Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(147)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

InsurTalks Podcast with KV Dipu: Protecting the Demand-side in the New Normal

7 minutes, 26 seconds read

The outbreak of Covid-19 pandemic has deeply impacted the global economy. Industries such as healthcare, travel and hospitality among others are still reeling from the immediate fallout of the crisis. The Pandemic has exposed the cracks in the Indian healthcare system, and the exposure of India’s masses to a multitude of personal risks who are largely uninsured to stave off financial ruin. At the same time, Insurance has had to adapt their processes to the fast changing climate. Core insurance functions like claims processing and customer support operations have had to accelerate transition to the cloud in order to ensure operational continuity during the crisis and adapt to the new normal. 

In this special podcast, we talk to Mr. KV Dipu about how Insurance is coping with this crisis. Before joining Insurance, he worked at GE Capital for 19 years, where he has built a career in retail finance operations. He is a certified Lean Six Sigma Black Belt and a member of the Harvard Business Review Advisory Council. Today he drives digital transformation as the President of Operations, Communities, and Customer Experience at Bajaj Allianz.

During our conversation Mr. Dipu shared valuable insights on the state of insurance, how insurers need to gear up for the challenges in the New Normal and the initiatives undertaken by Bajaj Allianz to meet their customer’s expectations.

You can watch the full podcast here: 

Interview Excerpts from Insurance in the New Normal

Potential Insurance Frauds amidst COVID-19

Insurance, at least in India, is not strange to the experience of dealing with outbreaks even though at a smaller scale – with virus outbreaks like Ebola & Zika in the past. However there aren’t too many reliable historical models to learn from and you’ve stated in the past that fraud triggers can only work if there are strong flags sitting on top of really good data. In the absence of really good data and unreliable historical models, how does this affect dealing with fraud?

KV Dipu: That’s a good question and I think this is exactly what a lot of players today across industries are grappling with because no PCP or model ever envisaged this. And if you do not have passed precedents then you have to learn as you go. So I think that is clearly what we have seen. In terms of COVID-19, you can see a series of potential fraud possibilities. 

I’m using the word ‘potential fraud possibilities’ because we have to see how they play themselves out. One is you could find a lot of people who possibly could get into scams, that they can maybe influence the entire ecosystem, especially in terms of helping customers who are seeking benefits from the insurance company or various entities. And whenever there are losses you typically will find that there are people out there who are going to try to to make a fast buck. So I think that’s one area we need to watch out for. 

The other is you will actually find that as business models emerge there are some people who’ll be quick to jump into the game. For example, today everybody feels that health insurance is one thing we should focus on and that’s typically when you could have both type A and type B errors. You have middlemen who basically promise health insurance saying ‘I can get you this.. I can have my way through various insurance companies’. You may have people trying to forge various checkups through the entire process. 

So these are some areas which we are very off, right, and the good thing is even if a model from the past is not going to help us with the specific input I think our own experience of various scenarios will come into play.I think as long as we are smart on that front it will help us. Now this is where it’s a classic combination of technology and expertise technology can enable the process but you need years of experience to figure out the fraudulent ones from the good ones. Which is where I think established companies like ours which are technically and technologically savvy, as well as years of deep expertise will be really able to figure out who the fraudsters are.

Change in the Nature of Risks & Its Impact on Underwriting

From an underwriting perspective it’s usually said that poor underwriting leads to poor financial performance, so the ‘not knowing what to expect’ will definitely have an impact on underwriting losses. Going forward, how does this change the nature of risk from perhaps the actuaries point of view? 

KV Dipu: If you look at actuarial science, what they do with every event is they learn, right. The learning adds to their kitty, so to speak. So, today you have various players globally trying to figure out what the models are, what are the potential scenarios and we can also learn from the experiences of different countries. You see while it’s still a global pandemic, the scenarios across various countries are different. Some countries for example have had a very sharp recovery, where they’ve shown a v-shaped recovery. Now there are some countries which are in a u-shape recovery pattern, and  there are some where there is a recovery-outbreak-and then a recovery which would be a W pattern. 

So I think as we see the scenarios play themselves out in various countries, we draw learnings very quickly and then basically recalibrate our models accordingly, that’s point number one. Point number two – I think once the lockdown is lifted and then when you start to see people back on the roads, when you start to see cars back on the roads, and when you start to see hospitals functioning again – that is when I think the rubber will start hitting the road and that is when our extreme vigilance will help. I think as long as we’re prepared with data it will really help us get through this.

[Related: New Product Development in Insurance: The Actuary]

Product Innovations in the New Normal

Today a lot of companies are ‘investing in digital’. They’re making sure they have digital assets, capabilities and tools not just for employees internally in the business but for outward facing agents as well. And that has been  the trend even before the Pandemic had broken out. Most sales teams and channel partners are equipped digitally with mobile apps to generate quotes, issues policies even remotely. 

Given that the physical act of selling itself has been severely affected due to lockdown restrictions and social distancing norms, How can insurance build and protect the demand side?

KV Dipu: Okay, so there is one famous whatsapp forward doing the rounds nowadays. it basically says “Guess who’s responsible for digital transformation in a company? Answer number one: CEO. Answer number two: the relevant CXO. Answer number three: COVID-19.” No prizes for guessing, right? Now what COVID-19 has done is to the point that you made everybody believe that in a push product like insurance in-person meetings, relationship building  is all important and rightly so. And that is the reason this business is intermediated and it’s been that way for a while now. New normal is where people will have to learn how to do contactless selling. That is where COVID-19 helps because if let’s say COVID-19 had been restricted to let’s say one particular city or one particular sector you would not have had a change in universal behavior. 

But the fact of the matter right now is globally right I think there are more people under lockdown than at any previous point of time in history. We have so many people on lockdown and everybody realizes the need for social distancing and the need to go digital. That is when people are also more amenable to being sold to digitally. Which is why now the smarter companies who figure out that in the new normal we have to build relationships while being physically away, and manage to sell from remote or contactless sales as i call it – are the ones who will be able to make a difference going forward. 

The good thing is from a process perspective we have enabled them like you rightly said they have the tools to generate quotes, they have the tools to issue policies, they have the tools to even raise claims. It’s about willingness and that willingness has been accelerated and fast tracked by COVID-19. So what could have potentially taken a long time has now been fast-tracked now in the last 60 days – which is why once the lockdown is lifted and we go back into the world you’ll realize that some parts of this contactless selling or even large parts of it are here to stay.

Click on the below link to watch the full episode of InsurTalks with KV Dipu –

Mantra Labs is an InsurTech100 firm building products and solutions for fast evolving enterprises. To connect with us for interviews, drop us a line at hello@mantralabsglobal.com 


Podcasts in this series:

Cancel

Knowledge thats worth delivered in your inbox

Silent Drains: How Poor Data Observability Costs Enterprises Millions

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

  1. Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
  2. Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
  3. Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
  4. Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
  5. Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, Forbes turned to Datadog for data observability. Previously, siloed data and limited access slowed down troubleshooting. With Datadog, Forbes unified observability across teams, reducing homepage load times by 37% and maintaining operational efficiency during high-traffic events like Black Friday.

4. Lenovo Maintains Uptime

Lenovo leveraged observability, provided by Splunk, to monitor its infrastructure during critical periods. Despite a 300% increase in web traffic on Black Friday, Lenovo maintained 100% uptime and reduced mean time to resolution (MTTR) by 83%, ensuring a flawless user experience.

Why Every Enterprise Needs Data Observability Today

1. Prevent Costly Downtime

Data downtime can cost enterprises up to $9,000 per minute. Imagine a retail giant facing data pipeline failures during peak sales—inventory mismatches lead to missed opportunities and unhappy customers. Data observability proactively detects anomalies, like sudden drops in data volume, preventing disruptions before they escalate.

2. Boost Confidence in Data

Poor data quality costs the U.S. economy $3.1 trillion annually. For enterprises, accurate, observable data ensures reliable decision-making and better AI outcomes. For instance, an insurance company can avoid processing errors by identifying schema changes or inconsistencies in real-time.

3. Enhance Collaboration

When data pipelines fail, teams often waste hours diagnosing issues. Data observability simplifies this by providing clear insights into pipeline health, enabling seamless collaboration across data engineering, data analytics, and data operations teams. This reduces finger-pointing and accelerates problem-solving.

4. Stay Agile Amid Complexity

As enterprises scale, data sources multiply, making Data pipeline monitoring and data pipeline management more complex. Data observability acts as a compass, pinpointing where and why issues occur, allowing organizations to adapt quickly without compromising operational efficiency.

The Bigger Picture:

Are you relying on broken roads in your data metropolis, or are you ready to embrace a system that keeps your operations smooth and your outcomes predictable?

Just as humanity evolved from carving records on clay tablets to storing data in the cloud, the way we manage and interpret data must evolve too. Data observability is not just a tool for keeping your data clean; it’s a strategic necessity to future-proof your business in a world where insights are the cornerstone of success. 

At Mantra Labs, we understand this deeply. With our partnership with Rakuten, we empower enterprises with advanced data observability solutions tailored to their unique challenges. Let us help you turn your data into an invaluable asset that ensures smooth operations and drives impactful outcomes.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot