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The 5 hidden problems for Insurtech

For Insurance giants, the marketplace is changing. For young insurtechs trying to displace these giants and keen on disrupting the landscape altogether; the next big market is becoming plain and obvious: Millenials and the generations that will follow them.

A new wave of AI-driven technologies is making subtle changes to the way young people are re-thinking the whole “Why do I need insurance again?” decision.

Millennials —  are most likely to purchase insurance through an app with a few taps on their smartphones — are driving less frequently than previous generations — thereby creating a market for lower cost, pay-per-mile auto insurance. 

Yet, despite the proclivity of this demographic to stay away from ownership (and, with that, the need for coverage), they do own assets that they want insured. Insurtech is well poised above all else, to satisfy their unique coverage needs.

A majority of the World’s insurance purchases are done physically (in-person), while only a small portion of sales comes from either the web or mobile – yes, even in 2019 and for the foreseeable future, that remains true.

The Hidden Problems of Insurtech


The ‘Insurtech’ model can be broken down into — those that operate at the broker-level, those that offer insurance services/products or product-level, and those that have a hybrid approach (such as peer-to-peer insurtech) that has an insurance product with a strongly linked brokerage aspect to it. Here is a look at the challenges that surround young companies operating in these models.

#1 Partnerships are stark & sparse


For existing incumbents, the advantage is obvious — seize on the hype created by insurtech upstarts, who are capturing previously untapped audiences towards new & innovative products. 

Also, read – Top Innovative Insurance Products of 2019

Large insurers will even venture into setting up their own start-ups; or invest in new technologies within their own business.  However, despite the mutual benefit-for-all reasoning behind partnerships, these are spread thin across most regions.

Without the support of a large insurer or two, insurtechs will find it hard to manage the unit economics of the policies they sell; which brings to question the sustainability of this model for scaling.

#2 Innovation beyond downstream distribution


Insurtechs that have either chosen not to partner/ not managed to attract the right partnership with large insurers — arguably face greater challenges. Most of the insurtech-startup funding pool has moved into distribution, and rightfully so.

Distribution has brought about long-awaited changes to delivering new products and customer experiences — aspects of the business that Insurance giants consistently struggle to produce in.

Insurance, however, has four fundamental units: the underwriting of insurance, claims servicing, regulatory overhead, and distribution (actual selling).

As these insurtechs grow, the looming question remains: how will they manage the other parts of insurance, if all the money has gone into refining one stream?. For example, are they sufficiently capable of handling claims and underwriting as the business scales? These questions are yet to be answered, and the models are yet to be proven.

#3 Frequent changes to the legal & regulatory framework


“Not all insurtech businesses qualify as insurance companies” since they depend on the type and extent of the services provided. A regulatory distinction is essential to separate them — without which a reliable guarantee cannot be given to customers in the event of a loss.

Legal and regulatory commitments change with region and country, hence insurtechs are typically unsuitable for covering potentially large losses. 

#4 Attitudes of the next generation


Younger generations are less likely than previous ones to pay heed to the importance of insurance. They simply do not see it as an important financial instrument. These challenges have plagued the industry for several decades, and insurtechs will have to assume this challenge for themselves as well. At its core, insurance is a hard product to sell, no matter how good the package looks.

Technology in insurance and advancements to customer experiences are making the furthest inroads, the industry has ever seen. Yet, low insurance penetration levels are still an indicator of how difficult it is for insurtechs to find adoption among the masses.

#5 Intelligent Customer-Experiences


Thanks to Big Tech (like Google, Amazon, Apple, etc.) — customer experience has evolved rapidly. Digital products and services are now highly customisable and can be delivered at a high quality consistently. Yet, it has taken until now for the same to slowly seep into insurance. Sensing a huge opportunity, Big Tech has started moving into the insurance on-demand space, which has forced the larger insurers to adapt quickly. 

Insurtechs, who are by-default product- and tech- first, tend to fare better than their much larger counterparts. Yet challenges with data will persist. Just how well insurtechs are using data, remains to be seen. 

Will technology in insurance have to face a test of time?

The use of exceptional data and advanced analytics can help link the behavioural characteristics of customers and their spending habits – true fodder for machine learning models. How will insurtechs leverage useful insights to tackle age-old insurance selling challenges, such as intention to abandon, the propensity to purchase, or the right communication channel — will be the true test of competitive advantage.

Mantra Labs is a deep-tech advisor & consultant for young Insurtechs helping them create a strategic vision and an agile evolution road-map that addresses challenges from scaling to delivery. To learn more, reach out 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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