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Analytics Magazine

Behavioral Economics: Bridging the information gap

September/October 2013

The information asymmetry problem: How decision science can help reduce market inefficiency.

information asymmetry problem: How decision science can help reduce market inefficiency

Krishna Rupanagunta, Ajay Parasuraman and Sourav BanerjeeBy Krishna Rupanagunta, Ajay Parasuraman and Sourav Banerjee

That information is integral to any economic transaction has always been generally accepted; however, the degree to which information influences the outcome of a transaction, especially when it is between human beings (as opposed to rational economic agents), is a relatively more recent discovery.

Classical economics is based on the perfectly competitive general equilibrium model in which every party involved in a transaction has access to exactly the same information. This model also assumes that any new information is rapidly disseminated to all the parties on the demand and supply sides, which takes the market back to equilibrium. This traditional model was based on the concept of a fundamentally rational decision-maker, which was evidently not true. This paradigm was challenged first by the neo-classical economists, who attempted to integrate psychology into economics; however, they continued to assume that people were focused on maximizing utility. In the 1960s, a whole new breed of economists challenged this paradigm by drawing upon how people irrationally seek satisfaction, rather than maximizing utility. And behavioral economics, which integrated cognitive psychology with economics, was born. One of the more interesting areas of study in behavioral economics in recent times has been how unequal access to information influences the outcomes of transactions.

Information Asymmetry: What is it?

One commonly accepted definition of information asymmetry is: “A situation in which one party in a transaction has more or superior information compared to another. This often happens in transactions where the seller knows more than the buyer, although the reverse can happen as well. Potentially, this could be a harmful situation because one party can take advantage of the other party’s lack of knowledge” [1]. Information asymmetry can prevent consumers in a market from taking fully informed decisions, which in turn can result in market inefficiencies, or in the worst case, in market failure.

A recent example of how information asymmetry could end up creating a Black Swan event with disastrous consequences was the 2008 subprime crisis. As banks started relaxing the credit requirements in their quest to get a larger slice of the mortgage market, sub-prime borrowers gamed the system by hiding information that otherwise would have disqualified them. This quickly turned into a negative spiral as banks/housing finance companies tried to out-do each other in chasing these high-risk borrowers. The Adverse Selection cycle set in quickly as riskier borrowers piled into the system. This giant Ponzi scheme finally blew up spectacularly in 2008 – information asymmetry was at the heart of this crisis.

This is clearly an extreme case but serves as a powerful illustration of how important information is to keep the wheels of modern capitalism in motion. Government regulators as well as private organizations recognize this and are constantly making efforts to minimize inefficiencies caused by information asymmetry in the marketplace. Governments mandate pharmaceutical companies to publish the risks and side effects associated with drugs. Likewise, private companies take it upon themselves to better inform buyers by providing as much information as possible – e-commerce sites providing information to buyers through product reviews and ratings are examples.

Even in a world where technological enhancements have dramatically reduced barriers to information availability, information asymmetries continue to exist in various forms in almost every business transaction. In a broad sense, asymmetric information is manifested in two major categories: adverse selection and moral hazard.

Adverse selection: Asymmetric information before a transaction, which leads to the less informed party selecting “bad” products or services. Insurance companies are exposed to the risk of adverse selection since it is very difficult to assess the true risk of every customer. This constraint often forces them to offer products at prices that could end up attracting high-risk customers and driving away lower-risk ones. Banks also face similar risks; they may end up having a significant proportion of high-risk customers in their loan books.

Moral hazard: A situation resulting from asymmetric information where the more informed party misuses private information for unethical behavior. For instance, in e-commerce transactions, buyers and sellers are physically removed from each other. Dishonest sellers may not divulge details about the product being sold, and the buyer may be sold an inferior product lacking in quality. On the other hand, in online marketplaces, which are buyer biased and where the onus of proof is on the seller, the buyer may make false claims of receiving a damaged item with the intention of manipulating the seller to lower prices.

Both these situations present a challenge to businesses, which constantly struggle to mitigate risks and potential losses arising from information asymmetry. Businesses tackle these problems using a combination of heuristic and fact-based decision-making. For example, most insurers now have sophisticated risk evaluation frameworks that help them alleviate the risks of adverse selection. E-commerce sites are constantly innovating and upgrading their infrastructure to provide more and more correct information to both buyers and sellers on the website. This is where analytics and behavioral sciences can come together as decision science to solve this problem.

Enter Decision Science

With the advent of newer technologies and the ability to store and process vast amounts of data, decision science is a fast emerging discipline that offers solutions leveraging analytical techniques and combines them with concepts from behavioral psychology. For instance, adverse selection is a particularly thorny problem in the world of insurance. The Progressive Insurance Company discovered a correlation between financial responsibility (or lack of it) and reckless driving. This nugget helped them better tailor their insurance products.

The information explosion (data from social media, clickstream and telematics being a few examples) combined with advances in technologies (big data, high performance computing) offers companies the ability to solve some of these problems.

In most situations in the insurance industry, information asymmetry exists in the form of adverse selection. Though most analysts faithfully adopt the use of credit scores to assess the risk of potential customers, a certain limit on accuracy still persists due to information gaps such as a lack of complete knowledge surrounding the characteristic behavior of an insurance buyer. Some of the more savvy insurance firms are trying to bridge this information gap by triangulating with other data sources to extract better behavioral signals about their customers. For instance, auto insurers are collaborating with auto manufacturers to capture telemetry data captured in cars that reflect driving behavior and use that to refine the risk profiles.

Data captured from a user’s activities online has become a goldmine of information for understanding customer behavior that normally cannot be figured out from merely the transactional metrics and demographics. As President Obama’s election campaign team demonstrated in the 2012 elections, micro-targeting and real-time monitoring helps in keeping track of behavioral changes in the voter. Insurance companies can apply similar techniques to fine-grain the risk profile of every customer, tailor a solution that best fits the customer and even continuously update the risk profile based on actions taken by customers to develop tailor-made insurance solutions; and in the process reduce the information gap that is currently resulting in a higher risk exposure.

The moral hazard problem has probably been around for as long as the market economy has existed. The online marketplace is turning out to be a test-bed where the moral hazard problem can be minimized by the use of decision science.

In normal buyer–seller transactions, it is extremely difficult to identify manipulative buyers who make false claims of delayed shipping or broken items and cause dissatisfaction to honest sellers. This moral hazard problem can end up driving honest sellers from the marketplace, clearly not the best solution.

The online world offers a chance to eliminate this inefficiency simply by storing historical transactions and leveraging them to draw patterns. For instance, looking at historical transactions and triangulating this with other sources (e.g., customer service transcripts), it is possible to predict the likelihood of a fraudulent buyer behavior and proactively alert the seller. Moreover, a buyer level-rating can be devised and used to better inform the sellers. One leading technology platform has implemented a text mining solution to sift through chat transcripts for early-warning signals of bad buyer behavior. This information is then used to proactively warn the sellers, alerting them to the potential for morally hazardous behavior from the buyers.

Separating Out the Signal from the Noise

Information asymmetry has contributed to creating market inefficiencies in a wide variety of industries, and companies have been trying to find ways to bridge this gap for a long time. The recent advances in decision science have created opportunities for companies to narrow this and inch toward a more perfect marketplace that minimizes the potential to create rent-seeking behavior in economic transactions. The availability of data is now making it possible to solve some of the adverse selection and moral hazard problems.

This comes with a caveat: where there is data, there is also too much of it. And sifting the signal from the noise is another challenge altogether. Winning companies will be the ones who manage to extract the signals and use them to remove inefficiencies caused by information asymmetry.

Krishna Rupanagunta is responsible for client delivery, people development and providing thought leadership across projects at Mu Sigma. With more than 14 years of experience, Rupanagunta has a strong background in business consulting, servicing Fortune 500 clients across multiple industries with specific focus on supply chain optimization. Prior to joining Mu Sigma, he was part of a non-profit that helped the Indian government in the conceptualization and design of the largest citizen identity project ever attempted in the world. He has a master’s degree from IIM – Calcutta.

Ajay Parasuraman is a senior business analyst at Mu Sigma and is based in Bangalore, India. He has several years of experience in data analysis across industry verticals.

Sourav Banerjee is a senior manager at Mu Sigma with vast experience in analytics consulting with multiple Fortune 500 clients. His experience spans across multiple industries – insurance, technology, telecom, retail and healthcare across multiple geographies.



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