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Impact of COVID-19 on Motor Insurance & Practical Challenges for Insurers

5 minutes, 36 seconds read

The outbreak of COVID-19 shook the very foundation of many industries. It is probably the first time that a pandemic created a dent in the world economy. Statista estimates that COVID-19 will bring down the global real GDP growth by 0.5 percent in 2020 compared to the previous year.

Consumers have become conscious of their expenditure. Due to disruptions in supply chains, many small and medium businesses have suffered huge losses. A dip in international trade has created a ripple effect across all industries including travel, hospitality, insurance, and manufacturing. 

The pandemic has different effects on the life and non-life segments of Insurance. While the rising concern for health has led to a spike in life and health insurance demands, the general insurance sector is suffering a setback due to restrained expenditure. 

Motor insurance is no different from being severely hit by the pandemic. Amidst this crisis, people are not keen on purchasing cars, bikes, which is directly affecting the insurance sector as well. Re-negotiation on premiums is another big challenge for Insurers. Let’s delve deeper into the impact of Covid-19 on motor insurance and practical challenges for Insurers.

The Real Picture

Till a cure is available in the market, there will be travel restrictions to a certain extent. People will hesitate to commute daily and avoid long-distance travel. The significant drop in the usage of motor vehicles is impacting claims and sales differently.

Claims and Premium 

In the initial lockdown period, many people were not able to drive their vehicles. The domino effect of this was a reduced number of motor insurance claims. 

At first, it sounds profitable for Insurers. But, for policyholders, continued premiums on policies they can’t use seems an additional burden. So most customers are either asking for bailouts or reduced premiums or refunds on premiums. 

Some major Motor insurance companies in the US and UK have already refunded 10-15% of annual premiums back to customers. In India, the finance ministry has extended the validity of the third-party insurance policies which were up for renewal during the lockdown.

Sales 

Moody’s Investors Service, expects a 20% drop in global auto unit sales as compared to its earlier projection of 14%. In many countries, Motor Insurance is compulsory. However, if people won’t use vehicles, there’ll be a significant dip in the requirement for Motor Insurance policies. 

In the wake of the current situation, IRDAI decided to withdraw its earlier policy of long-term third party vehicle insurance coverage from August 1, 2020. Earlier, the third party insurance was mandatory (three years for new cars and five-year policies for two-wheelers). 

The IRDAI’s decision is a result of concerns over the implementation of a long-term insurance cover package which made buying new vehicles an expensive affair. This will reduce the price of vehicles, which, in turn, will boost the automobile and motor insurance sectors.

Prevailing Challenges for Motor Insurance Companies

Motor Claims Process

Vehicles can still suffer damage due to theft, natural calamities, non-usage, etc. Moreover, once people start traveling, accidents are prone to occur. It will be difficult for claims investigators to assess the damage through an in-person visit.

Some insurance companies are accepting claims and renewing premiums through online inspection and vehicle photograph assessment. This procedure, however, is still in a nascent stage. Despite high-resolution cameras, it is possible to overlook a dent due to deflection caused by sunlight. 

[Related: How Machine Vision can Revolutionize Motor Insurance]

Sales and Marketing

Even though automobile sales dropped in the short-term, it is expected to pick-up in the early quarter of 2021. 

On one hand, marketing & selling policies at the original price will be difficult for motor insurers, and on the other hand, people will avoid public transport and prefer personal vehicles for commuting. 

Insurers, thus, have a challenge for positioning their product that suits both — customer requirements and their profit margins amidst fierce competition with InsurTechs.

Policy Changes due to Volatile Consumer Behaviour 

Since there were no clauses or policies for the pandemic in place earlier, some immediate mitigation measures had to be taken such as refunds on premiums to safeguard customers’ interests. 

Going forward, till there is a conclusive solution to this crisis it will be difficult for Insurers to formulate policies that preserve both – their and customers’ interests.

Business Continuity

With lockdowns, major workforce resorted to working-from-home. In the beginning, some companies faced issues in making sure whether their employees had the means to work remotely. 

Even though the lockdowns have been eased a bit and the workforce is getting used to collaborating online, the situation is here to stay. Smooth operations with a major part of the workforce working remotely is still a challenge, especially for call-centers, surveyors, and field investigators. 

[Related: Business Continuity for Call-Center Operations: Case Study]

Lack of Historical Data

During the SARS and Ebola outbreaks, only some countries like Singapore, Thailand, China, the African continent were affected. To a certain extent, businesses were cognizant of the effects which COVID-19 would have on their businesses. 

Therefore, insurers had come out with new policies and clauses on pandemics. However, the outbreak of a pandemic of this scale where the entire world felt the effects had not happened earlier. Lack of historical data for motor insurance is making it difficult to come up with mitigation strategies and business models for a sustainable and profitable business. 

Mitigation Measures and The Way Forward

“Claims” is one of the most important aspects of motor insurance and will now witness automation more than ever. Coupling Machine Vision technology with panoramic/360° pictures can give insurers a holistic view of the extent of the damage.

Car rental services have an extensive guide to click pictures of the car rented before driving which makes the process very tedious. This can be simplified through apps having pre-shot pictures of the car before renting it out. AI can also help assess the accuracy of the images. 

[Related: How can Artificial Intelligence settle Insurance Claims in five minutes?]

In the short run, finance ministries in many countries have taken steps to lessen the burden of the insurance premiums. But in the long run, insurers will have to come up with policies that are more viable for the insurance buyers. ‘Pay-as-you-use’ policies will see more demand because of their small ticket size. 

Technologies such as IoT can help gather data through sensors that could help underwrite insurance premiums for vehicles. The data gathered can help understand consumer behavior and profile them for creating future strategies. 

We’re an InsurTech100 firm, building AI-First Solutions for the new age Digital Insurer across the entire Insurance Lifecycle. For your specific requirements and Machine Vision for motor claims, please feel free to write to us at hello@mantralabsglobal.com.

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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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