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Key Takeaways of 4th Insurance India Summit & Awards 2019

Innovation and Disruption are causing a paradigm shift in the Indian insurance industry today.

The industry is expected to touch USD 280B by 2020. With the advent of InsurTech, Blockchain, Big Data, AI, IoT, AR amidst changing consumer preferences — there has been a holistic approach to insurance automation, challenging the traditional concepts making insurance a battleground of the old and the new.

The insurance penetration in India is only 3.7% as a percentage of GDP compared to the World average of 7%. However, changes in the demographics, technology and business models have opened up a plethora of opportunities for the Indian insurance industry which is growing at a rate of 11% annually. This has marked the beginning of breaking out of an emerging state into broader impact and use, enabling insurers to expand into more ecosystems than ever before.

The recently concluded “4th Annual Insurance India Summit & Awards 2019” with the motto of “Integrating Technology & Big Data to Enhance Distribution Channel, Marketing Strategy & Customer Experience” — aimed at having robust and key focused area discussions on the inherent insurance challenges. IISA creates a platform for one of India’s largest gathering of Insurance leaders and Innovators. 

Let’s have a look at the key takeaways of the 4th Insurance India Summit and Awards 2019.

Key takeaways of 4th Annual Insurance Summit and Awards 2019

PHYGITAL is the New Wave in Insurance  

There is still a trust deficit between the customers and insurance companies, primarily due to highly suspect products with unrealistic returns being sold in the past decade. Customer Expectations are very different online and offline for the same customer. 

In such a moment of crisis, the focus on Digital cannot be limited to just customer acquisition, as Customer engagement is the key

Phygital, i.e Physical + Digital, is the concept that brands and businesses are using as a sales strategy to amplify the yield. Phygital as a paradigm is challenging the cascaded approach of traditional insurance and bridges the gap between both the worlds effortlessly.

With the help of data visualization, one can help increase customer interactivity, analyze product performance, understand data consumption objectives and thereby improve customer experience. The objective is to provide the ultimate 360-degree experience. This includes a focus on relationships, lifecycle, and even life stages.

Click to know more on, ‘Scope of Phygital in Insurance‘.

The New Product is About Customer Journey:

Customer Expectations have changed significantly over a short period of time. The forecasted move to real-time interaction is indeed here. 

Source: SMA white paper

Customer journeys in insurance are often complex. It involves multifaceted relationships, multiple locations, and various insurance needs. Due to these complexities, 70% of Indians working in rural areas generate 40% of India’s income but have much lower access to the products and services.

Insurance companies are looking at creating efficiency across the Value Chain. Thus they are now also looking at creating or leveraging existing eco-systems e.g. E-Commerce, to widen the footprint. Instead of the focus being on removing agents and selling directly, Insurance companies are now focused on empowering agents.

According to recent SMA research, 85% of insurers report that customer experience and engagement is a top strategic initiative, ranking it as #1 – a significant shift from #4 and #5 in past years. This is good news for the industry, as it points to determination and focuses to place the customer first.

Cognitive RPA to Ease Insurance Problems:

Data is a vital ingredient for going Cognitive. The cognitive insurance business is the one that allows underwriters to be equipped with a repertoire of AI-enabled tools, empowering them to make better and more informed decisions about their customer.

RPA tools currently occupy the Peak of Inflated Expectations in the Gartner Hype Cycle for Artificial Intelligence, 2018. 

Cognitive RPA is widely adopted in various industries, insurance included. “End-user organizations adopt RPA technology as a quick and easy fix to automate manual tasks,” said Cathy Tornbohm, vice president at Gartner. In the insurance industry automation of the day-to-day tasks would potentially reduce cost, time consumption and increase accuracy, quality, and competency.

Miniaturizing of Insurance — Microinsurance

Insurance coverages are the greatest aid against the consequences of risk exposures and also provides support for the insured’s credits. However,  65% of Indians below the age of 35 don’t want to buy Health Insurance

In order to provide “insurance for all”, the Insurance Regulatory and Development Authority of India (IRDAI) has a specialized category of insurance policies called micro insurances. It promotes bite-sized insurance coverage among Gen-Y and the economically vulnerable sections of society.

Click here to know if ‘ Microinsurance actually works for the economically vulnerable sections of India.

Micro-insurances are easily affordable over the bulky insurance schemes. Recently MaxBupa, a standalone health insurer partnered with Mobikwik, a fin-tech platform to promote affordable and convenient microinsurance products. Priced at an annual premium of ₹135, their product, HospiCash will offer ₹500 per day hospital allowance for up to 30 days in a year. 

Click to know more about how ‘ AI can help bridge customer gaps for microinsurers


The non-partisan agenda of the Summit was to explore challenges and their deterrents like technology integration in insurance, customer engagement, and customer experience. The discussions were designed to draw out clear outcomes for the industry together – in order to realize growth, customer satisfaction, profitability and deliver definitive business value.

Mantra Labs was proud to be the business development partner at the successful Summit. We were honored to partake in the insightful conversations and gather appreciation for presenting ‘FlowMagic’ – our Visual AI Platform for Insurers, from all the insurance industry experts present.

We hope to see you again, in the next edition!

To know us in person, drop us a Hi 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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