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Here’s how Insurtechs are evolving India’s Insurance landscape during the Pandemic

7 minutes read

The COVID-19 pandemic and subsequent global lockdowns triggered plenty of structural changes that forced insurance companies to enter the arena with their eyes on the prize. The pandemic year thus proved to be a catalyst, in turn, nudging insurers to shift their focus and prioritize customer experience, market agility, and business resilience. 

According to BCG, “Globally, insurtechs raised $7.5 billion last year, as COVID-19 accelerated the need for digital transformation in insurance.” 

Investor funding in insurtech came to $5 billion in the first quarter of 2021 with 261 deals, according to Forrester’s “Insurtech funding roundup, Q1 2021” report. 

How has the pandemic impacted Insurtechs in India 

India is the second-largest insurtech (insurance technology) market in the Asia-Pacific region, accounting for 35% of the $3.66 billion of venture capital coming into the sector, according to S&P Global Market Intelligence data.

“Insurance technology investors are attracted to India since it is one of the fastest-growing insurance markets in the world,” said the report. 

Insurance premiums in India have been reported to have totalled $107 billion in India until March 31, 2020, growing at a compounded annual growth rate (CAGR) of 10% from FY15 to FY20. 

“While big techs are vying to become digital intermediaries in the insurance space, established carriers are building proprietary digital channels. Startups that assist both incumbents and big techs in making this transition will likely emerge as winners,” the S&P report continued.

“Partnerships between large insurers and insurtechs have the potential to enable more personalized online distribution, predictive underwriting, and more efficient claims management,” said Alpesh Shah, managing director and senior partner, Boston Consulting Group while speaking with the business daily, Mint. 

Read: How Insurtech is Reshaping the Future of Insurance

The fast-growing industry is introducing solutions for AI-based underwriting, virtual claim filing, among others. The next big revolution could come in the form of blockchain contracts, where customers might not need to file a claim. Bajaj Allianz General Insurance has already introduced a travel insurance product that uses blockchain to settle claims on flight delays automatically.

In another scenario, Acko General Insurance tied up with over 20 digital platforms across retail, travel, finance, and others to distribute bite-sized insurance. Ola’s trip insurance by Acko has insured more than 23 million rides in less than 10 months and is being hailed as one of the most innovative insurance products in the industry.

Another Insurtech startup, Toffee Insurance, offers insurance against theft or damage to bicycles and accidental injuries related to a fitness activity or sport.

Image Courtesy: fintechnews.sg 

Speaking about the Insurtech evolution and their funding in India, BCG’s India Insurtech Landscape and Trends reports that, “Global funding in Insurtechs have grown from about $2 billion in 2016 to $6 billion in 2020. While Americas account for the largest share of funding (68 percent of funding in 2020), Asia has been the fastest-growing geography till 2019 (5-year CAGR of 60 percent). In India too, albeit with a smaller base, funding has seen an increase from a modest base of $11 million in 2016 to $287 million in 2020. The funding trend has continued with Turtlemint raising $30 million in November 2020 and Digit raising around $84 million at the start of 2021.” 

“APAC-based insurtechs attracted $1.4 billion—up 15% year-over-year from the previous year—driven by companies headquartered in China ($800 million) and India ($450 million). Representative examples are Medbanks, a medical database-services company offering oncology-related services, which brought in $305 million in Series E+ funding, and Policybazaar, a price-comparison portal that raised $130 million in Series E+ funding,” the report continued. 

Insurers vs. Insurtechs in the current ecosystem

Image Source: everis.com 

Claims in the digital age

Even before the COVID-19 pandemic struck, customers had already begun leading digital-centric lives that required insurers to rethink their MO and strategies. “With the demands and constraints of the pandemic, a technology-enabled service delivery with a digital claims process is non-negotiable and mission-critical. In the past, these needs may have gone unmet due to lack of technology solutions or an insurer’s inability to capitalize on technology, but the situation today is very different,” reports Deloitte. 

The COVID-19 pandemic affected Insurtech firms on various levels, impacting demand, claims, and loss patterns in a number of ways across product lines and operating models. 

Thus, arose a need to overhaul and reset the core value system and give way to a new growth engine led by customer retention and loyalty, both driven by customer interactions with insurers, specifically during the claims experience.

“Claims operations, which have been traditionally treated as outputs of a “reactive back office,” will have to become a powerful differentiator—innovative and uncompromising on customer service, with multifaceted talent and capable of driving strong results,” continues the Deloitte report. 

Digit, an India-based multi-line insurer, launched a new product that covers pre-and post-hospitalization expenses, road ambulance charges, and a second medical opinion regarding eight viral diseases, including COVID-19 and dengue. 

For Care Health Insurance, erstwhile Religare, Mantra Labs, the Bengaluru-based Insurtech firm deployed Hitee, a conversational chatbot to be the first-level customer support for existing and new customers. This led to higher New Business Conversions by a factor of 10X, and a significant drop in Customer Queries over Voice Support by 20%.     

Source: www.mindbowser.com 

The pandemic and its subsequent wave accelerated the shift towards going digital in the insurance industry. In 2021, there is an apparent inclination towards personalization, data mining, automation, and cloud computing in the Insurtech space.

Read the 7 key trends we’ve expected this year in Insurance here. 

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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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