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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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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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