Upcoming Trends in Global Insurance Market

Ever since the Pandemic hit, the Insurance industry is upgrading at a fast pace. The main focus hovers on pandemic rehab and customer experience by developing data-driven ecosystems and hyper-personalization models.

According to a Gartner research, the long-term spending for insurance is forecast to grow at a CAGR of 7.5% to $311.8 billion in 2025 driven by IT services and software growing at a CAGR of 9.2% and 12.3%, respectively. These increased investments in data, AI, and digital twin technologies resulted in the emergence of a new generation of business and intelligence in the insurance industry.

But there have been several obstacles that the insurance business has faced.

The challenges of the current or traditional insurance industry

Shortage of speed to deliver new services into the market: Because insurance businesses’ digital implementation timescales are lengthier, customers may feel their insurer is slow and unable to cater to their demands.

High IT run time expense before migrating to digitally improved systems: When trying to get rid of legacy systems and introduce new sales methods that are incompatible with their current legacy technology, becomes substantially more expensive and time-consuming.

Interpreting a considerable volume of client data: With the vast amount of data available for customers and employees in one system, there comes the challenge of ensuring the shared information is comprehensive and accurate. Any discrepancy in handling or interpretation of data may lead to the approval of the incorrect type of insurance claim, causing further delays for clients.

5 upcoming trends in insurance to look forward to:

Let’s take a look at how these insurance trends are transforming and automating core business procedures, improving claims processing, and providing better insurance products.

Low-code

Professionals and non-professionals alike can use modern low-code platforms to create software tailored to their organizations’ specific needs.

By replacing or lowering the need to write code with a graphical interface, low/no-code platforms democratize and speed up the software development process. Insurers may now deploy digital applications with little or no computer programming, allowing them to quickly react to changing conditions, thanks to the growth of low-code and no-code platforms.

Using low-code platforms, insurance companies can increase their operational efficiency by removing the unfavorable consequences of skill gaps among their staff.

Gartner estimated that low-code platforms will make up 65% of application development activity by 2024.

Some of the well-known Low-code platforms are Zoho Creator, Salesforce Lightning, Mendix, Appian, Microsoft PowerApps, and Google App Maker, which are making the code development process faster and reducing the complexity of the application development process.

Conversational AI

According to a Mantra Labs report, 64% of insurers plan to allow chatbots to do increasingly advanced customer-facing tasks in the next five years.

Many of these employee assistance queries may be automatically fielded and resolved by conversational AI platforms, minimizing the need for human engagement and saving enterprises significant time and money.

Insurance chatbots enabled by advanced conversational AI might deliver omnichannel, round-the-clock, and multilingual support, to name a few obvious advantages. They can also help you create one-of-a-kind, high-quality client experiences. Chatbots can also be used to detect and track fraud signs, informing the insurer as well as the customer.

Smart contracts: Blockchain technology in insurance

According to Verified Market Research, the Smart Contracts Market was worth USD 144.95 million in 2020 and is predicted to reach USD 770.52 million by 2028, growing at a CAGR of 24.55 percent from 2021 to 2028.

In the past, uncontested claims may take months to process, but thanks to Blockchain and smart contracts, insurers can now automate the execution of insurance products agreements without the use of mediators, making them more transparent and less manipulable. The insurer’s administrative costs are decreased when claim processing speeds up. As a result, companies may reduce rates, increasing market share.

Neither party can lose information regarding the arrangement. Both the insurer and the insured cannot lose since smart contracts are traceable and irrevocable.

There are several Blockchain use cases in insurance, which you can read here: https://www.mantralabsglobal.com/blog/blockchain-use-cases-in-insurance-industry/

Extended reality (XR) insurance technology

According to an Accenture study, 85% of insurance executives agree that it’s critical to use XR insurance technology to bridge the physical distance gap between personnel and customers.

Some insurers are employing XR technology to improve and enhance certain portions of their business, including training customer service representatives on how to communicate with customers and guide them through the purchasing process using virtual customers. To hunt for risks in constructions, underwriters utilize on-site pictures and other images to create XR simulations. Using augmented imagery, insurers may engage and connect with their consumers remotely.

National Roads and Motorists’ Association Insurance in Australia and Liberty Mutual Insurance in the United States are using AR and VR technologies for car crashes and breakdown simulations. Zurich Insurance is using the same technology to improve staff training, and AXA Insurance uses VR for advertising.

Drones and Robotic insurance technology

IMARC Group expects the market to reach US$ 43.4 Billion by 2027, exhibiting a CAGR of 12.56% from 2022 to 2027.

Drones and robotics are currently being used by many insurers in their risk management and claims management techniques. Drones are a low-cost way to collect data, conduct surveys, and design mitigation plans. The system allows for more proactive and predictive fraud detection and reaction.

Robotics are being employed in their claims management operations to help forecast the result of a claim and recommend the best strategy based on that prediction (for example, recommending an early settlement on cases where the data suggests a high potential for long-term litigation). Robotics may even aid in the detection of discrepancies between internal policy terms and those offered by brokers. When a policy is originally issued, this allows insurers to spot plans that may result in future losses.

According to a report by McKinsey, programmable, autonomous drones; autonomous farming equipment; and enhanced surgical robots will all be commercially viable in the next decade.

Reasons behind insurance tech trends’ massive adoption

The majority of human workers can be removed from warehouse operations with AI-enabled infrastructure, changing the nature and purpose of workers’ compensation coverage. Wearables and artificial intelligence (AI) are transforming the way insurers use data to produce predictive insights and inform a variety of interactions with policyholders by providing real-time feedback on the impact of physical activity on personal wellness.

Many insurers are still updating their technology stacks and are at the beginning of their digitalization journey, making them vulnerable to being surpassed by more agile competitors.

Conclusion

According to a PwC survey, 65% of insurance agencies believe that AI investments in customer experience (CX) have lived up to expectations. 49% believe that improvements in internal decision-making have likewise met expectations, and 45% say the same about innovation in products and services.

While these technologies possess great opportunities for insurers, many are struggling to adapt. In fact, 53% of carriers struggle to understand blockchain and its use cases, 43% have other insurance technology taking priority, and 38% are concerned with its data security.

All of this emphasizes the significance of modernizing business operations by investing in training and implementation methodologies. This not only speeds up digital transformation but also improves organizational change readiness.

Other technology trends such as Automated Underwriting, Machine Learning, Cloud Computing, Telematics, Predictive Analytics for Competitive Benchmarking and Modeling, Open APIs, Proactive Risk Management, Embedded Insurance, and Machine Vision are also being researched as well as utilized aggressively to find their applications in the insurance market.

As a result of the convergence of these technological trends, insurers will be able to cover individuals in a more dynamic and responsive manner.

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.

Upcoming Trends in Global Insurance Market

The challenges of the current or traditional insurance industry