Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(44)

Solar Industry(8)

User Experience(67)

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(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(146)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(21)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

InsurTalks Podcast with KB Srinivas: The Road to Recovery for Indian Insurance Beyond Covid-19

8 minutes, 40 seconds read

It was hard to predict the extent of damage that the COVID-19 pandemic might cause to the whole world. As several economies across continents came spiraling down, various industries have been badly hit. Sectors such as healthcare, travel, hospitality, etc. have incurred huge losses. This has put a lot of pressure on the Insurance sector in terms of claims processing, operations, customer support, etc. To go one step deeper and get a sense of how Insurance and InsureTech industries are coping with this crisis, we interviewed Mr. KB Vijaya Srinivas — the insurance industry veteran and former Chairman cum Managing Director of United India Insurance Company Ltd.

Mr. KB Vijaya Srinivas is a renowned insurance industry expert with vast experience spanning around four decades. Mr. Srinivas retired after holding Joint Additional charge of United India Insurance Company Ltd. as Chairman cum Managing Director. He joined United India Insurance in 1981 as a direct recruit officer and has served in various metropolitan cities, in various capacities encompassing almost all the different aspects of the organization. 

Mr. Srinivas served National Insurance Company as a General Manager. He later became the Chief Marketing Officer and held charge of many other portfolios like foreign operations, crop insurance, rural insurance, publicity, CSR, estate, administration, to name some. 

He has been a recipient of awards on many occasions from the Insurance Institute of India for essays on various topics. Now, he is a freelance writer, trainer, consultant, and also director of MDIndia Networx and MDIndia TPA. 

Excerpt from the interview- 

Navigating the New Normal in Insurance

How is the Indian Insurance Industry addressing the impact of the COVID-19 pandemic? How are Insurers navigating the ‘new normal’?

Mr. Srinivas: Insurance follows the economy. If the economy goes up, insurance also does well and vice-versa. Due to the lockdowns, businesses across industries are not doing well. This has led to a decline in the demand for insurance. Due to a lack of adequate income, people are delaying buying insurance or being selective in their insurance requirements. 

However, on the brighter side, due to the pandemic, more people are looking towards buying health insurance. When one enters in one type of insurance, then there’s a tendency to explore other lines of insurance products as well.

There’s a possibility of introducing a new line of insurance such as Pandemic insurance. Pandemic insurance is available internationally but not in India. Following the recent example of Wimbledon, where they bought pandemic insurance in 2003 during the SARS outbreak, they now got a compensation close to 140 million euros due to this year’s cancellation of the event.

[Related: The Biggest Insurance Payouts in History]

In India, IRDAI and the General Insurance Council took some proactive steps. The General Insurance Council came out with some guidelines such as waving of the clause in Fire Insurance during the lockdown which states that if a factory is closed without notice for more than 30 days, the insurance cover will cease to exist. Spot Survey in accident policy has become lenient — such as survey through photos instead of in-person surveys at the accident spot is allowed. IRDAI has also relaxed the payment of premiums and claims processing of COVID cases within 2 hours. 

Processing of COVID-19 based claims

With the IRDAI placing special instructions for COVID-19 based claims, such as authorization of cashless requests within two hours, and processing of claims within shorter time frames — Insurers are trying to settle claims as quickly and without hassle as possible — Has this put undue burden on ‘claims processing systems’?

Mr. Srinivas: Health insurance incidentally constitutes a significant number of claims and amount of premiums in the insurance industry. Major portion of the claims process has already been digitized. This will make things faster especially for COVID patients. I believe that claims processing in the current times has not put a huge strain on health insurance. Certainly, due to lockdowns and remote working, things will be slightly delayed but once the lockdowns are lifted, they’ll be able to serve their customers better.

COVID-19: An impetus to going Digital?

Is the ongoing pandemic, going to be the impetus for insurance to move completely online (digital)?

Mr. Srinivas:  Definitely, this pandemic will give a special thrust to insurance towards going online. Moreover, customers will prefer online transactions rather than standing in line and submitting physical documents. IT and Telecommunication companies should come up with technologies that can make these digital processes seamless.

[Related: The role of AI in enhancing claims experience for Insurance customers]

Protecting the Demand side during the Lockdown

Given that physical selling (via agents) has been severely affected by lockdown restrictions and social distancing norms, we are starting to hear that the Insurance advisory is now turning to digital to stay in touch with their customers — What steps do Insurers need to take to build and protect the demand side?

Mr. Srinivas: For many years now, insurance has been a product that is sold and not voluntarily bought. There has certainly been a thrust to digital platforms for sales and marketing however, agents still contribute to a major chunk of the premiums. All companies have online agent portals through which they operate. 

A survey shows that around 75% of people still trust agents for buying a policy. They’ll be a dominating factor for many years to come. Collecting premiums and making payments have already been happening online, and it will accelerate in the future. Agents will also rely on online platforms for sales and marketing. Due to social distancing, in-person sales will drop down but agents will certainly protect their customer’s interests through other channels.

How can Insurers equip their agents to generate sales in this pandemic?

Mr. Srinivas: There’s a tremendous amount of work to be done in this area. Insurance agents are very diverse. Some are from rural areas with no knowledge about insurance and some are on the other end of the spectrum such as highly qualified Chartered Accountants. 

Today, a good number of agents still survive on the traditional line of insurances such as Motor or Health. First, insurance companies should educate their agents on the types of insurance available. Second, they should train their agents on how to sell the insurance products. Motor insurance is easy to sell as it is a standardized product but event cancellation insurance is hard to sell.

Insurance for Rural India

“Taking Insurance to Bharat” was a main theme entering into 2020. Going forward, what can Insurers do to reach Rural India?

Mr. Srinivas: In 1972, insurance was nationalized. Before which it was in the private sector and very urban-centered. The objective behind nationalizing the insurance sector was to take insurance to the deepest parts of India. There were so many districts where not a single person had insurance. So we had to open offices in these rural areas. It was a tough job but we opened offices in those districts. 

The second initiative was starting a single-person office to take insurance further into the deep pockets of rural India. In 2013-14, the government brought in new guidelines where offices were opened depending on the population in the area. Many less populated areas did not have offices. Offices were opened in those areas. 

Digitization has taken insurance one step further. New marketing channels have opened. In remote locations, along with agents, Point-of-Sales persons sell insurance. From 10th pass person to small shop owners, anyone can become a Point-of-Sales person. 

Schemes such as Pradhan Mantri Suraksha Bima Yojna which is an accident policy has more than 15 cr people under it. Ayushman Bharat covers around 10 cr. families (around 50 cr.) people across the country. Pradhan Mantri Jeevan Jyoti Yojna (a life insurance policy) and Pradhan Mantri Fasal Bima Yojna (crop insurance) have connected India to Bharat in a very big way. Perhaps it has not been projected in a constructive way however they have made significant progress. 

On-set of COVID-19, many states have said Ayushman Bharat which was previously limited to BPL strata (Below Poverty Line) is now providing coverage for the entire state. Things are happening silently but effectively.

Product Innovation

Before the ongoing crisis, the IRDAI had recently set up sandboxes for experimenting with new innovative motor and health products. What kind of new insurance products are being/need to be built during the Pandemic crisis?

Mr. Srinivas: Pandemic insurance is available internationally but not many have taken it. In India, this policy can be easily built and will have wider acceptance. For example, the Business Interruption policy states that claims can be filed for loss due to fire or breakdown of machinery ultimately leading to loss of revenue or profit. This policy was earlier called the Loss of Profit. Pandemics are not covered under this policy. 

There has been a lot of debate over this topic internationally as well whether claims should be processed or not. Time has come to develop a policy that includes business interruption due to pandemics causing loss of revenue. Limited travel due to the pandemic crisis has affected the various industries such as airline, shipping, hospitality, etc. There is no insurance cover for the losses incurred by these businesses. Spoilage of goods due to deterioration during the lockdowns, non-use of vehicle and machinery also cause a huge loss. It’s time to have policies or clauses in place for losses due to these reasons. 

Role of AI in Operational Continuity

Will AI have a role to play for Insurers trying to maintain operational continuity during this COVID-19 Pandemic? 

Mr. Srinivas: Most definitely. In 2015, during Pradhan Mantri Fasal Bima Yojna there was a challenge of handling huge volumes of data for which we had to make use of technology like satellite imagery and drones to assess the loss for claims processing. AI will have a larger role to play in insurance for its success. 

[Related: Embrace the New Normal | Business Continuity Solutions]

Closing Note

This exciting interaction gave a deep insight into the history of insurance in India and provided some useful insights on paving the way forward in the New Normal. Mr. KB Vijaya Srinivas also highlighted the importance of InsurTechs in the New normal and spoke about new insurance lines and policy coverages being introduced as a result of this pandemic.

AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com. 

Podcasts in this series:

Cancel

Knowledge thats worth delivered in your inbox

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

By :

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot