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How Behavioral Psychology is Fixing Modern Insurance Claims

Nivin Simon
3 minutes, 56 second read

Human Behavior is inherently hard to predict and mostly irrational. Infact, this irrationality is often overlooked because it offers no meaningful insight or patterns behind our motivations. 

In the early 70’s, Israeli-American economist Daniel Kahneman challenged the assumption that humans behave rationally when making financial choices. His research explored the fundamentals of how people handle risk and display bias in economic decision-making. He would later be awarded the Nobel Prize for his pioneering work which provided the basis for an entirely new field of study called Behavioral Economics

Standard Economics assumes humans behave rationally, whereas Behavioral economics factors in human irrationality in the buying process.

Along with another scientific approach to studying natural human behaviors (Behavioral Science), both these fields became particularly useful to the financial industry early on. By understanding the deep seated motivations behind people’s choices, a specific interaction can be designed to influence an individual’s behavior — also known as behavioral intervention.

By finding meaningful patterns in Big Data, usually performed by a data scientist, businesses are able to leverage analytics and behavioral customer psychology. The outcomes of these insights can help business owners learn about the customer’s true feeling, explore behavioral pricing strategies, design new experiences and retain more loyal buyers. This is why Behavioral Scientists have become highly sought after over the last decade. 

The Rise of the Behavioral Scientist

Take for instance Dan Ariely, who is a Professor of Psychology & Behavioral Economics at Duke University, and also serves as the Chief Behavioral Officer of Lemonade — the World’s biggest Insurtech. Ariely observes that human behavior is ‘predictably irrational’ and constantly exhibits ‘self-defeating’ characteristics. There is a lot of value in studying these behaviors, for many organizations, to encourage positive ones, dissuade dishonesty and improve the underlying relationship.

The ‘dissuading dishonesty’ part is particularly useful for Insurance carriers. For a business that fundamentally deals with both people and risk, Insurance is endlessly plagued by fraud. Insurance fraud losses were estimated around $80B in 2019 alone. On the other hand, legitimate claim instances can at times be overlooked due to the lack of evidence or nuances in the finer policy details. 

To combat fraud during the claims process, Ariely added a simple ‘honesty pledge’ agreement before the beginning of the claims intimation process. A customer signs the digital pledge, and is then asked to record a short video explaining the incident for which they are requesting the claim.

The process seems naive but it’s backed by tons of data and science — a byproduct of decades of research work put into psychology and behavioral economics. 

So, How are claims being driven by data science?
How do insurers capture honesty from their customers?
The answer is priming.

By enforcing an honesty pledge, Lemonade was able to bring down the likelihood of fraudulent claims being intimated for. In other words, they made it harder for customers to lie. The hypothesis that works is: Don’t blame people for mistakes in decision making, it’s on the designers of the system

After the customer got done with their video recording, Lemonade ran 18 anti-fraud algorithms against the claim to check its veracity and a payment was made in a few seconds. 

Behavioral Priming in Insurance

Behavioral work is built on strong academic research that identifies aspects that influence the  buying process. ‘Nudges’ are a perfect example of behavioral priming at work. Nudge theory (a concept within Behavioral Science) identifies positive reinforcement techniques as ways to influence a person’s behavior and ultimately their decision-making.

For example, according to a study published in the Journal of Marketing Research, research subjects who were shown an aged image of their faces allocated twice the amount to their retirement savings when compared to people who were shown images of their current younger selves.

In this case, the ‘nudging’ technique was effective in driving retirement planning behavior among the test group. 


Source: Centre for Financial Inclusion

Behavioral Economics also stipulates that once you start doing something, you are more likely to continue doing so. This is how Netflix uses subtle nudges on their platform, where after each episode a prompt asks if you would like to continue watching the show.

Deriving New Value

Swiss Re’s Behavioural Research Unit outlines five promising areas where behavioral economics can create new value for insurers.

Digital businesses are gradually realizing the limitations of human and machine systems without any real intelligence or computing power behind it. Between human prone errors and the scalability challenges of traditional technologies, a new mechanism is required to learn and adapt better. 

Behavioral Science interventions in insurance can help carriers align their strategies with the true needs of their customers. Using the insights posited from advanced machine learning models, the right behavioral intervention can bring about changes to real-world insurance demand behavior that closely matches the benchmark model.

Also read – how InsurTech beyond 2020 will be different?

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Across the Insurance ecosystem, a special fraction within the industry is noteworthy for its adoption of new technologies ahead of others. However slow but sure, uberization of insurance has conventionally demonstrated a greater inclination towards digitization. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers. 

92% of the C-Level Executives are increasing their pace of investment in big data and AI.

NewVantage Partners Executive Survey 2019 

Artificial Intelligence has brought about revolutionary benefits in the Insurance industry.

AI enriched solutions can remove the ceiling caps on collaboration, removes manual dependencies and report errors.

However, organizations today are facing a lot of challenges in reaping the actual benefits of AI.

5 Challenges for AI implementation for Insurers

5 AI Implementation Challenges in Insurance

Lack of Quality training data

AI can improve productivity and help in decision making through training datasets. According to the survey of the Dataconomy, nearly 81% of 225 data scientists found the process of AI training more difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.

Clean vision, Process, and Support from Executive Leadership

AI is not a one time process. Maximum benefits can be reaped out of AI through clear vision, dedicated time, patience and guided leadership from industry experts and AI thought leaders.

Data in-silos

Organizational silos are ill-advised and are proven constrictive barriers to operational productivity & efficiency. Most businesses that have data kept in silos face challenges in collaboration, execution, and measurement of their bigger picture goals. 

Technology & Vendor selection

AI has grown sharp enough to penetrate through the organizations. As AI success stories are becoming numerous investment in AI is also getting higher. However big the hype is, does AI implementation suits your business process or not – is the biggest question. The insurtech industries have continued its growth trajectory in 2019; reaching a funding of $6B. With the help of these insurtech service firms, Insurance organizations have made progress, tackling the age-old insurance ills with AI-powered innovations.

People, Expertise and Technical competency

‘Skills and talent’ in the field of AI is the main barrier for AI transformation in their business.

Still playing catch-up to the US, China, and Japan — India has doubled its AI  workforce over the past few years to nearly 72,000 skilled professionals in 2019. 

Are you facing challenges with your Insurance process but have no idea where the disconnect is? Is your Insurance business process ripe for AI in the year 2020?

What is the right approach?

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders on the 13th of February, 2020.

Register for the live webinar by Parag Sharma (AI Thought Leader & CEO Mantra Labs). 

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Ratemaking, or insurance pricing, is the process of fixing the rates or premiums that insurers charge for their policies. In insurance parlance, a unit of insurance represents a certain monetary value of coverage. Insurance companies usually base these on risk factors such as gender, age, etc. The Rate is simply the price per ‘unit of insurance’ for each unit exposed to liability. 

Typically, a unit of insurance (both in life and non-life) is equal to $1,000 worth of liability coverage. By that token, for 200 units of insurance purchased the liability coverage is $200,000. This value is the insurance ‘premium’. (This example is only to demonstrate the logic behind units of exposure, and is not an exact method for calculating premium value)

The cost of providing insurance coverage is actually unknown, which is why insurance rates are based on the predictions of future risk.  

Actuaries work wherever risk is present

Actuarial skills help measure the probability and risk of future events by understanding the past. They accomplish this by using probability theory, statistical analysis, and financial mathematics to predict future financial scenarios. 

Insurers rely on them, among other reasons, to determine the ‘gross premium’ value to collect from the customer that includes the premium amount (described earlier), a charge for covering losses and expenses (a fixture of any business) and a small margin of profit (to stay competitive). But insurers are also subject to regulations that limit how much they can actually charge customers. Being highly skilled in maths and statistics the actuary’s role is to determine the lowest possible premium that satisfies both the business and regulatory objectives.

Risk-Uncertainty Continuum

Source: Sam Gutterman, IAA Risk Book

Actuaries are essentially experts at managing risk, and owing to the fact that there are fewer actuaries in the World than most other professions — they are highly in demand. They lend their expertise to insurance, reinsurance, actuarial consultancies, investment, banking, regulatory bodies, rating agencies and government agencies. They are often attributed to the middle office, although it is not uncommon to find active roles in both the ‘front and middle’ office. 

Recently, they have also found greater roles in fast growing Internet startups and Big-Tech companies that are entering the insurance space. Take Gus Fuldner for instance, head of insurance at Uber and a highly sought after risk expert, who has a four-member actuarial team that is helping the company address new risks that are shaping their digital agenda. In fact, Uber believes in using actuaries with data science and predictive modelling skills to identify solutions for location tracking, driver monitoring, safety features, price determination, selfie-test for drivers to discourage account sharing, etc., among others.

Also read – Are Predictive Journeys moving beyond the hype?

Within the General Actuarial practice of Insurance there are 3 main disciplines — Pricing, Reserving and Capital. Pricing is prospective in nature, and it requires using statistical modelling to predict certain outcomes such as how much claims the insurer will have to pay. Reserving is perhaps more retrospective in nature, and involves applying statistical techniques for identifying how much money should be set aside for certain liabilities like claims. Capital actuaries, on the other hand, assess the valuation, solvency and future capital requirements of the insurance business.

New Product Development in Insurance

Insurance companies often respond to a growing market need or a potential technological disruptor when deciding new products/ tweaking old ones. They may be trying to address a certain business problem or planning new revenue streams for the organization. Typically, new products are built with the customer in mind. The more ‘benefit-rich’ it is, the easier it is to push on to the customer.

Normally, a group of business owners will first identify a broader business objective, let’s say — providing fire insurance protection for sub-urban, residential homeowners in North California. This may be a class of products that the insurer wants to open. In order to create this new product, they may want to study the market more carefully to understand what the risks involved are; if the product is beneficial to the target demographic, is profitable to the insurer, what is the expected value of claims, what insurance premium to collect, etc.

There are many forces external to the insurance company — economic trends, the agendas of independent agents, the activities of competitors, and the expectations and price sensitivity of the insurance market — which directly affect the premium volume and profitability of the product.

Dynamic Factors Influencing New Product Development in Insurance

Source: Deloitte Insights

To determine insurance rate levels and equitable rating plans, ratemaking becomes essential. Statistical & forecasting models are created to analyze historical premiums, claims, demographic changes, property valuations, zonal structuring, and regulatory forces. Generalized linear models, clustering, classification, and regression trees are some examples of modeling techniques used to study high volumes of past data. 

Based on these models, an actuary can predict loss ratios on a sample population that represents the insurer’s target audience. With this information, cash flows can be projected on the product. The insurance rate can also be calculated that will cover all future loss costs, contingency loads, and profits required to sustain an insurance product. Ultimately, the actuary will try to build a high level of confidence in the likelihood of a loss occurring. 

This blog is a two-part series on new product development in insurance. In the next part, we will take a more focused view of the product development actuary’s role in creating new insurance products.

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