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Four New Consumer-centric Business Models in Insurance

The insurance industry is changing and experts predict — nearly one-third of existing insurance models will disappear within this decade. The fierce competition, new opportunities with technologies like AI, and on top of that millennials’ changing preferences sum up to the call for more flexible and consumer-facing business models. Here are four new business models to set the insurance archetype.

Source: The Deloitte Global Millennial Survey 2019 

Social Good & Transparency as a Business Model

Currently, AI is being used to strengthen the capabilities and knowledge of insurers and not consumers, creating information asymmetry. But, the question is — for how long will the consumers accept being a victim of ignorance. 

A possible solution to this situation is bringing information transparency. It’s not like traditional insurers don’t share policy information with their customers. They do. However, lengthy policy documents and customers’ reliance on agents for information shadows the actual coverage, terms, etc. In a way, the information that customers receive becomes dependent on the agents’ knowledge and intentions.

Translating policy, terms and conditions documents into consumable bits of information with a clear distinction between what’s covered and what’s not will help in achieving transparency between insurers and customers.

For instance, Lemonade — the American Insurtech for renters and home insurance, disrupted the industry lately with their instant and transparent end-to-end insurance process. Their consumers are better aware of coverage and claims thanks to simplicity in the user experience. 

Moreover, Lemonade donates the unclaimed premiums to social causes their consumers care about. From its inception in 2015 to date, Lemonade has sold over 1.2 million policies, in complete transparency and all through their AI bot — Maya!

Nearly 46% of millennials are willing to make a positive impact on the society/community. Lemonade has partnered with 92 charities and has donated $8,46,849 from unclaimed premiums. Hence, the answer.

Similarly, Swedish InsurTech Hedvig has successfully deployed it’s “nice insurance” services, giving back 80% of the unclaimed premiums to charities chosen by the customers.

More insights on — millennials and their expectations from insurance ‘beyond’ convenience.

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.

B2B2C or API-based Model

When user acquisition is the top priority, B2B2C or API-based model comes into action. Also known as an open-source platform solution, this business model connects people and processes with technology infrastructure and assets to manage user interactions. 

In the API-based model, apart from traditional distribution channels, 3rd party apps also become a medium for customers to buy/access insurance policies. Automation plays a key role in this insurance model. Here, any other customer-centric digital application can install the API without manual/human intervention.

API-based Insurance Model Affinity Distribution Channel

For example, in January 2018, Allianz announced that it will offer parts of its Allianz Business System (ABS) to other insurance companies for free. Interested organizations can simply install the API (Application Programming Interface, which is nothing but a chunk of software that connects two different apps) and start selling Allianz policies to their customers.

Lemonade — after disrupting the insurance space through transparency, has now stepped into this model. In October 2017, the company launched its public API, allowing anyone to distribute Lemonade’s policies through their websites or apps.

“It takes years to pull together the licenses, capital, and technology needed to offer insurance instantly through an app, which is why it’s almost nonexistent. Today’s API launch changes that. Anyone with a slight familiarity with coding can now include these capabilities in their app, in a matter of hours.”

Shai Wininger, Co-founder, President & COO, Lemonade

P2P Insurance

Unclaimed premiums also contribute to conflicts between insurers and policyholders. What if a customer is not interested in donating to charity, unlike mentioned in the above case? 

Peer-to-Peer (P2P) insurance is perhaps an answer to eliminate premium settlement conflicts. It is also an emerging business model to access insurance coverage at lower costs than most of the traditional insurances. 

This insurance model pools the individuals who share at least one relation — friends, family, or interest (community/clubs) and it serves two-fold benefits-

  1. Every member knows other members, funds available, and claims initiated/processed. Therefore, irrespective of the information shared by the insurer, there’s a transparent collaboration among peers.
  2. Since the members know each other socially, there’s a negligible chance of fraudulent claims. For instance, in the US alone, insurance frauds amount to nearly $80 billion/year.

Also read – how behavioral psychology is fixing modern insurance claims

The notion of financial protection for the community has been prevalent in our societies since the 1600s. In the middle ages, the tradesmen followed the guild system (an association of craftsmen and merchants), where participants paid fees as a kind of insurance safety net. Though, the successful conceptualization of P2P insurance in the modern business models dates back to 2010 with German InsurTech — Friensurance. However, the P2P insurance model has credited the success to many more InsurTechs like Guevara, Axieme, TongJuBao (P2Pprotect), and PeerCover

Microinsurance

The greatest limiting factor for the success of microinsurance is distribution. For example, in the US, 18% of the premium represents the distribution cost, set aside marketing and advertising costs. Availability isn’t the issue for microinsurance. 

The new business model for microinsurance focuses on outreaching and distributing policies at scale. Workflow automation solutions like document processing, automated customer query resolution, etc. make microinsurance models more effective. 

  1. Aggregator model: Instead of traditional agents, retailers, utility or mobile network operators, etc. can be intermediaries for the distribution of microinsurance policies. They provide access to a very large consumer base and even more with free and freemium coverages. For example, Check24, a European aggregator together with HDI insurance developed AurumPROTECT that is available exclusively through aggregators channels. 
  2. Harnessing proxy insurance sales force: Banks have been the ideal partners to distribute microinsurance policies at scale for ages. But, for short-term policies, this is a good time to utilize the agents of other products to offer insurance as an ancillary product. For example, Ola — an Indian cab aggregator provides a number of travel-related microinsurance underwritten by Acko General Insurance. 

The Bottom Line

The effectiveness of each of these models drills down to the smart use of technology in their implementations. Moreover, most of these business models are automated, thus, eliminating additional human resources for implementations. For instance, in India, an agent can charge up to 20% of the premium amount as fees, which can reduce significantly if the distribution is automated. Investment in technology for automating operations is also worth it because it makes customer outreach simpler and faster. 

Also, read – 5 Front-office operations in Insurance you can automate with AI.

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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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