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InsurTalks Podcast with KB Srinivas: The Road to Recovery for Indian Insurance Beyond Covid-19

8 minutes, 40 seconds read

It was hard to predict the extent of damage that the COVID-19 pandemic might cause to the whole world. As several economies across continents came spiraling down, various industries have been badly hit. Sectors such as healthcare, travel, hospitality, etc. have incurred huge losses. This has put a lot of pressure on the Insurance sector in terms of claims processing, operations, customer support, etc. To go one step deeper and get a sense of how Insurance and InsureTech industries are coping with this crisis, we interviewed Mr. KB Vijaya Srinivas — the insurance industry veteran and former Chairman cum Managing Director of United India Insurance Company Ltd.

Mr. KB Vijaya Srinivas is a renowned insurance industry expert with vast experience spanning around four decades. Mr. Srinivas retired after holding Joint Additional charge of United India Insurance Company Ltd. as Chairman cum Managing Director. He joined United India Insurance in 1981 as a direct recruit officer and has served in various metropolitan cities, in various capacities encompassing almost all the different aspects of the organization. 

Mr. Srinivas served National Insurance Company as a General Manager. He later became the Chief Marketing Officer and held charge of many other portfolios like foreign operations, crop insurance, rural insurance, publicity, CSR, estate, administration, to name some. 

He has been a recipient of awards on many occasions from the Insurance Institute of India for essays on various topics. Now, he is a freelance writer, trainer, consultant, and also director of MDIndia Networx and MDIndia TPA. 

Excerpt from the interview- 

Navigating the New Normal in Insurance

How is the Indian Insurance Industry addressing the impact of the COVID-19 pandemic? How are Insurers navigating the ‘new normal’?

Mr. Srinivas: Insurance follows the economy. If the economy goes up, insurance also does well and vice-versa. Due to the lockdowns, businesses across industries are not doing well. This has led to a decline in the demand for insurance. Due to a lack of adequate income, people are delaying buying insurance or being selective in their insurance requirements. 

However, on the brighter side, due to the pandemic, more people are looking towards buying health insurance. When one enters in one type of insurance, then there’s a tendency to explore other lines of insurance products as well.

There’s a possibility of introducing a new line of insurance such as Pandemic insurance. Pandemic insurance is available internationally but not in India. Following the recent example of Wimbledon, where they bought pandemic insurance in 2003 during the SARS outbreak, they now got a compensation close to 140 million euros due to this year’s cancellation of the event.

[Related: The Biggest Insurance Payouts in History]

In India, IRDAI and the General Insurance Council took some proactive steps. The General Insurance Council came out with some guidelines such as waving of the clause in Fire Insurance during the lockdown which states that if a factory is closed without notice for more than 30 days, the insurance cover will cease to exist. Spot Survey in accident policy has become lenient — such as survey through photos instead of in-person surveys at the accident spot is allowed. IRDAI has also relaxed the payment of premiums and claims processing of COVID cases within 2 hours. 

Processing of COVID-19 based claims

With the IRDAI placing special instructions for COVID-19 based claims, such as authorization of cashless requests within two hours, and processing of claims within shorter time frames — Insurers are trying to settle claims as quickly and without hassle as possible — Has this put undue burden on ‘claims processing systems’?

Mr. Srinivas: Health insurance incidentally constitutes a significant number of claims and amount of premiums in the insurance industry. Major portion of the claims process has already been digitized. This will make things faster especially for COVID patients. I believe that claims processing in the current times has not put a huge strain on health insurance. Certainly, due to lockdowns and remote working, things will be slightly delayed but once the lockdowns are lifted, they’ll be able to serve their customers better.

COVID-19: An impetus to going Digital?

Is the ongoing pandemic, going to be the impetus for insurance to move completely online (digital)?

Mr. Srinivas:  Definitely, this pandemic will give a special thrust to insurance towards going online. Moreover, customers will prefer online transactions rather than standing in line and submitting physical documents. IT and Telecommunication companies should come up with technologies that can make these digital processes seamless.

[Related: The role of AI in enhancing claims experience for Insurance customers]

Protecting the Demand side during the Lockdown

Given that physical selling (via agents) has been severely affected by lockdown restrictions and social distancing norms, we are starting to hear that the Insurance advisory is now turning to digital to stay in touch with their customers — What steps do Insurers need to take to build and protect the demand side?

Mr. Srinivas: For many years now, insurance has been a product that is sold and not voluntarily bought. There has certainly been a thrust to digital platforms for sales and marketing however, agents still contribute to a major chunk of the premiums. All companies have online agent portals through which they operate. 

A survey shows that around 75% of people still trust agents for buying a policy. They’ll be a dominating factor for many years to come. Collecting premiums and making payments have already been happening online, and it will accelerate in the future. Agents will also rely on online platforms for sales and marketing. Due to social distancing, in-person sales will drop down but agents will certainly protect their customer’s interests through other channels.

How can Insurers equip their agents to generate sales in this pandemic?

Mr. Srinivas: There’s a tremendous amount of work to be done in this area. Insurance agents are very diverse. Some are from rural areas with no knowledge about insurance and some are on the other end of the spectrum such as highly qualified Chartered Accountants. 

Today, a good number of agents still survive on the traditional line of insurances such as Motor or Health. First, insurance companies should educate their agents on the types of insurance available. Second, they should train their agents on how to sell the insurance products. Motor insurance is easy to sell as it is a standardized product but event cancellation insurance is hard to sell.

Insurance for Rural India

“Taking Insurance to Bharat” was a main theme entering into 2020. Going forward, what can Insurers do to reach Rural India?

Mr. Srinivas: In 1972, insurance was nationalized. Before which it was in the private sector and very urban-centered. The objective behind nationalizing the insurance sector was to take insurance to the deepest parts of India. There were so many districts where not a single person had insurance. So we had to open offices in these rural areas. It was a tough job but we opened offices in those districts. 

The second initiative was starting a single-person office to take insurance further into the deep pockets of rural India. In 2013-14, the government brought in new guidelines where offices were opened depending on the population in the area. Many less populated areas did not have offices. Offices were opened in those areas. 

Digitization has taken insurance one step further. New marketing channels have opened. In remote locations, along with agents, Point-of-Sales persons sell insurance. From 10th pass person to small shop owners, anyone can become a Point-of-Sales person. 

Schemes such as Pradhan Mantri Suraksha Bima Yojna which is an accident policy has more than 15 cr people under it. Ayushman Bharat covers around 10 cr. families (around 50 cr.) people across the country. Pradhan Mantri Jeevan Jyoti Yojna (a life insurance policy) and Pradhan Mantri Fasal Bima Yojna (crop insurance) have connected India to Bharat in a very big way. Perhaps it has not been projected in a constructive way however they have made significant progress. 

On-set of COVID-19, many states have said Ayushman Bharat which was previously limited to BPL strata (Below Poverty Line) is now providing coverage for the entire state. Things are happening silently but effectively.

Product Innovation

Before the ongoing crisis, the IRDAI had recently set up sandboxes for experimenting with new innovative motor and health products. What kind of new insurance products are being/need to be built during the Pandemic crisis?

Mr. Srinivas: Pandemic insurance is available internationally but not many have taken it. In India, this policy can be easily built and will have wider acceptance. For example, the Business Interruption policy states that claims can be filed for loss due to fire or breakdown of machinery ultimately leading to loss of revenue or profit. This policy was earlier called the Loss of Profit. Pandemics are not covered under this policy. 

There has been a lot of debate over this topic internationally as well whether claims should be processed or not. Time has come to develop a policy that includes business interruption due to pandemics causing loss of revenue. Limited travel due to the pandemic crisis has affected the various industries such as airline, shipping, hospitality, etc. There is no insurance cover for the losses incurred by these businesses. Spoilage of goods due to deterioration during the lockdowns, non-use of vehicle and machinery also cause a huge loss. It’s time to have policies or clauses in place for losses due to these reasons. 

Role of AI in Operational Continuity

Will AI have a role to play for Insurers trying to maintain operational continuity during this COVID-19 Pandemic? 

Mr. Srinivas: Most definitely. In 2015, during Pradhan Mantri Fasal Bima Yojna there was a challenge of handling huge volumes of data for which we had to make use of technology like satellite imagery and drones to assess the loss for claims processing. AI will have a larger role to play in insurance for its success. 

[Related: Embrace the New Normal | Business Continuity Solutions]

Closing Note

This exciting interaction gave a deep insight into the history of insurance in India and provided some useful insights on paving the way forward in the New Normal. Mr. KB Vijaya Srinivas also highlighted the importance of InsurTechs in the New normal and spoke about new insurance lines and policy coverages being introduced as a result of this pandemic.

AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, 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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