5 Use Cases of AI to Show How It Is Transforming the Industry

Imagine the time when people used to go through a lot of clumsy paperwork and tedious formalities just in order to raise an insurance claim. The process of buying insurance policies, disbursing claims, policy renewal, etc., had been extremely complicated until we entered the age of the internet.

Although the internet made a lot of things easier for the insurance companies, there were still many pain points left to be addressed. This is where the role of AI has been commendable, as it has revolutionised the insurance sector completely. No one would have ever imagined buying an insurance policy just with a few clicks. None of us had ever thought that we can raise an insurance claim just by uploading pictures online. However, not all of these changes in the insurance sector happened by intention. Events like COVID pandemic and some big natural calamities forced the insurance industry to adopt the new age AI technologies.

If we look at the data, the Covid-19 pandemic has caused more than $55 billion in losses to the insurance companies. This figure only comes second to the financial loss the industry suffered due to hurricane Katrina. Such catastrophic events do force various business sectors to evolve, and the insurance industry is one to witness these changes. Before starting on what use cases AI is offering in the transformation of the insurance sector, let us get a glimpse at some key figures.

Here are some numbers to support our point:

• According to the research done by McKinsey, around 55% of the significant work which is performed by insurance companies is expected to take root in automation in the upcoming decade. In addition to this, 70% of the insurance-related tasks such as claims, underwriting, finance and operations will see a significant change in scope. As a result, people working in insurance companies will require upskilling and reskilling to stay relevant in this industry.

• Accolade Inc's Maya Intelligence platform is taking the help of machine learning and AI to assist patients in selecting the most appropriate, cost-effective insurance coverage plan depending on their medical history and other sets of data. Right now, Accolade is providing this service to more than 1.1 million clients.

• 70% of potential insurance customers will trust an insurance company if it is readily available via messaging to provide sensible advice, solve their queries and help them in buying insurance. Bupa is one of the best international health insurance groups, which is serving more than 33 million people worldwide using LivePerson's Conversational Cloud in order to connect with their clients over Apple Business Chat, Whatsapp, and SMS in addition to providing call centre-based services.

What Does All This Usage of AI Actually Mean?

The above real-world stats of the finance industry clearly depict that making investments in AI and the related technologies such as machine learning, deep learning, and predictive analytics is now one of the significant talk points in decision-making meetings of finance companies.

The good thing about using AI is that its implementation actually pays off well. For instance, McKinsey came up with a massive number of 1.1 trillion dollars of upcoming investment in AI by the finance industry. So how does this enormous investment opportunity work for an insurance company? Let's find out.

Use Case 1: Streamlining The Claim Process

In the age of the internet, customers are always looking for on-demand, real-time processing of their queries and claims. Insurance companies can use AI to implement a new method of submitting claims, such as submitting them via smartphones, web portals, and insurance company mobile applications. The AI can assist the customer with the submission of claims and will answer the questions which might arise during the claim submission process.

Apart from this, at the end of filing the claim, AI can check if a customer has written the verified policy details and pass it through the fraud detection algorithm before sending the claim to the insurance company to start the settlement process.

Cost efficiency evolution per industry, percentage is normalised to 100% in 2009 to take it as a reference point.

(As from the data-driven out by McKinsey & Company in the above graph, we can see that over the years, the insurance industry is the one which is falling behind when it comes to addressing the issues and the costing of its operations.)

Besides this, AI-powered chatbots can help in keeping customers in the loop about the latest updates on their claim process. Meaning they don't have to manually connect with a customer care representative to find out how many steps have been completed in their claim process.

AI can be used to detect and capture documentation. It will also ensure that the documents are not fake with the use of character recognition. The AI has come to that level where it can read handwritten text as effectively as humans.

There are so many telematics and onboard sensors in cars, home assistant devices, and fitness trackers in watches, along with various types of IoT devices that allow insurance companies to constantly collect the needful data from customers to provide them with better services.

What's That One Thing Which is Good About AI in This Case?

Well, with enough data provided by the insurance company. AI, with the help of Machine Learning (ML) and Deep Learning (DL) algorithms, can improve itself on its own. Meaning there is no extra programming which needs to be done in order to have better and quicker results out of the AI. Apart from this, insurance teams will be able to gain access to even more accurate data for insurance claims and complex insights about the customer which were not accessible in the past.

Fukoku Mutual Fund Insurance Company

In recent years Fukoku, one of the prominent mutual fund insurance companies, replaced its 34-strong claim assessment workforce with the implementation of an advanced form of IBM's Watson Explorer AI. The company was aiming to increase the productivity of their work by 30% and save 140 million yen each year.

Lemonade Transforming Its Insurance Process Via AI Chatbots

Lemonade is a company based in New York which is working towards making changes to the insurance industry from necessary evil to social good with the assistance of AI chatbots. The very first chatbot company is named Maya, and it is a virtual assistant that is capable of building perfect insurance plans for Lemonade customers.

The second AI chatbot which the company developed was called Jim, and it was explicitly designed to handle the claims. The best part about the Jim AI chatbot is that it was actually able to take one-third of the cases on its own. Its massive progress in helping customers to get their claims sorted via AI and machine learning resulted in the company gaining a value of $3.9 billion during the IPO in 2020.

Use Case 2: Adjustment of Claim in Shortest Time Possible

Everyone wants the claim process to be as simple as possible. This saves time for both customers and the company. With the implementation of AI, insurance companies are aiming to bring up that blazing fast speed of claim adjustments.

With AI, some of the intense, labour-heavy and inspection-driven tasks can be quickly taken care of. But why do we need AI for inspection purposes, you may ask?

Well, the stats are straightforward, property insurance rate adjusters are 4x times more prone to sustain an injury than construction work, this is a pretty crazy stat, but it's all true. As a result, we saw the implementation of flying drones equipped with AI to assess the damage to properties after Hurricane Harvey in 2017 Texas.

With these drones, insurance companies are able to create comprehensive datasets, which include Geospatial data, HD video or imagery, and lots of data collected from IoT devices as well. Now let's see which companies around the world are using AI and ML to automate the automotive insurance sector.

Liberty Mutual Insurance Using AI for Assessing Vehicle Damage

Liberty Mutual is a well-known company and probably one of the first ones to ever provide insurance policies to customers. The company is taking assistance from Solaria Labs in to develop an AI that estimates auto damage. This is done by conducting comparative analyses of anonymous claim photos.

The AI is able to access the damage on the vehicle and provide the repair estimates post-accident on its own. As a result, no one has to actually meet the customer in person to come up with the cost of the insurance claim.

Apart from this, Liberty Mutual tied up with MIT in order to create a five-year collaboration to support AI research in computer vision, understanding of the computer language, data privacy and security as well.

Use Case 3: AI Helping in Detecting Insurance Claim Settlement Frauds

Insurance claim frauds cost a minimum loss of 80 billion dollars annually in the United States alone. Faking an accident, injury, or any form of damage which can be used to claim against an insurance policy will come under a claim settlement fraud. With the use of AI in insurance claims, a company can make the following benefits and steer clear of these sorts of claim frauds.

AI could use the historical data available to find the probability of an incident occurring. Not only that, it will also calculate the cost of claims by accessing typical claims which were raised by the policyholder in previous years.

With the implementation of AI, insurance companies can detect fraudulent activities and patterns. These will trigger automatically when the insurer is filling claims repeatedly. With the clubbing of all these scenarios, AI could create a list of high-risk profiles so the insurance agency can take a deeper look at their case when they come for future claims.

Use Case 4: Use of OCR for Fast Document Digitization

Optical Character Recognition(OCR) is the AI technique which is being used by insurance companies to scan customers' documents when they avail for an insurance policy and claim from the insurance company as well. There are certain benefits which insurance companies are able to enjoy with the use of AI-based OCR. The first advantage is increased security. This is because physical documents are hard to track but scanned documents can be easily tracked down. Also, accessibility of digitised documents can also be restricted to certain employees in the company who are taking care of that specific case.

Apart from this, employees of the insurance company don't have to manually write or type the information of customers on their portal as it will be taken from the scanned documents automatically.

Furthermore, the OCR also helps in easy data search as you can simply type in the search keyword, and the document will appear on your screen. The cost of producing a single physical document is more than creating 1000s of digitised documents.

Physical documents require paper, a printer, ink, and a place to store them as well while with digitised documentation, everything is done by a single system and documents are saved online. All of this leads to __saving 80%__of the individual processes, which are pretty standard and can be quickly done by an automated machine.

OCR is a great way to streamline the KYC process for customers and provide them services in the shortest time possible. The use of AI reduces the cost of customer onboarding while, at the same time, also increasing the speed of car insuranceclaim processing and customer satisfaction.

According to the reports shown by the EY insurance industry Outlook of 2021, we can see the following results.

• __58% of life insurance customers__prefer purchasing the insurance plan online rather than purchasing it from the insurance company's office.
  
• Apart from this, 68% of customers in Sweden and 52% in Spain prefer to search for an insurance policy online before making a decision about whether to purchase it or not.

These numbers are pretty essential as it shows how certain developed countries are moving toward digitalising their insurance process and implementing AI to streamline the day-to-day tasks at an insurance firm.

AXA Extractions of Data from Hand-Written Forms

AXA is one of the great leaders in the industry of finance and insurance as they offer complex services in the field of health and property insurance to their customers.

(Source:- Snapshot of web interface prototype designed by Rare Technologies For AXA. )

The AI company wanted to increase the efficiency of taking in handwritten forms so as to extract information from them. At the same time, AXA also wants to reduce human labour in order to save money and reduce the chances of human error.

They collaborated with RARE technologies to build a deep learning solution which gave the final POC output in the form of analysis, classification of incoming documents, extraction of hand-printed field values, a web interface as a prototype to test out the working, and implementation of REST API which calls the service in a pragmatic fashion.

Use Case 5: Faster & Better Underwriting Process

The increase in complexity of insurance policies and fraudsters levelling up their strategies, making them elaborate, results in insurance companies being more careful in their underwriting process, rules-based evaluations and other tasks which deal with particular sets of risks.

With the advancement in the field of computer science, we see connected devices everywhere, from our mobile phones to our cars, everything is connected, and with all these connected devices, a mature insurer can easily find better ways to make appraisals.

The AI-enabled underwriting process, which is paired with IoT devices, will help insurance companies carefully track down the required data in real time.

For example, with the use of a GIS data stream, an insurance company can completely eliminate the need for in-person property inspections, as well as monitor the state of the property over the course of time to come up with an accurate policy pricing.

On the other hand, this technology is also beneficial for raising appraisals for massive industrial infrastructure to estimate the recovery cost of any sort of damage and operational mishaps. A single seismic dataset is hundreds of gigabytes, which results in multiple terabytes of information processing and interpretations for a single day.

This AI processing of 3D seismic datavastly improves our picture of the earth's surface and makes it possible for scientists to completely abandon the need to drill a multi-million dollar hole in the earth to get the same results.

(Source:- Nvidia showcasing how different tasks in oil and gas mining acquire data on a daily basis.)

Besides this, insurance companies can take needful information from the above data and churn it into predictive analytics of their own to find out the levels of degradation, perform automatic defect inspections, and find the potential failure rates and other risks which come from operating with such machinery.

Baker Hughes Partnered with NVIDIA for Changing Future of Oil & Gas

Massive oil companies like Baker Hughes have to deal with oceans of data before they can take out a single drop of oil. As a result, in 2018, the oil and gas company came up with the plan to partner with Nvidia to use AI and GPU accelerated computing to distil the incoming data in real time. This leads to a reduction in the cost of finding, extracting, processing and delivering oil.

With the use of AI, the company was able to unlock the data by predicting the machinery failure, optimising the supply chains, implementing neural networks and more.

The following use of AI results in:

• 50% - 75% increase in accuracy of finding anomalies before drilling.
• 10x increase in an interactive exploration of massive datasets.
• Reduction of OPEX by 15% across the full stream.

Future of AI in The Insurance Industry

Truth be told, the insurance industry is under a lot of pressure as we are now living in a post-pandemic world. The thing is, AI is not the solution for every single problem which arises in this industry. It can indeed reduce the cost and time spent on most of the tasks commonly performed by an insurance company. Still, it isn't a single solution to all the problems on which the whole insurance industry needs to focus at present.

The above use cases that we have mentioned depict how AI can be used in the insurance industry. But these are some of the few ways in which AI is currently helping insurance companies sort their things out. These use cases are making sure that insurance companies are moving towards a digital-first customer experience and providing a technically advanced line of products to their customers.