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The 7 InsurTech Trends That Matter for 2021

The COVID-19 pandemic has triggered structural changes that have forced insurance players to become more competitive than ever. The pandemic has proved to be a catalyst, nudging insurers to prioritize their focus on improving customer centricity, market agility, and business resilience.

As per a report by Accenture, almost 86% of insurers believe that they must innovate at an increasingly rapid pace to retain a competitive edge.

‘Insurtech’, short for ‘insurance technology’, is a term being widely used these days to talk about the new technologies bringing innovation in the insurance industry. The digital disruption caused by technology is transforming the way we protect ourselves financially.

In this article, let’s explore the top insurtech trends for 2021 that will pave the way for the future of insurance. 

  1. Data-backed personalization

Insurance companies are increasingly drifting towards collecting data to understand customer preferences better. Using data collected from IoT devices and smartphones, insurance companies are trying to deliver customized advice, the right products, and tailored pricing. 

Personalization enables exceptional experiences for customers while offering them products and services tailored to their specific needs. The idea is thus to put customers at the core of their operations.

Some examples of data-backed personalization include the following –

  • Reaching out to customers at the right time. This involves pitching to customers when they are thinking of buying insurance like while making high-value purchases, during financial planning, or during important life events.
  • Reaching out to customers through the right channel. This involves reaching out to customers through appropriate platforms like a website or mobile app.
  • Delivering the right products to specific individuals. This involves delivering products to customers based on their specific needs like reaching out with auto insurance to a customer who travels often.

Take the example of the financial services company United Services Automobile Association. The organization collects data from various social media platforms and uses advanced analytics to personalize its engagement with customers. The company advises customers when they are buying automotive insurance or are looking to purchase a vehicle. The company also provides its customers tailored mobile tools to help them manage and plan their finances.

  1. Usage-based policies

One of the biggest trends in the insurance industry is the growth of usage-based policies. In the coming year, we are going to hear a lot more about the ever-growing popularity of short and very-short term insurance that needs to be activated quickly.

We are going to see the rise of dedicated apps that allow easily activating policies based on usage needs. For instance, one would be able to take insurance for a sports event or a travel plan.

  1. Robotic and cognitive automation (R&CA)

Both robotic process automation (RPA) and cognitive automation (CA) represent two ends of the intelligent automation spectrum. At one end of the spectrum, there is RPA that uses easily programmable software bots to perform basic tasks. At the other end, we have cognitive automation that is capable of mimicking human thought and action. 

While RPA is the first step in the automation journey for any industry, cognitive automation is expected to help the industry adopt a more customer-centric approach by leveraging different algorithms and technologies (like NLP, text analytics, data mining, machine learning, etc.) to bring intelligence to information-intensive processes. R&CA, therefore, encompasses a potent mix of automated skills, primarily RPA and CA.

In the insurance industry, there are vast opportunities for R&CA to ease many processes. Some of its use cases in the insurance industry include –

  • Claims processing – R&CA can help insurance companies gather data from various sources and use it in centralized documents to quickly process claims. Automated claims processing can reduce manual work by almost 80% and significantly improve accuracy.
  • Policy management operations – R&CA can help automate insurance policy issuance, thus reducing the amount of time and manual work required for it. It can also help in making policy updates by using machine learning to extract inbound changes from policy holders from emails, voice transcripts, faxes, or other sources.
  • Data entry – It can be used for replacing the manual data entry jobs, hence saving a significant chunk of time. There are still many instances where data like quotations, insurance claims, etc. is entered manually into the system.
  • Regulatory compliance – R&CA can be key in helping companies improve regulatory compliance by eliminating the need for human personnel to go through many manual operations that can be prone to errors. It helps reduce the risks of compliance breach and ensures the accuracy of data. Some examples of manual work that R&CA can automate include name screening, compliance checking, client research, customer data validation, and regulatory reports generation, etc.
  • Underwriting – It involves gathering and analyzing information from multiple sources to determine and avoid risks associated with a policy like health, finance, duplicate policies, credit worthiness, etc. R&CA can automate the entire process and significantly speed up functions like data collection, loss assessment, and data pre-population, etc.
  1. Data-driven insurance

Although insurance has always been driven by data, new technology means that insurers are likely to benefit from big data. Using valuable data insights companies can customize insurance policies, minimize risks, and improve the accuracy of their calculations.

Here are a few use cases of how insurance companies use big data – 

  • Shaping policyholder behavior – IoT devices that monitor household risk help insurers shape the behavior of policyholders.
  • Gaining insights on customer healthcare – Medical insurance companies are drawing insights from big data to improve recommendations in terms of immediate and preventive care.
  • Pricing – Companies are using big data to accurately price each policyholder by comparing user behavior with a larger pool of data.
  1. Gamification

Gamification is turning out to be a very interesting and promising strategy that may get a lot more popular in 2021. It involves improving the digital customer experience by applying typical dynamics of gaming like obtaining prizes, bonuses, clearing levels, etc.

Gamification has shown promise in increasing engagement and building customer loyalty. For example, an Italian insurance company was able to observe a 57% increase in customers (joining the loyalty program) due to a digital game created by the company.

  1. Smart contracts

Smart contracts are lines of code that are stored on a blockchain. These are types of contracts that are capable of executing or enforcing themselves when certain predetermined conditions are met.

The market for smart contracts is expected to reach a valuation of $300 million by the end of 2023.

The insurance sector can benefit from smart contracts because these can emulate traditional legal documents while offering improved security and transparency. Moreover, these contracts are automated, so companies do not need to spend time processing paperwork or correcting errors in written documents.

  1. Other key trends

Some other key trends that may be relevant in 2021 include – 

  • Extended reality – Although it’s still in its early days, extended reality can benefit the insurance industry by making data gathering much safer, simpler, and faster by allowing risk assessment using 3D imaging.
  • Cybersecurity – Since insurance companies are migrating towards digital channels, they also become prone to cyberattacks. That is why cybersecurity will remain a trend in 2021 as well.
  • Cloud computing – The year 2021 could witness cloud computing become more essential than ever before. 
  • Self-service – It allows customers to have an alternative path to traditional agents as per their need and convenience, and thus looks to pick up pace in 2021.

Conclusion

It can be concluded that the pandemic has accelerated the shift towards digital in the insurance industry. As for the trends for 2021, there seems to be a general inclination towards personalization, data mining, and automation in the industry.

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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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