patient. Regulations need to be made to ensure that the applications developed are not biased towards a specific view.

The biasing may be intentional when it is incorporated by the developer of the application. Sometimes, it may be incorporated due to the training data set. The developer may not do it intentionally. Policy is needed to make the public data available to the developers to promote the development of applications.

Several applications depend on the availability of large amount of public data. For example, the data on the traffic, road conditions may be necessary to develop applications for advising the drivers on the routes. It may be necessary to annonymize the data before making it public in order to protect the privacy of the individuals and organizations.

Policy is needed for making the results of R&D available to the public. Several R&D projects are funded by the Government in the country but often the results remain confined to a limited number of persons. Several countries have made legislations to make the results of the R&D funded by the Government available to the public by putting it in open-source domain. This ensures that the benefit of the public money reaches the public. As a policy, Government should also work on making people aware about this technology.

The Future of AI in Insurance:

While challenges appear to dismay the present market, insurers still like to view the potential of AI in the Insurance industry with optimistic eyes. To reap full range of benefits, insurance companies need to devise an enterprise-level strategy to implement AI in such a way that it offers more than just customer experience.

When it comes to image recognition, the overall damage analysis, cost estimation and claim settlement would be carried out by bots that scan through pictures and videos. This way, with time, companies can rely completely on image recognition technology for first level claim automation and subsequently, settle claims or resolve fraud detection in insurance automatically.

 

AI has the potential to transform the insurance experience for customers from frustrating and bureaucratic to something fast, on-demand, and more affordable. Tailor-made insurance products will attract more customers at fairer prices. If insurers apply AI tech to the mountain of data at their disposal, we will soon start to see more flexible insurance such as on-demand pay-as-you-go insurance, and premiums that automatically adjust in response to accidents, customer health, etc.

We will see insurance become more personalized, because insurers using AI tech will be able to understand better what their customers need. Insurers will be able to realize cost savings by speeding up workflows. They will also discover new revenue streams as AI-driven analysis opens up new business and cross-selling opportunities. Most importantly, the AI solutions described above can make it easier for customers to interact with insurance companies. This could result in people being more likely to purchase insurance.

 

Artificial Intelligence (AI) is likely to transform the way we live and work. Due to its high potential, its adoption is being treated as the fourth industrial revolution. As with any major advancement in technology, it brings with it a spectrum of opportunities as well as challenges. On one hand, several applications have been developed or under development with potential to improve the quality of life significantly.

As per a study, it is expected to double the annual economic growth rate of 12 developed countries by 2035. On the other hand, there is a possibility of loss of jobs. As per the available reports, the loss of jobs during the next 10-20 years is estimated to be 47% in the US, 35% in the UK, 49% in Japan, 40% in Australia, and 54% in the EU. In the era of globalization, no country can isolate itself from the impact of the advances in technology.

However, the benefits can be maximized and losses can be minimized by putting necessary infrastructure and policy in place. Though several countries have decided their strategy for AI, India has not yet formulated its strategy. Use of artificial intelligence and analytics will significantly improve customer experience. A shift from push products (traditional plans) to pull products (pure protection) driven by awareness among the internet population is expected. The industry will see more byte-sized products distributed through technology advancement.

 

To get ahead, insurers are using advanced analytics, machine learning and other AI-driven tools to compete with agile new players and elevate the customer experience. According to a study by PwC, more than 80 percent of insurance CEOs said AI was already a part of their business model or would be within the next three years.

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