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TOP 10 INNOVATIVE INSURANCE PRODUCTS OF 2019

6 minutes 9 seconds read

We are witnessing the slow but sure, uberization of insurance. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers. The industry is abuzz with a steady influx of new innovative products, deriving value in areas that were previously untapped.

Processes like faster KYC verification and onboarding, automated underwriting, virtual claims adjusting, to name a few have become hot commodities within the last year. With AI-assisted technologies improving functionality, reducing real-time data fraud or meddling; insurers are creating custom-fitted coverages for the end-user.

For example, AI-powered underwriting solutions are already saving up to 97% of the time and resources that were traditionally required, enabling the corporate underwriter to specialize in cases that require deeper thought and analysis.

According to a recent CB Insights report, here’s what’s next for P&C Insurance.

The general insurance industry in India alone is valued at US$ 21B in 2019, growing at 13% CAGR over the next 5 years, and is expected to touch US$ 57B by 2025. Customer’s coverage expectations in the subcontinent have shifted toward desires of flexible insurance products that more closely match their lifestyle needs. These trends across the APAC landscape mirror the changes being witnessed in more advanced insurtech markets across Europe and North America.

Keeping customers primed at the centre of insurance innovation, here’s a look at the top ten most game-changing products in insurance today (in no particular order)

  • Splitsurance: Allianz Suisse used KASKO’s cloud-based insurance lifecycle platform to create and run a new type of insurance product – splitsurance. The offering targets university students in Switzerland, who live in a ‘flatshare’. Customers can get a liability cover, insure up to three high-value items of their choosing and also get discounts if their flatmates decide to join. Users can manage and update their cover autonomously through an after-sales customer portal.  
  • CUVVA: Cuvva provides hourly car insurance. In the mobile app, you simply enter the registration number and approximate value of the car you are borrowing from a friend or family member, choose the time you want to be covered for, take a picture of the car and Cuvva will get you an instant quote. Cuvva integrates with Facebook so that you can see which of your friends have cars to borrow. Cuvva queries various data sources to check driving licence data, the Claims and Underwriting Exchange and automated fraud protection to verify coverage quicker than legacy players can.

  • Digital Risks: DigitalRisks is an insurance specialist built for tech companies, offering a flexible, pay monthly Insurance-as-a-Service model. A founder could start out by protecting their laptop and end up with employer liability insurance and insurance against data breaches as the company grows.

  • Back Me Up: Back Me Up is an offshoot of Ageas. Their unique proposition is to be a parental-like cover for young people and students. For £15, one can insure their three most valuable items (eg: laptop, mobile), that also includes theft loss and worldwide travel insurance, plus there are no annual contracts.

  • Mango: The Mexico-based life and retirement insurance intermediary, allows users to obtain life insurance “in minutes.” They are pioneers in Mexico, who use technology to streamline every interaction you have with your insurance, avoiding unnecessary paperwork and confusing coverages. They have intelligent bots at work to answer insurance related queries, plus their UI is outstanding.

  • Bought By Many: The UK-based startup is a free, members-only service that helps users to find insurance for the not so common things in life. They offer pet, travel, car, bike, shoes, gadgets, home insurance covers and more. Members save an average of 18.6%. The company negotiates discounts directly with insurers for the clients’ unique situations.  

  • Dad Cover: The product is uniquely propositioned for Dads looking to get life insurance and financially protect their families. They’re full-sized professional financial planning firms, working with life insurance specialists.  Using a streamlined service, one can get a free quote after a quick chat with their DadBot, then one of their associated FCA registered advisers will talk you through your needs, answer all your questions and give you proper independent advice on what’s best to help protect your family.

  • Go Girl: GoGirl is a woman-only drivers insurance, that rewards good drivers with lower premiums. The insurance cover also includes a free courtesy car when your car is in for repairs, legal cover, child car seat, personal accident and windscreen cover. The company also insures your handbag and its content if it is stolen from the car. A free quote is available in minutes, and the whole transaction can be completed online.

  • Safety Wing: The “Insurance for Nomads” via SafetyWing is travel insurance that’s creating a safety net for online freelancers and entrepreneurs. The company offers coverage – up to $250k via Tokio Marine HCC – for unexpected illness or injury, including eligible expenses for the hospital, doctor or prescription drugs. They plan to extend their products to medical travel insurance in the near future.

  • Vlot: The Vlot platform provides life risk analysis and coverage solutions that smoothly adjust to your changing life situations. If you meet unexpected changes in your life, such as moving to a new city, getting married, or loss of a job – you can adjust your life risk coverage accordingly and never be over or underinsured. You only pay for what you really need in your current life situation, and control the premiums as and when dynamic changes occur.  

Special mention:

Fizzy: Fizzy is a revolutionary web & mobile insurance cover for flight delays of 2 hours or more. Developed by AXA, with Fizzy you combine the benefits of a startup and the insurance knowledge of a global insurer. They offer a one-shot coverage tailored to your own flight route, with automatic compensation in case of a delay, with no exclusions. You can purchase fizzy in 4 clicks at any time after your flight ticket has been purchased, up to 5 days prior to departure.

As customer tastes continue to evolve, the future looks promising for the state of innovation, while insurers align their offerings in lieu of the demand for newer insurance products.

The marketplace of insurance ideas is already a reflection of the changes customers want to see from their insurance providers, with young insurtechs being instrumental in bridging those unmet need-gaps, and bringing out positively unique insurance coverages for the average consumer.

(Note: The products highlighted here are not rank-based and are not indicative of the ‘best’ insurtech products available today. For more analysis on Insurtech products such as those from Lemonade, Trov etc. – which are not included here, read our blog on the Adoption of Chatbots across Insurance.)

webinar: AI for data-driven Insurers

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders and decision-makers on April 14, 2020.

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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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