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Corona Kavach: Implications & Opportunities for Insurers in India

5 minutes, 23 seconds read

In this unprecedented time, the announcement of the Covid Standard Health Policy by IRDAI has unlocked dual benefits. One, people who don’t have comprehensive health cover, will now have insurance covered against Covid. Two, most of the processes including distribution and claims have a digital process, which is simplifying operations for Insurers.

All health and life Insurers will start offering Corona Kavach and Corona Rakshak policies by July 10, 2020. The premiums for both the products are standard PAN India.

What is Corona Kavach policy?

Corona Kavach is a standard indemnity-based policy that will cover the cost of treatment of any comorbid conditions, including pre-existing conditions, along with the treatment for the coronavirus infection.

What is Corona Rakshak policy?

Corona Rakshak is a standard benefit-based policy, which hands out a pre-agreed lump-sum upon diagnosis. This can be used as a supplement for additional funds during a pre-insured health incident.

The main aim of these policies is to help people with better health insurance coverage in these unprecedented times of pandemic. To simplify operations, most aspects of these policies will be handled digitally. “Trusting digital” has been a serious concern for customers and a major roadblock in the Insurers’ digital transformation strategy. But this move by the Government is opening new avenues for the adoption of digital among Insurance customers.

Let’s discuss the business implications and new opportunities with the introduction of Corona Kavach and Corona Rakshak policies.

New Challenges with the Introduction of “Corona Kavach”

While customer-centricity is the main theme of announcing standard Covid policies, there lies some inherent challenges to implementation.

For instance, claims management will be an important aspect of these policies, especially when Insurers can foresee the voluminous requests. In general, nearly 80% of claims filed are manually reviewed by adjusters.

Normally, health insurance claims settlement is a document-heavy process where the claimant has to submit a number of documents like hospital discharge certificate, medical bills, prescriptions and pharmacy cash memos, FIR (for accident cases), to name some. There isn’t a standard format for all of these documents and Insurers have to manually review each of them which is time-consuming and delays the settlement process.

Insurers can leverage technologies like ICR (Intelligent Character Recognition) with handwritten document processing capabilities to improve speed & accuracy and reduce manual document processing efforts.

For example, Mantra Labs’ ICR can extract data from a 5-page handwritten Insurance form in under 30 seconds with over 90% character level accuracy in interpreting data. 

Related: Pushing the Envelope on ICR Accuracy in Hand-written Forms

However, the influx in the number of claims applications will require a robust system infrastructure that can handle thousands of claims without lag.

Another foreseen problem will be handling the sudden increase in customer queries. Normally, Insurers rely on call-centers for handling customer queries. However, in this difficult time, when most of the staff is working remotely, it is difficult for Insurers to coordinate and scale. For the success of this scheme, Insurers need to focus on reducing the pressure on customer support centers through automation solutions like chatbots. Insurance chatbots have been found to reduce human intervention for routine queries by 10x.

New Opportunities for Insurers amidst Covid-19

Covid-19 has accelerated the adoption of digital as well as increased affinity towards buying insurance. According to a survey conducted by Swiss Re, Indian consumers are seeking insurance driven by financial and mental health concerns.

ChinaIndiaJapanMarket Avg*
Searched for new policies73%62%13%28%
Bought a new policy56%28%7%15%
Made a claim
(those who hold a policy)
23%25%5%11%
*Singapore, Hong Kong, Australia

1. Mark Presence in Digital Insurance Marketplaces & Expand Portfolio

These policies aim to cater to people who don’t have a holistic health insurance policy. Given the continuously increasing cases of Covid and reliance on private medical facilities and the undetermined cost of treatment, people are inclined towards buying insurance policies to cover basic treatment costs.

Apart from benefiting individuals and health insurers, this move by the Government can also improve the market for life and non-life Insurers. This is also a great opportunity for Insurers to reach out to prospects in rural areas.

IRDAI has approved all possible distribution channels (physical and digital) including Micro Insurance Agents, Point of sale persons and Common Public Service Centers.

With no restriction on the distribution channels, Insurers have an opportunity to experiment online selling on existing popular marketplaces like PolicyBazaar, Gramcover, BankBazaar and PayTM.

2. Opportunity to Up-sell/cross-sell

“Insurance is not bought but sold” is the bitter fact. Making customers invest in a product that they might need in the future is somewhat hard to sell.

The Covid situation has made people aware of the benefits of insurance in mitigating their financial burden. With more customers, there’s a better chance to up-sell and cross-sell insurance products.

3. Awareness for Micro Insurance Products in India

Indians are accustomed to comprehensive insurance policies and prefer buying policies through insurance agents. Accelerating the change in buyer preferences, Covid Kavach opens opportunities for micro and usage-based insurance products.

Instead of comprehensive insurance policies, Microinsurance products are cost-effective and address the immediate need of customers. Small investments are comparatively easier and this opens the market for microinsurance products in the low and medium-income groups.

4. Digital Policy Documents

Earlier, as per IRDAI norms, Insurers were required to provide policy documents to customers in a physical form.

However, to reduce operational costs, IRDAI has allowed Insurers to issue the policy contract of Corona Kavach Policy in electronic/digital format through email/web link. This will not only help Insurers reduce the cost of operations but also encourage customers to trust duly signed digital documents.

Leveraging the Opportunity

When it comes to scalability, digital is the solution. Although every Insurer has a digital presence, not everyone has deployed automation for their core operations.

For example, most of the queries during this time will be regarding policy coverage, tenure, claims, etc. Self-service chatbots can help customers with immediate response and at the same time automate the claims filing process, policy renewal, raise tickets, and more. Moreover, NLP-based vernacular chatbots can converse with people in their local regional languages.

Related: Mantra Labs launches Multilingual AI chatbot with Video Calling for SMEs

The next step in the preparation for a digital future involves leveraging technologies like Artificial Intelligence. AI can help Insures to precisely understand different personas, policy preferences and customer journeys. It can help Insurance adjusters, claims managers, and other stakeholders with the knowledge about claimants and their current situation, hence delivering a more empathetic experience.

Related: How can Artificial Intelligence settle Insurance Claims in five minutes?


We build AI-First Solutions for the new age Digital Insurer across the entire Insurance Lifecycle. Please feel free to reach out to us for your specific requirements 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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