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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 


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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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