How real AI is shaping the insurance industry

We can automate a majority of manual tasks with the help of AI so that insurance companies can provide their services faster, cheaper and with fewer errors.

22 days ago   •   10 min read

By Nisar Hundewale, Ph.D.

Artificial Intelligence (AI) was born from a wish to simulate human intelligence. While we are still years away from completely simulating the human mind, there are specific tasks involving learning and problem solving that machines do really well, sometimes even better than humans.

In the last decade, AI has become almost ubiquitous in our day-to-day lives. Every time you search something on Google, an AI runs in the background to give you the most relevant searches based on your location, browsing history, language etc. in fractions of a second. The ads being shown to you on the internet, the movies and songs recommended to you on streaming services - all use AI. You cannot even imagine something as mundane as commuting from place A to place B without the use of some form of AI. Even as I am writing this, the autofill tries to make my work easier by using AI in the background.

What is real AI?

People use Machine Learning (ML) and AI interchangeably. They are not the same. ML is a subset of AI and therefore, AI is much broader than ML. AI is used to refer to any machine that emulates the cognitive functions that are associated with human minds, such as learning and problem solving. ML deals with algorithms that allow computer programs to improve through experience.

There are some algorithms or systems that are totally rule-based and do not employ any kind of learning, which are still called AI. (seems unreasonable? Well.... I didn't coin the term). However, there are AI based learning systems that generate a set of rules while learning from the data.

Now, if we dive a little deeper into ML, we get Deep Learning (DL). It is so named because of the multi-layered neural network it uses. The fundamental units of the neural network called neurons are very similar to those in the human brain. However, the connections between them are way more simplified than in any human brain. If I had to make a Venn diagram to explain the how AI, ML, and DL fit together, it would be:

AI, ML, and DL Diagram

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How does AI work?

Moving AI from the limelight and hype of popular media onto a programmer’s computer or a scientist’s lab, you see that data fuels AI. Thanks to the internet and digitization in the last few decades we can collect tremendous amounts of information. AI is nothing but some math and programming performed to extract useful insights or build useful systems from this otherwise incomprehensible data.

ML works by combining large amounts of data with fast, iterative processing and sophisticated algorithms, allowing the software to learn automatically from patterns or features in this dataset. When you have loads of data, DL is better to use (over ML) because of its ability to find more complex patterns in the data. It is also the de facto way to deal with data in image or text format.

Let’s get into two main types of machine learning:

  1. Supervised Learning: Here, while training the algorithm, you have a full set of labeled data. The learning takes place under "supervision". Let's say, if you want your algorithm to differentiate between cats and dogs, you will feed it a set of cat images and a separate set of dog images while explicitly stating (labeling/annotating) the image. This basically sets your goal beforehand as showing an image of a different animal (i.e. a parrot) will result in a cat or dog label as that is all the model knows.
  2. Unsupervised Learning: In this case, you feed a dataset, which is a collection of examples without a specific desired outcome or correct answer. You rely on the algorithm to find a pattern, if there is one. There is minimum supervision required. You must be thinking how this works, right?

A common example of this is clustering, which is used to group data points that are similar.

An example of clustering

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The semi-supervised learning typically learns from a smaller portion of labeled (supervised) data and a larger portion of unlabeled data. Practically finding (labeling/annotating) large amounts of data may not be possible. The semi-supervised learning becomes the natural choice of learning. In a real-world scenario, these data points could be people and we are trying to find social networks of people that interact with each other or they could be products and we are finding similar products to recommend to customers.

Let’s explore in depth how we can leverage these algorithms and harness the power of this data in a specific industry.

How can AI provide sustainable growth to the Insurance Industry?

Insurance is an industry with a wide range of manual tasks. These manual paper-based processes are slow and require human intervention. Even today, customers have to deal with time-consuming paperwork when getting a claim reimbursed or signing for a new insurance policy. In the digital age, this leaves customers frustrated and has led to the use of AI in insurance.

We can automate a majority of manual tasks with the help of AI so that insurance companies can provide their services faster, cheaper and with fewer errors. McKinsey estimates that 25% of the insurance industry will be automated by 2025 thanks to AI and ML.

The specific areas that benefit from AI are:

  • Claims Processing
  • Risk Assessment and Standardized Writing
  • Claim Fraud Detection
  • Reinvent worker’s compensation with SaaS solutions
  • Better Customer Support with Chatbot(s)
  • Personalized Customer Services and enriched customer experience

Let’s look at each one of these briefly.

Insurance claims require a lot of manual work

Claims Processing

Claims processing is one of the most crucial and involved aspects of insurance, and it means that many insurers can make significant improvements to their business by streamlining the claims process. An enormous amount of work involved in claims processing is time-consuming and administrative. These tasks are prone to human errors, require data verification as customers send in claims in various formats, and also have to keep up with regular change in regulations by training staff and updating processes.

Claims processing by insurance companies requires processing of large volumes of handwritten documents sent by fax, mail, or otherwise scanned. These documents can be processed much faster and with more accuracy by Intelligent Document Processing Tools. This leverages handwriting recognition (image processing) and information retrieval- two techniques grounded in AI. Besides the documents, supporting images, audio and video contents needs to be processed. Image processing, audio processing, video processing, NLP and DL can also automatically extract information from audio, images and video claims data and augment to the text based information. With document automation, insurers can automatically extract data from documents, identify fraudulent claims and validate claims that are in line with policies. This, in turn, leads to better customer experience.

While automation of document processing is essential, AI can also do more complicated tasks where decision making is critical. ML allows AI software to study behavioral analytics and customer related data to make more accurate decisions on whether a claim is genuine, which can apply ever more nuanced types of claims.

Tractable uses Deep Learning to analyze automobile accident images and estimate repair costs in real-time. In this way, we can settle claims and respond to accidents up to ten times faster.

Measuring risk is complex

Risk Assessment and Standardized Underwriting

Risk management is crucial for the insurance industry. Therefore, insurers must consider every available quantifiable factor to develop profiles of high and low insurance risk during underwriting. Level of risk decides insurance premiums with those at higher risk charged a higher rate.

Risk assessment performed by companies to evaluate a customer for life insurance can take up to a month based on the person’s specific circumstance. The process is susceptible to the subjective nature of risk analysts and different people may give the different evaluations for a single customer.

By analyzing the information on existing customers like demographic, financial, psychographic, geographic information, along with properties of insured objects, companies can build risk assessment models that evaluate new customers faster and more accurately.

This problem of handling and using large amounts of data accurately within limited time gives rise to the need of using ML to automate the process. These ML methods can analyze or determine the risk level of an insurance policy.

Allstate Insurance uses dedicated aircraft or drones to record images used to write policies for customers faster, or to look at damage more quickly after a catastrophe. Its customers with auto insurance can send in photos from an accident scene to be analyzed by AI models and expedite claims. Orbital Insight and Flyreel are two more companies using AI and image data to assess insurance risk faster and more accurately.

Fraud is often hard to detect

Claim Fraud Detection

Insurance fraud has existed as long as the Insurance industry itself. Different insurance are prone to different frauds. In most cases, what is most common is intentional damage to the insured items and false claims to insurance money. The FBI estimates that the total cost of insurance fraud (excluding health insurance) is greater than $40 billion per year.

Detecting insurance fraud is hard and requires thorough investigation. Traditionally, an investigator is appointed to assess whether or not a claim is genuine. This process, while being time-consuming, also requires (costly) skilled labor.

Using ML to evaluate insurance frauds significantly shortens the investigative time period and also detects fraud more accurately, more often than a human agent. It can uncover hidden connections between different factors that may be imperceptible to human beings.

Computers learn to detect fraud differently than other use cases. We train the algorithm to detect anomalies by feeding large amounts of normal instances (genuine claims) and some outliers (fraudulent claims). The machine forms an internal model of what typical claims look like and can detect anomalies. This method, besides being fully automated, is also able to predict frauds that have never happened before - something very difficult for a human to do.

DeepFakes, synthetic content generated by combining several pristine contents and fake content using DL, pose challenges to detect fraudulent claims. To combat the rise of DeepFakes, we can use AI defensively to detect AI-created DeepFake content to reduce/eliminate fraudulent claims.

FRISS Fraud Detection at Claims is a company that enables claim segmentation, improves the customer experience with faster payment of legitimate claims, and automatically detects insurance fraud before we pay claims. The company’s AI-driven fraud detection software automatically detects suspicious claims, reveals networks and hidden patterns while safely automating business processes.

Workers' Compensation is changing as a result of AI

Reinventing Workers’ Compensation with SaaS-based Solutions

With SaaS-based workers’ compensation insurance software, customers can more easily leverage both ML and AI to assess risk in real-time and ultimately make more accurate and efficient decisions. This enhanced understanding also allows insurers to provide customer-friendly product options, such as pay-as-you-go pricing.

With a better worker’s compensation insurance program comes the data, compliance, and medical reports that organizations require to take care of customers while still providing the flexibility needed for today’s operations. And, these solutions have the capabilities of AI and data to scale up as per the demand.

Chatbots are powering the next generation of real-time customer support

Better Customer Support with Chatbots

Conversational AI can respond more efficiently to customer queries than many call centers. According to a study, one third of customers say they will consider switching companies after a single case of poor customer service. The sheer mass of customer requests requires a customer service that can be scalable and can function 24/7, in multiple languages and various digital/social media channels, while guaranteeing personalized interactions.

Chatbots allow customers to manage their insurance claims quickly and efficiently while serving as an inbound channel for insurance companies that provide actionable insights to further understand customer behavior and preferences. This information allows insurance firms to deliver personalized services and suggest better quotes that adjust and tailor to each client’s needs. The more efficient customer service is, the fewer chances there are for human errors and the bigger the savings in operational costs.

According to Study, the use of conversational AI chatbots for insurance will lead to cost savings of almost $1.3 billion by 2023, across property, motor, life and health insurance, from $300 million in 2019.

AI helps you personalize experiences at scale

Personalized Services and Enriched Customer Experience

AI helps insurers provide a more personalized experience to their customers by offering tailored policies to customers based on their requirements. AI can even recommend coverage levels based on previous customer interactions or buying behaviors of customers who fit similar profiles - a process called collaborative filtering. The burden of identifying the optimal product is no longer solely on an insurance agent, and is instead heavily supported by massive amounts of data that can only be processed using AI. ReFocus AI is a company that provides AI sales enablement to increase sales at least by 10% by using sales data from the CRM and proprietary collaborative filtering algorithms. By using additional external data, the potential of sustainable growth is huge with ReFocus AI.

Insurers can engage with their customers more and prevent attrition based on customer data like demographics, social media information, purchase history and environmental factors to identify customers in need of attention. Then redirect re-engagement campaigns towards these customers to prevent churn.

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