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Key Takeaways of Indian Insurance Summit & Awards 2019

India, despite being the 2nd most populous country on the planet, accounts for only 1.5% of the World insurance premiums, and 2% of World life insurance premiums. But, with the increasing numbers to serve, the insurance market in India promises huge growth and exciting potential – were only about 20% of Indians were insured last year.

Key challenges like market penetration, product innovation, risk and fraud need to be mitigated, for insurance players to achieve better growth, customer satisfaction and profitability.

The recently concluded Indian Insurance Summit and Awards 2019 aimed at having robust and key focused area discussions on these challenges, brought together the entire insurance industry network in front of a global audience.

Here are some of the highlights and takeaways from the two-day conference:

Key takeaways of India Insurance Summit and Awards 2019

  • Application of AI beyond claims and underwriting:

AI has paved its way far beyond claims and underwriting policies. The rising InsurTech wave is marking this change by tailoring solutions for individual customers and replacing the one-size-fits-all type of product that is currently available. AI also plays a major role in fraud detection and risk management strategies.

AI in insurance will allow carriers to deliver scalable and customized solutions for members and policyholders,”

 says Ramon Lopez, Vice President of Property & Casualty Claims and Innovation at USAA.

Although, India represents a smaller share of this market, in terms of revenue in comparison to the North American region; India, (along with the rest of Asia) is expected to outperform Europe over the next five-year period.

  • Product innovation for the ease of insurance processes:

While the insurance landscape is experiencing radical changes in product innovation; innovation in technology is the next frontier.

Predicting the probability of future losses can help insurers improve pricing and accuracy; which precisely can be useful in case of risk, with little historical data from which estimates have to be drawn. Around 44% of the insurers say that they have started deploying predictive analytics solution.

California based InsurTech, Carpe Data, has fully automated systems that leverage social media to detect claim frauds and ease out specific insurance processes. Allstate insurance partnered with Carpe Data to generate meaningful insights and help them to mitigate risks in insurance processing.

“The insurance industry is used to working with historical data—the most important                challenge before them is to move from that model to a predictive one.”

Gilles Ferreol, Managing Director, CNP Partners

Bajaj Allianz introduced usage-based auto insurance called ‘DriveSmart Service’. The service monitors the car through a vehicle tracking-device and provides relevant diagnostics data on the performance of the driver.

  • Cognitive Insurance is a new wave of innovation:

Data is a vital ingredient for going Cognitive. The cognitive insurance business is one that allows underwriters to be equipped with a repertoire of AI enabled tools, empowering them to make better and more informed decisions about their customer.AXA Insurance has implemented a Google Tensor Flow-based application to optimise pricing by predicting large-loss traffic accidents with over 78% of accuracy. By leveraging a deep analysis of their customer profiles, AXA was able to understand which clients were are at a higher risk of large-loss cases requiring payment of more than 10,000 – which means, they were able to optimize the pricing of its policies.

Cognitive computing is at the “peak of inflation” on the Gartner Hype Cycle. The Cognitive approach to insurance business after the digital insurance business is the new wave to bring innovation and transformation purpose of going cognitive was created solely with the purpose of reducing human effort and refining the existing process across various insurance verticals.

  • Use a Sandbox approach to test customer’s interest:

To keep pace with the fast-evolving world of InsurTech, insurance companies should consider testing their products in a controlled environment or a “Sandbox”. This approach can provide certain advantages such as allowing insurers to launch unconventional products on a pilot-basis before seeking necessary approval.

The first insurance plan launched under this method, called “Insurance Khata” was directed towards those with seasonal incomes, mostly belonging to the underserved sections of Indian society. The buyers can pool multiple single plans in one account.

 “We want insurers to think out-of-the-box,” said Nilesh Sathe, a member at the IRDAI.

This rather unique proposition encourages insurance companies to place the policyholder right at the front of their approach, consequently not allowing regulation in being a constricting force in their innovation journey.

Data, by its very nature, is both an asset and a liability, which presents inherent risks in its handling and management. Risks that can be quite severe, in a business foundationally based on dealing with uncertainties.
Insurance is one of the richest data-driven businesses, and the consequences of a data breach extend far beyond the reputational damage that results from negative news headlines.

On July 2018, SingHealth, the largest network of healthcare institutions in Singapore, came under a severe cyber-attack and the personal data of around 1.5 million patients, including those of the Singapore PM, Lee Hsien Loong, were stolen.-Straits Times reports

In the past couple of years, the insurance industry has fallen short, by being on the defensive, of handing cyber-attacks and cyber-frauds. The industry cannot afford to take be reactive for much longer – at some point, they need to be thinking ahead of their adversaries.

The non-partisan agenda of the Summit was to explore challenges and their deterrents like market penetration, product innovation, risk, and fraud. The discussions were designed to draw out clear outcomes for the industry together – in order to realize growth, customer satisfaction, profitability and deliver definitive business value. Mantra Labs was proud to sponsor the successful Summit and partake in the insightful conversations held between insurance leaders from all corners of the industry.

We hope to see you all again, in the next edition!

https://www.insurancebusinessmag.com/asia/features/interviews/protecting-the-insurance-sector-from-cyber-threats-109124.aspx

Together Towards AI: Notes from InsureTech Connect 2017

Strategic Technology Trends in Insurance

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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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