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Here’s how Insurtechs are evolving India’s Insurance landscape during the Pandemic

7 minutes read

The COVID-19 pandemic and subsequent global lockdowns triggered plenty of structural changes that forced insurance companies to enter the arena with their eyes on the prize. The pandemic year thus proved to be a catalyst, in turn, nudging insurers to shift their focus and prioritize customer experience, market agility, and business resilience. 

According to BCG, “Globally, insurtechs raised $7.5 billion last year, as COVID-19 accelerated the need for digital transformation in insurance.” 

Investor funding in insurtech came to $5 billion in the first quarter of 2021 with 261 deals, according to Forrester’s “Insurtech funding roundup, Q1 2021” report. 

How has the pandemic impacted Insurtechs in India 

India is the second-largest insurtech (insurance technology) market in the Asia-Pacific region, accounting for 35% of the $3.66 billion of venture capital coming into the sector, according to S&P Global Market Intelligence data.

“Insurance technology investors are attracted to India since it is one of the fastest-growing insurance markets in the world,” said the report. 

Insurance premiums in India have been reported to have totalled $107 billion in India until March 31, 2020, growing at a compounded annual growth rate (CAGR) of 10% from FY15 to FY20. 

“While big techs are vying to become digital intermediaries in the insurance space, established carriers are building proprietary digital channels. Startups that assist both incumbents and big techs in making this transition will likely emerge as winners,” the S&P report continued.

“Partnerships between large insurers and insurtechs have the potential to enable more personalized online distribution, predictive underwriting, and more efficient claims management,” said Alpesh Shah, managing director and senior partner, Boston Consulting Group while speaking with the business daily, Mint. 

Read: How Insurtech is Reshaping the Future of Insurance

The fast-growing industry is introducing solutions for AI-based underwriting, virtual claim filing, among others. The next big revolution could come in the form of blockchain contracts, where customers might not need to file a claim. Bajaj Allianz General Insurance has already introduced a travel insurance product that uses blockchain to settle claims on flight delays automatically.

In another scenario, Acko General Insurance tied up with over 20 digital platforms across retail, travel, finance, and others to distribute bite-sized insurance. Ola’s trip insurance by Acko has insured more than 23 million rides in less than 10 months and is being hailed as one of the most innovative insurance products in the industry.

Another Insurtech startup, Toffee Insurance, offers insurance against theft or damage to bicycles and accidental injuries related to a fitness activity or sport.

Image Courtesy: fintechnews.sg 

Speaking about the Insurtech evolution and their funding in India, BCG’s India Insurtech Landscape and Trends reports that, “Global funding in Insurtechs have grown from about $2 billion in 2016 to $6 billion in 2020. While Americas account for the largest share of funding (68 percent of funding in 2020), Asia has been the fastest-growing geography till 2019 (5-year CAGR of 60 percent). In India too, albeit with a smaller base, funding has seen an increase from a modest base of $11 million in 2016 to $287 million in 2020. The funding trend has continued with Turtlemint raising $30 million in November 2020 and Digit raising around $84 million at the start of 2021.” 

“APAC-based insurtechs attracted $1.4 billion—up 15% year-over-year from the previous year—driven by companies headquartered in China ($800 million) and India ($450 million). Representative examples are Medbanks, a medical database-services company offering oncology-related services, which brought in $305 million in Series E+ funding, and Policybazaar, a price-comparison portal that raised $130 million in Series E+ funding,” the report continued. 

Insurers vs. Insurtechs in the current ecosystem

Image Source: everis.com 

Claims in the digital age

Even before the COVID-19 pandemic struck, customers had already begun leading digital-centric lives that required insurers to rethink their MO and strategies. “With the demands and constraints of the pandemic, a technology-enabled service delivery with a digital claims process is non-negotiable and mission-critical. In the past, these needs may have gone unmet due to lack of technology solutions or an insurer’s inability to capitalize on technology, but the situation today is very different,” reports Deloitte. 

The COVID-19 pandemic affected Insurtech firms on various levels, impacting demand, claims, and loss patterns in a number of ways across product lines and operating models. 

Thus, arose a need to overhaul and reset the core value system and give way to a new growth engine led by customer retention and loyalty, both driven by customer interactions with insurers, specifically during the claims experience.

“Claims operations, which have been traditionally treated as outputs of a “reactive back office,” will have to become a powerful differentiator—innovative and uncompromising on customer service, with multifaceted talent and capable of driving strong results,” continues the Deloitte report. 

Digit, an India-based multi-line insurer, launched a new product that covers pre-and post-hospitalization expenses, road ambulance charges, and a second medical opinion regarding eight viral diseases, including COVID-19 and dengue. 

For Care Health Insurance, erstwhile Religare, Mantra Labs, the Bengaluru-based Insurtech firm deployed Hitee, a conversational chatbot to be the first-level customer support for existing and new customers. This led to higher New Business Conversions by a factor of 10X, and a significant drop in Customer Queries over Voice Support by 20%.     

Source: www.mindbowser.com 

The pandemic and its subsequent wave accelerated the shift towards going digital in the insurance industry. In 2021, there is an apparent inclination towards personalization, data mining, automation, and cloud computing in the Insurtech space.

Read the 7 key trends we’ve expected this year in Insurance here. 

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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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