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5 InsurTech Trends for 2023

3 minutes read

For 2023, we believe that InsurTech will be used to supplement the rising concerns of inflation, arrested economic development, and heavily burdened pension schemes by catering to customers with greater attention to detail. 

# Digitally Enabled CX 

Insurance models in the present context have become bloated and complicated to the point where customers feel alienated. Customer needs are also converging across a wide range of areas: health, retirement, and investment management, to name a few. Simplifying the existing delivery model is key, and one such model that is likely to emerge is that of being a ‘distribution specialist’.

These firms are predominantly client-centric and extremely capital-light as they do not take on balance sheet risks. These firms will invest heavily in client-facing technology, and those that curate a delectable insurance discovery and delivery experience will have a huge leg-up over their peers. These developments are in line with Gartner’s predictions for the InsurTech industry, where digitally enabled CX is listed as a key success factor for InsurTech in the coming years.

# InsurTech native Telematics

Analysts and experts alike have been citing usage-based insurance programs as the next big thing in the world of insurance for nearly two years now. But how effective can usage-based programs be if they rely entirely on the customer to predict their decisions and make purchases accordingly? 

This is where telematics systems come in. As cars become increasingly ‘smart’, it will become easier and cheaper to integrate telematics into the insurance plan to implement a real-time ‘pay as you go’ plan. Telematics will be crucial for developing markets in Asia as societies become increasingly digitized and people start to get comfortable with the idea of insuring themselves and their vehicles separately. 

# Algorithmic Risk Assessments

Research has shown that with the application of machine learning models to the risk assessment strategies employed by risk analysts, Insurance companies can decrease the time taken to evaluate customer profiles by allowing faster servicing and thereby leading to greater customer loyalty and satisfaction. This will allow companies to process claims swiftly and accurately, thereby allowing risk assessment professionals to focus on refining their models.

Some firms have already demonstrated success by incorporating AI into their workflows. Lemonade, an insurance company that is ‘digital first’ has seen massive success by using AI to facilitate claims, quotes, and personalizing prices and interactions with individual customers.

# Broadening capabilities in the Metaverse

With over $25Bn dollars having been invested into it by Facebook alone, Metaverse is here to stay for the long run. And for Insurers, the possibilities offered by metaverse are hard to ignore. This means they finally have a tool to combine the efficiencies of AI-powered chatbots, with the warmth of face-to-face interactions. Internal training, conducting sales pitches, and using NFTs to verify personal documents are some of the most highly anticipated use cases.

Max Life insurance, a leading Indian insurance player has already started to think about how best to use the metaverse to boost employee engagement and morale.

# Disruptors will strive to stay afloat

Much of what made new-age insurers attractive to customers was the way they structured themselves (tech-first, expedited claims, etc.) that were antithetical to running an insurance business at scale. Kimberly Harris-Ferrante of Gartner predicts that the coming year will see a lot of new Insurtechs pivot to more traditional operating models, with the successful ones being acquired and the others being forced to shut shop.

Some have already closed down, such as GoBear (Asia Pacific) citing increasing regulatory and compliance pressures as the primary reason. Other examples include Kinsu (from Latin America) and Coverly for small businesses.

Conclusion: 

2023 is likely to see the beginning of the final stretch of digital transformation in the insurance industry as many have already caught on to the basics that are required to run a robust digitally-enabled sales and servicing operation. Conservatism will go hand-in-hand with novel, disruptive technologies as incumbents will lap up all existing software capabilities to bolster direct distribution, simpler delivery mechanisms, and a narrower focus on servicing the customer. Expect greater use of APIs, hybrid cloud architectures, and ‘headless tech’ in the coming year.

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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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