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10 Takeaways from the World InsurTech Report 2019

6 minutes, 6 seconds read

The insurance market dynamics are changing rapidly. While a connected ecosystem is the need of the time, agility and new business models are a way through. The current edition of the World InsurTech Report (WITR) emphasizes on developing synergies between Insurers and InsurTechs for the success of the future insurance marketplace. Here are 10 key takeaways from WITR 2019.

Insurance Business Process Improvements

Tech giants like Alibaba, Amazon, Apple, Facebook, and Google are entering the Insurance space with enormous customer data. Moreover, customers (nearly 30%) are responding positively to buying insurance products from BigTech firms, according to the World Insurance Report 2018. WITR proposes the following business process improvement for Insurers to remain market-fit.

#1 Partnerships with Insurtechs, Financial Institutions and Industry Players

90% of InsurTechs and 70% of Incumbents believe partnerships are crucial. And these partnerships are not confined only to the insurance sector. These can include collaboration with financial, technology, healthcare, travel, transportation, hospitality, retail, and more. 

Partnerships - world InsurTech Report 2019
The diagram illustrates the Insurance and InsurTechs’ level of willingness for partnerships – World InsurTech Report 2019

Baloise Insurance partnered with Swiss bank BLKB, and Swiss online insurance broker Anivo to develop a flexible and scalable digital insurance platform with B2C integration. The product released as Bancassurance 2.0 achieved a hit ratio of 50% for video-chat advisory sessions; more than 90% of customers rated the experience as good or very good. 

Partnerships can also bring compound insurance products, which otherwise seems impossible. For example, Swiss Re and French cybersecurity InsurTech firm OZON together, launched CyberSolution 360°. It is a risk management solution combining insurance and cyber-attack protection services for small and medium-sized enterprises.

#2 Adopting New Business Models

Not only Insurers, but also customers approve of new insurance models. For instance, 41% of customers are ready to consider usage-based insurance and 37% are willing to explore on-demand coverage. To meet the coverage gaps, offer convenience and personalization, Insurers are adopting the following new business models.

  1. Usage-based model for as-you-go coverage/premiums for a customer’s potential risky behaviour.
  2. On-demand model for cost-effective requirement-based coverage.
  3. Parametric insurance for covering uninsured risks, based on an objective-triggering event.
  4. Microinsurance services with low-premium packages.

#3 Aligning Strategies with the Future Insurance Marketplace

An insurance marketplace is a viable solution to support a broad spectrum of customer demands. It can also offer coverage for emerging risks and can deliver easy-access compound offerings from individual players of the insurance, manufacturing, and technology ecosystem.

For example, Friday, a Berlin-based startup, launched in 2017, offers digital automotive insurance with kilometre-based billing, flexible tenure, and paperless administration. With telematics support from BMW CarData, Automotive services from ATU, car-rental marketplace Drivy, and distribution channel from Friendsurance, Friday offers customer-centric insurance products.

“The insurance marketplace of the future will provide data and insights about customers that the industry never had before. This will allow firms to design a product closer to customers’ needs and, more importantly, offer them the product when they need it!”

Stephen Barnham, Asia CIO, MetLife

#4 Building an Integrated Ecosystem

As aggregators, OEMs (Original Equipment Manufacturers), policy management apps, and third parties enter the insurance value chain, an integrated insurance ecosystem can smoothen the overall functioning. 

For instance, digital integration with aggregators and third parties can broaden the Insurers’ distribution channel. Partnering with OEMs can help them with real-time customer data. Further, APIs, cloud-based storage, and blockchain can foster the insurance ecosystem with data security and transparency.

Technology Implementation Partners- World InsurTech Report 2019
An overview of digitally integrated ecosystem – World InsurTech Report 2019

#5 Being an Inventive Insurer

Inventive Insurers are the ones who have strategically updated their product portfolios, operating models, and distribution methods. They are realistic about their competencies. By identifying their distinct capabilities and partnering with other players to bridge their competency gap, Inventive Insurers can deliver an end-to-end product to the customers.

The World InsurTech Report 2019 defines the competencies of Inventive Insurers as follows –

  1. Capable of making business processes more intelligent, efficient, and effective using AI, automation, and analytics.
  2. Creating new scalable products with shorter development cycles.
  3. Enabling seamless integration with new data sources and distribution models.
  4. Offering value-added services to the customers.

Product Innovations

The tech-savvy customers are seeking easy-to-understand products with the facility of direct online purchases. Even leading Insurer like Berkshire Hathaway’s Insurance Group – BiBerk launched ‘THREE’ – only three pages long product covering workers’ compensation, liability, property, and auto to catch the pace. The drift is towards the following new insurance products.

#6 Bundling Financial and Non-financial offerings

An insurance package comprising both financial and non-financial products can expand an Insurer’s products portfolio, giving a competitive edge. It can also help in pitching new prospects. Bundling products and services will increase customer touchpoints and can help insurers identify their needs more effectively.

Bundling financial and non-financial services: World InsurTech Report 2019

For example, Homeflix insurance provides renters and homeowners insurance to its core. In addition to insurance coverage, it also offers concierge maintenance services like plumbing and electricity. The company also plans home delivery, babysitting, and cleaning services next.

#7 Tailored Products

Traditional insurance policies don’t fit today’s desire for add-on services, personalization, and flexible offerings. The World Insurance Report 2019 survey found that more than 75% of B2B customers and 85% of retail policyholders believe they’re not covered against the emerging risks.

Being aware of the need for customized products, 84% of Insurers and 80% of InsurTechs say they are focusing on “developing new offerings.”

#8 Products that Engage and Educate Customers

Gamification, video-chat sessions, and social media are promising channels for engaging with customers and educating them about risks and their need for coverage. Healthy interactions with customers through their preferred channels can boost sales.

“Insurers should focus on providing user friendly, transparent information via digital channels, allowing customers to make an informed decision. This will be critical not only for upselling, but also for attracting more new-generation customers, who are tech savvy and want to make faster product decisions.”

Jas Maggu, CEO, Galaxy.AI

Operational Improvements

For operational success- understanding customer preferences, conceptualizing new products portfolio, partnerships, and an effective go-to-market strategy is crucial. Fundamental shifts in the current operational models towards experience-driven solutions, strategic use of data, partnerships, and shared ownership of assets portray emerging trends. 

#9 Embracing Digital Agility

70% of insurers and 85% of InsurTechs believe a lack of technological readiness is a critical concern.

The more quickly Insurers implement initiatives, the closer they will be to achieve the digital maturity and hence actively participate in the connected ecosystem. The agile digital infrastructure demands real-time data gathering and analytics and automation of complex processes.

It will also lead to product agility. Insurers can offer new products at a faster pace and with reduced GTM (go-to-market) time, they can gain a competitive advantage. 

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.

#10 Automating Processes

Not only claims processing and underwriting, but much more insurance back and front-office operations can also be automated. Automation brings two-fold benefit to the insurers. One- mundane tasks are carried by machines, speeding the processes and freeing humans for sophisticated work. The other benefit lies in enhanced accuracy. 

For example, AIA Hongkong has improved claims processing time by 40% through AI-driven ICR techniques and intelligent process automation. 

Read claims automation case study: How AIA Hong Kong saves 60% through claims automation.

Deutsche Familienversicherung (DFV) provides a digital automated platform for property and supplementary health insurance. It can process the transactions in real-time enabling customers to file claims and receive feedback immediately. Moreover, policyholders can engage with the firm via several digital channels, including Amazon Alexa.

Source: World InsurTech Report 2019

InsurTech Report 2019: Summing-up

  1. Scope of business process improvements through partnerships, devising new business models, embracing insurance marketplace, building an integrated ecosystem, and being an inventive insurer.
  2. Introducing innovative products that are tailor-made and educate customers about potential risks; bundling financial and non-financial offerings.
  3. Operational improvement through automation and digital agility.

We’re AI-first products and solutions firm for the new-age digital insurer recognized among the InsurTech100 for pioneering the transformation of the global insurance industry. Drop us a line at hello@mantralabsglobal.com to know more about our offerings.

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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.

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:

  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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