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How Behavioral Psychology is Fixing Modern Insurance Claims

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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Open Finance: Reality or Hype?

3 minutes read

Open Banking has reshaped the fintech industry. Customers want a seamless experience with more convenient and flexible access to services. Technological innovation and digital transformation have led to the emergence of neobanks which offer a banking experience similar to delivery apps. Now the customers can avail of services like opening an account in minutes. In the last few years, another new concept- Open Finance has joined the queue. What exactly is open finance? Is it just hype or reality? And how open finance might improve customer experience (CX). These are some of the questions that we’re going to talk about in this blog. 

Open Banking

In open banking, banks and other financial institutions allow third-party financial service providers to access the bank’s customers’ data via APIs (application programming interfaces). This helps banks to create more personalized offerings and meet the changing needs of their customers.

What is Open Finance?

Open Banking and Open Finance are similar. However, Open Finance is slightly more advanced in the process. Simply put, it is the next step in open banking. 

Open Finance is a more customer-centric approach. It gives users a safe and dependable way to share their data with the financial tools and apps they prefer to use.

How is Open Finance different from Open Banking?

How is Open Finance different from Open Banking?

Source: Accenture

Open Banking has certain limitations when it comes to sharing of financial data. Here, only that data can be shared which is related to financial operations made within the bank’s app or in a branch office. Open finance goes beyond this limitation.

In Open Finance, non-banking financial data including mortgages, savings, pensions, insurance, and consumer credit – basically your entire financial footprint – could be opened up to trusted third-party APIs if you agree.

Open finance will help open new gateways for financial institutions to improve CX. Let’s dig deeper to understand how this concept will change CX in the Fintech world for the next-Gen customers. 

  1. 360-degree Customer Insights: Data acts as a tool to study deeply about your customers. Organizations can analyze the customer data and extract some valuable insights to design the complete customer journey. Open Finance opens a more secure pathway for financial institutions and gives a more complete picture of their customer’s finances. 
  2. Partnerships & Collaborations: With open finance, comes an opportunity for the financial institutions to network and collaborate with various providers. This means they could deliver a wider variety of services based on consumer data, uncovering new business models and innovations.
  3. Transparency for the Lenders: Lenders can evaluate and measure the creditworthiness of potential borrowers, audit documents, and offer customized solutions by securely collecting customer data. Machine learning algorithms may help to extract valuable insights from raw data.

Open Finance offers freedom and flexibility to consumers giving more options and control over the data they share and how they engage with their finances. With just 8 seconds of attention span, the new age consumers want better experiences to get hooked to one brand. Open finance creates unparalleled access to a broader range of products and services. With data sharing, banking organizations can keep track on the changing customer expectations who want frictionless interactions and hyper-personalized experiences across all touchpoints of the customer journey.

The Road Ahead

Statista predicts that there will be 63.8 million open banking users globally by 2024, increasing at an average annual rate of about 50% between 2020 and 2024. This means there will be more demand for innovative products and services in the industry. Banking organizations would need to analyze the rising customer expectations more closely than ever. And for this, data would act as a key to designing the experience of tomorrow. 

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