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TOP 10 INNOVATIVE INSURANCE PRODUCTS OF 2019

6 minutes 9 seconds read

We are witnessing the slow but sure, uberization of insurance. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers. The industry is abuzz with a steady influx of new innovative products, deriving value in areas that were previously untapped.

Processes like faster KYC verification and onboarding, automated underwriting, virtual claims adjusting, to name a few have become hot commodities within the last year. With AI-assisted technologies improving functionality, reducing real-time data fraud or meddling; insurers are creating custom-fitted coverages for the end-user.

For example, AI-powered underwriting solutions are already saving up to 97% of the time and resources that were traditionally required, enabling the corporate underwriter to specialize in cases that require deeper thought and analysis.

According to a recent CB Insights report, here’s what’s next for P&C Insurance.

The general insurance industry in India alone is valued at US$ 21B in 2019, growing at 13% CAGR over the next 5 years, and is expected to touch US$ 57B by 2025. Customer’s coverage expectations in the subcontinent have shifted toward desires of flexible insurance products that more closely match their lifestyle needs. These trends across the APAC landscape mirror the changes being witnessed in more advanced insurtech markets across Europe and North America.

Keeping customers primed at the centre of insurance innovation, here’s a look at the top ten most game-changing products in insurance today (in no particular order)

  • Splitsurance: Allianz Suisse used KASKO’s cloud-based insurance lifecycle platform to create and run a new type of insurance product – splitsurance. The offering targets university students in Switzerland, who live in a ‘flatshare’. Customers can get a liability cover, insure up to three high-value items of their choosing and also get discounts if their flatmates decide to join. Users can manage and update their cover autonomously through an after-sales customer portal.  
  • CUVVA: Cuvva provides hourly car insurance. In the mobile app, you simply enter the registration number and approximate value of the car you are borrowing from a friend or family member, choose the time you want to be covered for, take a picture of the car and Cuvva will get you an instant quote. Cuvva integrates with Facebook so that you can see which of your friends have cars to borrow. Cuvva queries various data sources to check driving licence data, the Claims and Underwriting Exchange and automated fraud protection to verify coverage quicker than legacy players can.

  • Digital Risks: DigitalRisks is an insurance specialist built for tech companies, offering a flexible, pay monthly Insurance-as-a-Service model. A founder could start out by protecting their laptop and end up with employer liability insurance and insurance against data breaches as the company grows.

  • Back Me Up: Back Me Up is an offshoot of Ageas. Their unique proposition is to be a parental-like cover for young people and students. For £15, one can insure their three most valuable items (eg: laptop, mobile), that also includes theft loss and worldwide travel insurance, plus there are no annual contracts.

  • Mango: The Mexico-based life and retirement insurance intermediary, allows users to obtain life insurance “in minutes.” They are pioneers in Mexico, who use technology to streamline every interaction you have with your insurance, avoiding unnecessary paperwork and confusing coverages. They have intelligent bots at work to answer insurance related queries, plus their UI is outstanding.

  • Bought By Many: The UK-based startup is a free, members-only service that helps users to find insurance for the not so common things in life. They offer pet, travel, car, bike, shoes, gadgets, home insurance covers and more. Members save an average of 18.6%. The company negotiates discounts directly with insurers for the clients’ unique situations.  

  • Dad Cover: The product is uniquely propositioned for Dads looking to get life insurance and financially protect their families. They’re full-sized professional financial planning firms, working with life insurance specialists.  Using a streamlined service, one can get a free quote after a quick chat with their DadBot, then one of their associated FCA registered advisers will talk you through your needs, answer all your questions and give you proper independent advice on what’s best to help protect your family.

  • Go Girl: GoGirl is a woman-only drivers insurance, that rewards good drivers with lower premiums. The insurance cover also includes a free courtesy car when your car is in for repairs, legal cover, child car seat, personal accident and windscreen cover. The company also insures your handbag and its content if it is stolen from the car. A free quote is available in minutes, and the whole transaction can be completed online.

  • Safety Wing: The “Insurance for Nomads” via SafetyWing is travel insurance that’s creating a safety net for online freelancers and entrepreneurs. The company offers coverage – up to $250k via Tokio Marine HCC – for unexpected illness or injury, including eligible expenses for the hospital, doctor or prescription drugs. They plan to extend their products to medical travel insurance in the near future.

  • Vlot: The Vlot platform provides life risk analysis and coverage solutions that smoothly adjust to your changing life situations. If you meet unexpected changes in your life, such as moving to a new city, getting married, or loss of a job – you can adjust your life risk coverage accordingly and never be over or underinsured. You only pay for what you really need in your current life situation, and control the premiums as and when dynamic changes occur.  

Special mention:

Fizzy: Fizzy is a revolutionary web & mobile insurance cover for flight delays of 2 hours or more. Developed by AXA, with Fizzy you combine the benefits of a startup and the insurance knowledge of a global insurer. They offer a one-shot coverage tailored to your own flight route, with automatic compensation in case of a delay, with no exclusions. You can purchase fizzy in 4 clicks at any time after your flight ticket has been purchased, up to 5 days prior to departure.

As customer tastes continue to evolve, the future looks promising for the state of innovation, while insurers align their offerings in lieu of the demand for newer insurance products.

The marketplace of insurance ideas is already a reflection of the changes customers want to see from their insurance providers, with young insurtechs being instrumental in bridging those unmet need-gaps, and bringing out positively unique insurance coverages for the average consumer.

(Note: The products highlighted here are not rank-based and are not indicative of the ‘best’ insurtech products available today. For more analysis on Insurtech products such as those from Lemonade, Trov etc. – which are not included here, read our blog on the Adoption of Chatbots across Insurance.)

webinar: AI for data-driven Insurers

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders and decision-makers on April 14, 2020.

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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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