Tag Archives: timm

Big Data Can Solve Discrimination

Big data has the opportunity to end discrimination.

Everyone creates data. Whether it is your bank account information, credit card transactions or cell phone usage, data exists about anyone who is participating in society and the economy.

At Root, we use data for car insurance, an industry where rating variables such as education level or occupation are used directly to price the product. For a product that is legally mandated in 50 states, the consumer’s options are limited: give up driving and likely your ability to earn a living or pay a price based on factors out of your control.

Removing unfair factors such as education and occupation from pricing leaves room for variables within an individual’s control — namely: driving habits. In this way, data can level the playing field for all consumers and provide an affordable option for good drivers whom other companies are painting with a broad brush. In the lon term, everyone wins as roads become safer and driving becomes prohibitively expensive for irresponsible drivers.

This is just one example where understanding the consumer’s individual situation deeply allows for more precise — and more rational — decision making.

But we know that the opportunity of big data goes beyond the individual. For example, the unfair practice of naively blanketing entire countries, religions or races unfairly as “dangerous” is a major topic in the news. What happens if you apply the lens of big data to this policy?

See also: Industry’s Biggest Data Blind Spot

Causal Paths vs. Assumption-Based Decisions

With the increased availability of data, we are able to better understand the causal paths between data generation and an event. The more direct the causal path, the better predictions of future events (based on data) will perform.

Imagine having something as trivial as GPS location data from a smartphone on a suspected terrorist. Variables such as having frequent cell phone conversations with known terrorists or being located within five miles of the last 10 known terrorist attacks will allow us to move away from crude, unjust and discriminatory practices and toward a more just and rational future.

Ahmad Khan Rahami, who placed bombs in New York and New Jersey, was flagged in the FBI’s Guardian system two years earlier. The agency found there weren’t grounds to pursue an investigation — a failure that may have been averted if the FBI had better data capture and analysis capabilities. Rahami purchased bomb-making materials on eBay and had linked to terrorist-related videos online before his attempted attack. Dylann Roof’s activities showed similar patterns in the months leading up to his attack on the Emanuel AME Church in Charleston, SC.

The causal path between a hate-crime or terrorist attack and the actions of Dylann Roof and Ahmad Khan Rahami is much more direct than factors such as religion, race or skin color. Yet we naturally gravitate toward making blanket assumptions, particularly if we don’t understand how data provides a better, more just approach.

Today, this problem is more acute than ever. Discrimination is rampant — and the Trump administration’s ban on travel is unacceptable and unnecessary in the era of big data. For those unmoved by the moral argument, you should also know policies like the ban are hopelessly outdated. If we don’t begin to use data to make informed, intelligent decisions, we will not only continue to see backlash from discriminatory policies, but our decision making will be systematically compromised.

The Privacy Red Herring

Of course, if data falls into the wrong hands, harm could be done. However, modern techniques for analyzing and protecting data mitigate most of this risk. In our terrorism example, there is no need for a human to ever view GPS data. Instead, this data is collected, passed to a database and assessed using a machine learning algorithm. The output of the algorithm would then direct an individual’s screening process, all without the interference of a human. In this manner, we remove biased decision making from the process and the need for a “spy” to review the data.

See also: Why Data Analytics Are Like Interest  

This definitely provides a challenge for the U.S. intelligence community, but it is an imperative one to meet. If used responsibly, analytics can provide insights based on controllable and causal variables. The privacy risk is no longer a valid excuse to delay the implementation of technologies that can solve these problems in a manner that is consistent with our values.

This world can be made a much better and safer place through data. And we don’t have to sacrifice our privacy; we can have a fair world, a safe world and a world that preserves individual liberties. Let’s not make the mistake of believing we are stuck with an outdated and unjust choice.

3 Forces Disrupting Personal Lines

Five years ago, insurance-focused technology conferences were attended mostly by insurance carriers and large consulting firms. Now, I’m amazed and encouraged at both the size of the audiences and the diversity of the audiences – a melting pot of venture firms and eager entrepreneurs, as well as all the traditional industry folk. “Insurtech” is starting to get some serious attention, and for good reason.

There are new funding announcements every couple weeks, new conferences popping up left and right and corporate venture funds now at almost all major carriers. The funding in this space alone has risen from $740 million in 2014 to $2.65 billion in 2015, and as a category insurance tech has seen 50% more deal activity in 2016 year to date than in all of 2015 combined.

As Peter Thiel has said, “Humans are distinguished from other species by our ability to work miracles. We call these miracles technology.” We’ve seen technology revolutionize other industries, and now it’s our time.

Our industry has been here before, and, every time, new companies have emerged while incumbents have suffered. Technology has cycled through the industry many times, each time weeding out the latent and rewarding the agile.

What’s different this time is the pace of change – winners will become losers faster than we’ve ever seen. This time, three forces will significantly affect the personal lines insurance industry: shifts in consumer purchasing behavior, the proliferation of data and the interplay between data and consumers.

The mobile-first era

Technology makes life easier for consumers, and we’ve seen a shift in behaviors because of it (or is it the other way around?). Regardless, as a result of this shift, mobile is the fastest-growing retail channel, and “one click” ordering has become the standard.

See also: Blockchain Technology and Insurance  

Unfortunately, as an industry, we are far behind. The industry standard still touts a 15-minute purchasing experience as a win – on a computer. Despite the inherent value of convenience, the mobile experience is far more tenuous for consumers than other distribution channels across all major competitors. Consumers are asked to enter in form field after form field designed for a desktop, but on a mobile device and with only two thumbs. The result is a digital experience that ranks worse than government services.

We’ve seen this trend before. The internet had a very similar effect on our industry in the late ’90s and early 2000s and continues today. With the exception of Progressive, Geico and USAA, most large carriers still struggle to understand how to compete in an internet-first world. These three companies successfully cornered the market by embracing the internet while the rest of the industry doubled down on the spiraling agent-model.

It’s clear that we’re trending toward the same pattern with mobile. Today, it’s a relatively level playing field. Those who support a mobile-first experience will win big. Those who are late to the game won’t ever catch up.

Open the data floodgate

The rise of mobile means access to new data, and new data is paramount for our industry. A fundamental value that insurance companies provide to the economy is the ability to price and understand risk. Data is essential to this understanding.

As Seth Lloyd of MIT says, humans used to be hunters and gatherers of data. With technology, data is now flying by us every second, and the real challenge is successfully understanding how to capture, sort and use this data.

Smart mathematicians and engineers have already figured this out to a large extent, creating supercomputers able to do machine learning mathematics on large quantities of data, producing insights never before seen. However, despite the accessibility of these improved techniques, most actuarial modeling is still based in classical statistics and generalized linear modeling.

The interactions: data + consumer

These two trends — the customer move to mobile first, and the proliferation of data — are difficult to manage alone. When combined, the interaction becomes disproportionately challenging. This has created an environment where the industry has largely pegged new data collection against consumer experience, rather than executing on both simultaneously.

For example, telematics through OBD II programs have been major efforts of the industry. The reality is these devices are confusing for consumers, the value proposition to them is meager and the process of receiving the device, plugging in the device and returning the device is starkly arduous in contrast to modern consumer purchasing experience expectations.

Smartphones can now do everything an OBD II device can, and, with connected cars, these OBD II devices are completely obsolete. The question is whether carriers will continue to throw good money after bad, or realize the sunk cost of OBD II programs and begin investing in new technologies.

See also: Insurtech: One More Sign of Renaissance  

And it’s not that the industry isn’t spending money on IT – it is. Armies of engineers working on old technologies are provided with hundreds of millions of dollars to attempt to overhaul policy management systems. Billion dollar companies specializing in just fixing policy management systems exist, capitalizing on the inability and incompetence of the industry.

The dawning of insurance tech

The industry has for too long mistreated technology, looking at it as a cost of doing business, rather than an investment in consumer experience and better data management. It is rare, if ever, that you see a seasoned engineer in the C-suite at an insurance company or even on the board. Talented engineers run from the industry. Can you blame them? The agile engineer, eager, stumbles into a lagging and latent system. It’s almost the start of a bad joke.

The result is that all of these implications and their interactions with one another have cost consumers dearly. The purchasing experience is confusing and onerous. The price is unfair, based off the same data as 20 years ago and off out-of-date statistical modeling. Consumers are paying for an inefficient system.

This is clear to the venture community, and clear to many entrepreneurs. The industry has been protected by regulation, capital and complexity. These barriers may have slowed the pace, but, increasingly, we are seeing startups that are not partnering with existing carriers, but becoming carriers. This is the beginning of a new end for car insurance. Technology will continue to create miracles, and these miracles will belong to the consumer.