InsurTech is a buzzword nowadays where a variety of technologies are set to transform the traditional insurance industry. In the last two years, insurers have already transformed themselves digitally to offer convenience, security, choice, and a seamless experience to their customers.

Accenture estimates that insurance companies can increase their annual profitability by 20% with the right investment in the technology.

Internet of Things (IoT), telematics, drones, the blockchain, smart contracts, and artificial intelligence (AI) are providing new ways to measure, control, engage customers, reduce cost, improve efficiency and increase customer experience.

Here are five ways Insurers can stay ahead in the market and successfully fulfill high customer expectations.

1. Lower Insurance rates:

– Fitness apps or wearable devices:

Staying fit has many perks. Some of the fitness apps like Wysa and wearable devices help maintain weight, and food habits and boost energy and mood. And most importantly they can help save a huge amount of expenses related to health insurance costs. Numerous insurance providers have tapped into wearable devices to keep motivating their customers to stay fit and healthy and offer them discounts and benefits based on fitness levels.

– Self Driving car:

Self Driving cars can help in reducing the chances of accidents and lower life insurance rates. Since road deaths are a significant percentage of deaths in the entire world, any slight downward change will ultimately lead to lower deaths and hence life insurance claims.

2. Fraud Prevention:

Insurance fraud costs companies billions of dollars per year across the globe. Insurance companies should establish a technology framework, tap into advanced automation and analytics, and take steps to prevent it.

– Digital Signature:

Digital signature technology is without a doubt lowering fake insurance account activation and hence a fraud. For example, a digital signature can prevent fraud- insurance purchased after the accident can be brought down with digital signatures verifying the actual date.

– Data analytics:

The technology involves data mining tools and quantitive analysis. Data analytics can be applied to detect fraud. Predictive analytics is useful to improve the fraud detection process, helping prevent claims payouts. Analytics on claims and fraud transactions helps enhance risk management.

3. Lower underwriting cost:

–IoT

According to IoT Analytics, the global number of connected IoT devices is likely to grow at 9%, with 12.3 billion active endpoints. By 2025, there will likely be more than 27 billion IoT connections, which will have a significant impact on the availability of real-time information that insurers can use for better pricing/underwriting. Drones are satellites on steroids at least as far as underwriting is concerned. Satellites have dramatically changed how home insurance policies are written due to fire. Everything can be captured via drone footage even the houses that get covered behind the trees. Captured data can be used for underwriting purposes.

4. Billing efficiency:

Billing systems are not only integrated but now can accept varied forms of payments allowing ultimate flexibility to the customer and thereby making the billing systems efficient. The automated systems inform and remind customers of approaching due dates for premiums thereby lowering unintentional defaults.

Digital wallet has become one of the most widely used platforms for payment systems. Insurance companies are leveraging payment gateways like Google Play to sell insurance to users. Last year, SBI General Health Insurance launched Arogya Sanjeevani on Google Pay Spot to offer standard coverage at affordable premiums and improve the penetration of health insurance in the country.

5. Specialized insurance:

Each type of insurance is different from the other and the factors that are suited to one are not suited to the other. This requires the insurance agents to have specialized knowledge and the internet helps. however, Machine learning is vitally important here. It has the capability to learn and analyze billions of patterns and identify suitable underwriting clauses as well as identify specific customized plans for the customers based on the data provided. This can change the customer perception of the insurance company and provide an engaged customer who is likely to stay longer.

Dinghy, is a pay-by-the-second insurance provider that customizes coverage for freelancers and businesses where customers may switch their policies on and off as needed without any upfront premiums, interest, credit checks, or fees.

6. Smart and Faster Claim Processing and Settlement:

–AI-Powered Chatbots:

Claim settlement has been one of the pressing issues in insurance. With intense competition looming in the market, delay in the claim settlement gives a bad experience to the customer who prefers to switch to another brand. Insurance providers worldwide have been investing in AI-powered insurance chatbots to enhance customer experience. Metromile can validate 70% to 80% of claims instantly using AVA, an app based-claims assistant.

7. Data-driven pricing

–Telematics:

Innovation has become one of the top priorities for insurers today due to rapid change in customer demand. The usage-based insurance market is projected to hit over $190 billion by 2026, telematics is allowing carriers to capture user data and create personalized usage-based insurance products.

For example, auto insurance was based on a pay-as-you-drive model where customers use to pay a premium based on the distance covered. But with technological innovation, insurers are working on a pay-how-you-drive model where customers can get discounts based on their driving skills.

Rise in demand for innovative solutions, intelligent experiences, and speedier processes has led to technological disruption in the insurance industry. According to IDC, IT spending in the insurance industry will increase globally at a CAGR of 6.0% by 2024, touching $135 billion. With continuous investment in technology, insurers are working on improving customer experience and operational efficiency to maximize profitability in the long run.

Thanks you Scott W Johnson, owner at WholeVsTermLifeInsurance.com for providing your valuable information on how technologies are helping Insurance industry.

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

By : Sampada

Published on Dec 19, 2024

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:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, 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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InsurTech: 5 benefits of technologies in Insurance Sector