How InsurTech-Insurance Partnership Delivers New Product Innovations

In 2019, InsurTech funding reached $6 billion, acknowledging the pace that technology can bring to overcome the age-old Insurance problems, the State of AI in Insurance 2020 says. While Incumbents are known for their core competencies in end-to-end insurance processes (from underwriting to claims settlement and reinsurance), InsurTechs are enticing millennials with fully digital innovative products and solutions.

The current situation can be viewed as either growing competition for traditional Insurers or an opportunity to collaborate and procure maximum benefits from each other’s competencies.

The World InsurTech Report 2019 states that nearly 90% of InsurTechs and 70% of Insurers are interested in collaboration with other InsurTechs and Insurance firms.

[Quick read: 10 Key Takeaways from the World InsurTech Report 2019]

In this article, we will discuss how InsurTech and Insurance partnership is proving beneficial for the entire ecosystem along with some successful partnership stories.

InsurTech and Insurance Partnership Benefits

A recent study pointed out that 70% of Insurance Executives are interested in collaborating with InsurTechs for developing new offerings. While developing new & innovative offerings remains the focus, such partnerships can play a crucial role in improving operational efficiency, enhancing customer experience, and increasing data capabilities.

InsurTech and Insurance Partnership outlook — *Source:* *The State of AI in Insurance*

Enabling Mobile-first Business Model

The current generation cares about self-managing everything that matters to them (including Insurance) on mobile. If it’s not convenient to use, the consumer is, perhaps, not ready to adopt it. For instance, each day, more than 5 billion people go online using their smartphones or mobile devices.

InsurTechs, as consumer-focused they are, have been leveraging mobile technologies for micropayments, mobility and IoT connectivity.

Insurer’s benefits:

Capability to extend their services/products to the mobile channel.
Attracting new customers who are more inclined towards self-service options.
Making information and services accessible and available everywhere, irrespective of geographical location, thus enhancing the customer experience.

Gaining Operational Efficiency at Scale

Insurers can harness InsurTechs’ capabilities on cutting-edge technologies like cognitive process automation, natural language processing, and ML-derived insurance analytics. Applications built using these technologies are scalable to the enterprise level.

For instance, with cognitive automation, Insurers can improve the efficiency and quality of computer-generated responses. Forrester predicts cognitive processes will overtake nearly 20% of service desk operations.

Similarly, InsurTechs are investing in developing workflow automation solutions, using which Insurers can create new automated workflows and/or customize existing workflows. Workflow automation with intelligent document and data processing capabilities has resulted in over 80% operational gains over manual processes.

Another milestone in improving operational efficiency is achieved through the adoption of chatbots. NLP-powered chatbots seamlessly integrate with an organization’s workflows and are a great way to humanize machine conversation and at the same time automate customer service portals.

Opportunity to extend the portfolio

InsurTechs still require traditional Insurers’ support for underwriting and during risk mitigation. On the other hand, Insurers are sceptical about micro and on-demand insurance because of the distribution challenges it poses for low-profit products. Insurers and InsurTechs can easily bridge the gaps and at the same time extend their range of offerings through strategic collaboration. Since 2017, Insurance and technology firms have announced more than 180 partnerships, KPMG states.

For example, American Family Insurance (AmFam) organizes its interests around innovation, advanced analytics, and connectivity. It has investments in CoverHound, HomeTap, Bunker, Wireless Registry, and LeaseLock.

“By making these investments, we do seek a financial return with the investment, but really we look for opportunities to work together, reconnaissance on how the world is changing.”
Dan Reed, MD, Managing Director, American Family Ventures

Source: Insurance Journal

Thus, InsurTech and Insurance partnership can also benefit from extending the product portfolio. Let’s now look at some remarkable examples.

4 Noteworthy InsurTech and Insurance Partnerships from Recent Years

1. Zurich Connect and Yolo

Zurich Connect, the digital arm of Zurich Italy, partnered with on-demand digital Insurance broker Yolo to provide virtual assistance to its customers. Together, they launched HomeFlix — to provide a range of Insurance coverage to renters and homeowners.

HomeFlix offers laundry service, concierge maintenance services such as plumbing and electric, and cleaning services to its customers along with regular and short-term insurance coverages starting at a nominal price of € 3.55 per month.

2. FRIDAY and Friendsurance

FRIDAY is a Berlin-based InsurTech startup. It offers digital automotive insurance with flexible terms like kilometre-accurate billing and the option to terminate at month’s end. The company partnered with Friendsurance, an online peer-to-peer insurance service provider. Friendsurance business model relies on paying out a percentage to customers who do not use (or use very little) annual insurance.

This partnership helps FRIDAY to sell at its policies on the Friendsurance platform and Friendsurance benefits from providing a range of insurance cover options to its customers.

3. Generali Global Assistance with Lyft and CareLinx

Generali Global Assistance is a division of Italy’s Generali Group. It provides travel insurance-related services. The company partnered with InsurTech Lyft and CareLinx to improve customer service and provide value-added services (e.g. CareRides, a door-to-door transportation service for special-needs individuals) respectively.

4. Prudential Singapore and StarHub

Singapore-based Prudential Insurance Company is the subsidiary of Prudential Plc, a British multinational life insurance & financial services company. The company partnered with StarHub to create FastTrackTrade — a digital trading platform. Using the FastTrackTrade platform, users can buy/sell goods, track shipments, make transactions, access financing, and buy insurance.

We’re a recognized InsurTech100 company with main focus on developing AI-first products and solutions for modern Insurance enterprises. For more details, please feel free to drop us a word at hello@mantralabsglobal.com.

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.

Image Source

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:

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.

Industry	Potential Annual Value
Banking	$200 billion – $340 billion
Retail and CPG	$400 billion – $660 billion

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.

Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

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.
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.
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.
Limited Contextual Understanding:
- AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
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:

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.
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.
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.
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:

Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.

Future Trends in Code Automation:

AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
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.

How InsurTech-Insurance Partnership Delivers New Product Innovations

InsurTech and Insurance Partnership Benefits