Tag Archives: Christopher Fernandes

Set the Machines Free to Learn

A decade ago, straight-through processing was a buzzword, and speed to market was critical. The progress that financial institutions have made in making almost all aspects of their transaction footprint digital has left little to leverage on the transaction side.

Today, while most organizations are busy revamping their policy administration systems, which were long ready to be replaced a decade ago, the companies that will be set apart are those that start considering machine learning and artificial intelligence (AI) for their core systems.

If you look at the fundamentals of any kind of insurance, at the core, insurance offerings are about risk pooling and the ability of the insurer to price products so that over time the premium revenues outstrip the claims experience. Historically, all the analysis has been done by people, and rightly so, as we lived in a world that was not connected, and human intervention to analyze outside factors was critical.

See also: What You Must Know on Machine Learning  

Fast forward to current time: All the data is on some kind of digital medium and more often than not connected and accessible. What is missing is the machine learning and artificial intelligence integration into the different facets of the insurance life cycle and the software platforms that are used to manage and maintain the data. The amount of data that needs to be analyzed and the patterns that are needed to be determined are so humongous that relying on data analysis by a person alone may not be the best approach.

If you use Google often, you have noticed that now Google can predict what you are searching and what you are looking for based on data it collects on your location, your emails, your past transaction etc. Over time, there will be a cognitive angle to the search capabilities exhibited by Google. If you apply the same rule of thumb to underwriting, insurance pricing and risk aggregation, why would we not want to leverage machine learning in a similar manner?

For this to happen, we need to start building software systems with not just automation in mind but also a consideration of how system design can extend machine learning. If the dots are connected and the data patterns understood and logic applied, there are certain decision making aspects that can move away from people to machines and over time evolve to largely autonomous ecosystem.

What will differentiate the market leaders from the laggards is investment in this aspect. These changes will come in the next decade or maybe even sooner, and the underwriting and actuarial aspects will lean toward machine learning and AI-assisted functions. The next wave would lead to a totally autonomous ecosystem.

The picture simplistically highlights the possibilities of embedding machine learning in the software ecosystem that we see in today’s insurance landscape. This is a generalized view, agnostic of the domain or line of business. Insurance carriers would need to start thinking out of the box to translate this into software platforms of the future, pushing current roles into those that co-exist or radically change them.

Before we set the drones to fly and change the commercial insurance ecosystem, machine learning and AI need to be adopted into mainstream core software platforms. The emerging market in the foreseeable future will be opened to the players that will NOT be consumed with dev-ops and pushing the realms of delivery automation but by those firms investing in infusing machine learning and artificial intelligence into core platforms enabling underwriting and actuarial functions to be supplemented by machines.

See also: How Machine Learning Changes the Game  

Insurance has traditionally followed and adopted what has been tried and tested in the banking space. For a change, there may be an opportunity for insurance carriers to take the lead and beat the banks and other financial institutions to set free the machines and change the way products are conceived and priced and premiums calculated.

Connected Humans, Version 3.0

Whether you commute to work on public transport to work or fly between busy airports to serve your clients, wherever you go you will see people glued to their phones, tablets or e-readers. More than likely, all these devices are connected to the Internet in real time over a mobile network or capable of connecting via Wi-Fi.

There is so much written on the connected car and the connected (“smart”) home, but we also need to open a discussion about connected humans.

Let me clarify: I have no interest in talking about social networking. I’m more interested in connections from the perspective of tracking health and biometric data to be used by the healthcare and insurance industries for pricing.

A decade ago, we were limited by the technology and the computing power of hand-held devices. Wearables and ingestible devices were nowhere in the ecosystem. It made perfect sense to use historical data to price and sell products based on stale census information.

Technology drivers

Fast forward to the current time. Computing power has scaled exponentially over the last decade. We have devices that can track, store and filter essential lifestyle and health data, and we have predictive analytic capabilities that would make historic rating methods look like the Stone Age.

Market demographics

The growth rate of Millennials earning paychecks is not keeping pace with the growth in the aging population living off savings. If that was not bad enough , buying behaviors of Millennials indicate that insurance is not one of their top priorities. There are numerous surveys you can find online that point to this problem.

We have heard of “gamification” and customer engagement in the context of banking and financial services, to attract Millennials, but insurance and healthcare companies have barely touched the tip of the iceberg on this. The amount of biometric data that can be harvested and used for predictive analytics could include a host of items, including blood pressure, heart rate, vitamin count, sleep patterns, activity metrics and blood sugar, just to name a few. All this information, harvested and analyzed to price and sell a host of new products to new market segments with lifestyle diseases like diabetes or obesity, opens the route to gamification of healthcare apps and much better life insurance pricing. Providers today stop at just providing discounts on the fringes as I see it, not truly revisiting pricing.

With technology evolving at the pace it is and with our ability to get more out of the data through predictive analysis, the healthcare and insurance segment could look very different 10 years from now.

There is a school of thought that says privacy issues will limit the use of biometric data, but, if there is a business model that works for weight watchers and diabetic forums, there is a business case and a market segment to change the way insurance and healthcare products are priced and sold.

Hertz has begun to pitch itself as a used-car sales channel, allowing the consumer to test drive a car for an extended renting period and then buy or not buy the car. In the insurance or healthcare context, if pricing were driven by behavioral patterns and biometric statistics, you could offer an extended free look or evaluation period allowing a skeptical diabetic or obese customer to try devices, see the effects on their health and the corresponding premium discounts and then make a decision on locking into the product.

Insurance and healthcare have not truly embraced the technology and buying behavioral shift of customers. What remains to be seen is who leads the charge. Will it be insurance and healthcare companies? Will it be technology giants like Google, which are already tracking a lot of what people do? Or will it be a company like Tesla and Uber, which have disrupted traditional industry segments where they were never the incumbent.