Is this the right time for innovation, digitalization, and AI?

Disclaimer: The views represented in this article are the personal views of the author.

Let us first take a moment to thank all the medical professionals, healthcare workers, sanitation workers, police, fire personnel, pharmacists, and all other individuals on the frontlines who are tirelessly working to keep us all safe and to keep the essential services in an economy running during the COVID-19 pandemic. If you are one of those fortunate ones, who have been afforded the enviable luxury to stay at home, work from home, and continue to get a paycheck while doing so, then thank your good fortune and remember to pay it forward. There are millions of others who are unemployed, underemployed, have loved ones suffering, or worse yet, lost someone. Let’s offer a prayer and a moment of silence for the lives lost.

While on the one hand there is immense suffering, on the other hand most business leaders are working extremely hard to keep their startups, factories, companies, and organizations running so that when life returns to normal, there are jobs to go back to for all their employees and the economy can begin humming again. This is not being naive, this is being optimistic. Amidst all of these seemingly insurmountable challenges, innovations are happening around the world.

As noted in [1], the great plague in the 1300’s gave birth to the modern society with increasing wages and more people moving into the middle class. The pandemic of 1918 gave birth to advances in healthcare administration, creation of health departments, and investments in epidemiological studies. The more recent SARS epidemic paved the way for e-commerce to take hold in Asia. Similarly, the COVID-19 pandemic is already spurring innovations in all sectors of the economy [2-7]:

  • Medicine
    • modified ventilators
    • 3-D printing
    • vaccines
    • AI in medical decision support
    • Telemedicine
    • 5G-powered thermometers
    • smart bracelets to enable round-the-clock health stats monitoring
    • contact-tracing and associated data mining
  • Tech for remote working
  • Entertainment media
  • Augmented reality
  • Holographic telepresence
  • e-shopping
  • e-learning
  • Food delivery
  • Transportation and Logistics
  • Autonomous vehicles
  • Cybersecurity and AI-based threat detection
  • Digital KYC
  • Anti-money laundering and Anti-fraud tools

Wherever feasible, this is the time for organizations to double down on innovation, digitalization, and AI. If anything has become clear, it is that if you are not ready to become digital-first and be innovative, you may not survive another pandemic, worse still, not even this one. It is imperative that organizations and teams around the world realize the importance of digitalization and innovation. These are not buzzwords, sideshows, afterthoughts, website space-fillers, or crowd-pullers; they may very well save your teams and organizations. Don’t learn this the hard way.

This is a time for reflection and evaluation of your organization’s digital capabilities and innovation arsenal and to seek help wherever needed. If you are a service provider, now is the time to service your clients better and you will be better off for it post this crisis. Wherever feasible, do consider offering these services for free or at a reduced price during this crisis. Help your clients make better decisions using their existing data. Introduce them to how digitalization, data analytics, and AI can open new doors and create new business opportunities. Make a sincere effort to invest in your clients now and I am sure they will reward you with a lifetime of loyalty. Post the pandemic, when life is back to the new normal, the visionary leaders will have take already taken on digitalization and innovation projects and you don’t want to be left behind.

In every organization, the innovation engines must not stop, in fact they should spur on with twice the capacity so that they can take on the newly created challenges once lives are back to the new normal.

What could it mean for different industries?

In insurance companies this could mean more chatbots, more agents on video calls on i-pads, more automated claims reporting, more automated claims processing, customer onboarding, and superfast claim payments.

In banks, this could mean more video tellers, e-payments, no-touch loan processing, and other features.

In logistics companies, this could mean using more autonomous vehicles and drones.

In restaurants, more automated ordering and delivery.

For establishments like malls and restaurants, it could mean more robots assisting with sanitation tasks.

In hospitality and travel, it could mean more holographic telepresence.

In education, it could mean more e-learning.

All in all, let’s endeavor to come back stronger than when we went into this crisis.









[8] Free images from

The Impact of Telematics Data and AI on Motor Insurance

Co-authored with David R. Hardoon and cross-posted on

Disclaimer: The views represented in this article are the personal views of the authors

The insurance industry dates back to the 17th century BC when the Babylonians developed a system practiced by early Mediterranean sailing merchants. If a merchant received a loan to fund their shipment, they would pay the lender an additional sum in exchange for the lender’s guarantee to cancel the loan should the shipment be stolen, or lost at sea. Despite having come a long way over the last three millennia, and the more recent impacts to the industry from emergence of AI, the underlying fundamentals of insurance have not changed much since the Babylonians’ times. 

Today, the field of insurance is typically divided into the two main areas of Life and General Insurance. General Insurance encompasses areas such as Motor, Travel, Fire, Home, Critical Illness, Liability, Workers Compensation, and Health Insurance. In the U.S., General Insurance is also known as Property and Casualty Insurance whereas in Europe it is simply referred to as Non-Life Insurance.

One of the main drivers of change is the availability of new types of data for the calculation of risk. In motor policy risk underwriting, insurance companies have historically considered attributes such as age, driving experience, driving record of the applicant, characteristics of the motor vehicle, current and proposed mileage, storage of car (indoor / outdoor), anti-theft, anti-lock braking systems, among other factors for the estimation of risk. However, all of these attributes save for the first three, are more likely to help estimate the likelihood of the vehicle being involved in an accident rather than the estimation of the likelihood of the driver causing the accident; as these are attributes that do not represent how the driver performs on the road.

The proliferation of internet-of-things (IoT) based telematics devices, in the form of on-board diagnostic (OBD) devices or ML-driven mobile applications for telematics, have made it possible to capture the intricate details of actual driving behaviour (such as acceleration, deceleration, braking, bearing, speed, hard brakes, hard turns) on a real-time basis. These facilitate a more accurate estimation of a person’s risk while driving. Telematics, and the AI analysing this data, has ushered in the possibility of ultra-personalised driver risk scoring, which aims to present a more accurate view of a person. As a result, an 18 or an 80 year old driver may drive similarly and thus have comparable risks.

The collection of real-time behaviour data poses a number of considerations with regard to privacy and the insurance business model as a whole. In traditional motor underwriting, risk and premiums calculations are usually done on an annual basis. Given that the commonly used data attributes do not change frequently, an annual calculation is reasonable in capturing the fluctuations and changes to risk. However, if we are now able to collate both behavioural data as well as real-time driving data, risk can equally be calculated on a near-real-time basis. This allows a more precise measure as risk is increased or decreased. 

What would this mean to the business model? Should risk be calculated in such a manner, and should the pricing model be significantly more dynamic in nature? Our view is that it would depend on the context under consideration. For example, in the relatively smooth and organised traffic of North American, European, or major Asian cities such as Singapore, Seoul, or Tokyo, telematics scoring could be a viable business model for insurers to underwrite risk, and could be an attractive proposition for customers to get good-driver discounts. However, telematics scoring that captures second by second driving behavior may not be very effective in gridlocked traffic in South Asian cities like Mumbai, Delhi, Bangalore, and Dhaka. Nevertheless, telematics could still be used in the emerging economies when scoring drivers during weekends and longer trips on the expressways. 

In addition to the behavioural factors discussed, many telematics scoring providers are also proposing a variety of new factors for scoring, such as locational risk (types of roads–interstate, city, residential, school and hospital zones), mobile, sms, and application usage while driving (distraction indicators). Some telematics providers are also introducing a social component by gamification of the driving process, issuing best driver badges among friends, and accumulating good driver scores which can be exchanged for fuel, food, drinks, movie tickets and other perks. 

The incorporation of telematics and AI in insurance is as inevitable as the adoption of electricity, automobiles, and steam engines in the last couple centuries. Scoring people based on how they drive, and not based on secondary rating factors such as age, gender, or the zip code, could become the norm over the next decade. Nonetheless, it is important that we proactively and continuously assess where the underlying business models need to evolve to accommodate, and address the consequences, arising from these new technological and data possibilities.


2019: A roller coaster ride in books

This year was a real roller coaster ride with books. There were some real gems and some real duds. A great decision i made this year was to discontinue books that were either poorly written or weren’t worth the time and effort. Here are some books and the ratings.

My top 3 for the year were:

  1. Leadership in Turbulent Times by Doris Kearns Goodwin, a US Presidential Historian
  2. The Signals are Talking by Amy Webb
  3. I Contain Multitudes by Ed Yong

Vishnu’s Bookshelf

Check out some of my 2019 reads.

Books I read in 2018

Vishnu’s bookshelf from 2018

Here are the books I read in 2018. This year my reading ranged from history to AI to business to economics!
Did not get to my goal of 50, but 28 is not bad either

Share book reviews and ratings with Ajetunhsiv, and even join a book club on Goodreads.


Cool Books I read in 2017

It was well short of my personal goal of 50 books…but I cannot complain. I read some really good ones in 2017.

Vishnu’s 2017 Bookshelf

Review of “Internet of Money” by Andreas Antonopoulos

Andreas Antonopoulos is considered one of the foremost authorities on Bitcoin. In fact his Senate hearing in Canada in 2014 (Click here for speech) is considered one of the key turning points in Bitcoin’s journey in educating the governments on one of the most pathbreaking phenomena since the internet. This “book” is essentially a transcription of his key speeches.

If you are embarking on your bitcoin learning journey, this book and the associated videos must be your first readings and viewings. Antonopoulos keeps his speeches accessible to everyone and that is the key in this book. There are some fundamental concepts that are explained in these speeches, such as simple networks and innovation at the edge, infrastructure inversion, and key design principles for the bitcoin.

Discussions about networks was an illuminating aspect and it gives a clear insight into the mind of Antonopoulos. He is one of the fiercest advocates for decentralization. He draws interesting parallels to the original pulse dialing phone network, which is a smart network with “dumb old dialing phones” at the edges. In the bitcoin age this is flipped on its head. The network is “dumb” but the innovation is pushed to the edge. As Antonopoulos puts it, the network is dumb as rocks and it simply processes scripts. Nothing else. Doing this affords the advantage that central permissions are not needed to innovate. Innovation can happen independently, and the network can stay as is.

Infrastructure inversion is the principle where initially the new technology runs on the old creaky infrastructure, then it upends the old infrastructure and the old technology also begins to run on the new innovations. Classic example is that of automobiles running on cobblestone and unpaved roads. They faced a lot of challenges initially, and then once roads were paved it benefited both cars and horse-drawn carts. These and several other fundamental bitcoin principles and disruptions are eloquently explained, with easy to grasp examples that makes the Bitcoin very real and appealing to all.

For anyone interested in bitcoin, this collection of essays / speeches is a must read.

My rating 5/5