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Can Distributed Ledgers Accelerate Insurance Workflows?

The years 2018-19 are the banner years for the US$ 5.17 trillion global insurance sector. However double-booking, counterfeiting, and premium diversions through unlicensed brokers still throb insurance companies. And one of the prime reasons for such unethical activities is the lack of tight coupling between stakeholders. A simple solution to these challenges is distributed ledgers- a contemporary technology that ensures transparency. Distributed ledger technology in insurance can create a collaborative environment for handling information, minimizing instances of fraudulent activities. 

How Can Distributed Ledgers Accelerate Insurance Workflows?

Where most insurtech startups and small insurers are looking for “insurance-in-a-box” technology, big players demand bespoke technology to develop distinct capabilities for customer convenience and manage their enterprise workflows. Fortunately, distributed ledger technology solves a major chunk of this problem. 

For startups and small to medium size insurtech firms, cloud-based, customizable workflow management products can simplify the processes and create a collaborative work environment. Large enterprises can, of course, afford time and investment for tailor-made technologies suitable for their overall business requirements.

#Smart Contracts

Smart contracts can automatically determine whether to transfer an asset to the nominee or back to the source, or a combination of both. It does not necessarily create a contract or legal act, but can sure validate a condition. For example, Ethereum provides a prominent smart contract framework. 

Smart contracts allow credible transactions with or without involving third parties (oracles).

For example, Etherisc uses smart contracts concepts for building insurance products. The fundamentals used for Etherisc’s insuring flight delays product is applicable for insurance products like crop insurance, flood, earthquake, etc.

#Claims Management

Cifas reports a 27% rise in false insurance claims across the UK in the past year. Moreover, insurers identify 1 in every 30 claims as fraudulent. Organizations can track records better with distributed ledgers minimizing the illicit instances. 

Blockchain technology allows for automated real-time data collection and analysis. BCG expects Property and Casualty (P&C) insurance has the potential of processing claims up to 3x faster and 5x cheaper than traditional processes. 

It can also enhance customer experience by removing indirections due to various touchpoints between him and the claim settlement manager. Distributed ledgers can overall benefit processing time, automating payments, eliminating trust issues, and fraud reduction.

Traditional Insurance Model vs Distributed Ledger Insurance Model: Distributed Ledger Technology in Insurance

#Reinsurance

Reinsurance (passing a whole or part of insurance liabilities to another company) will simplify the sharing of data like bordereau and claims databases. For the insurance companies not preferring to share their client’s data, access rights can be customized in distributed ledgers.

According to PWC research, the reinsurance industry can save up to $10B by increasing operational efficiencies through distributed ledgers.

#Underwriting

“A shared, distributed ledger lends itself to this need for exchanging transparent, trustworthy data in a standard format in real-time.” 

Stefan Schrijnen: Director, Insurance, EY

Having accurate real-world data can help underwriters reduce paperwork and measure the assets and risks effectively.

Insurwave, a blockchain-enabled insurance platform uses a distributed database with secure access for insuring shipments across the world. Maersk, the world’s leading shipping and logistics company have partnered with Insurwave for insurance renewal of its fleet of 800 container ships. 

In the words of Lars Henneberg, Head of Risk Management at A.P. Moller – Maersk. “A simple dashboard gives us a live overview of how our assets are insured, and our brokers and insurers have access to the same overview. If the location, cargo, or other data about our ships changes, everyone is notified — no delays, no paperwork, no mistakes.” 

#Product Design using Distributed Ledger Technology in Insurance

Instead of all-encompassing insurance policies, consumers look for short, custom-built policies that satisfy their immediate needs. Therefore, to stay competitive, insurance companies (and even e-commerce startups) need to consistently build new and relevant insurance products. Expanding features or building new products on the same fundamentals can be effectively realized with strong and transparent ledgers.

AXA’s smart contract product Fizzy is a next-generation Parametric Insurer, which uses transparency as its USP. It provides travel insurance on flight delays and cancellations. The claims displayed on the website are stored in a blockchain and no one can change the terms after purchase. User can buy the insurance online. When the flight is delayed or canceled, the public databases of plane status information automatically triggers the insurance holder’s compensation. The event confirmation executes and closes the claim process instantly.

Precautions to Take With Distributed Ledgers in Insurance

  1. Enterprises should be cautious about sharing access rights on distributed ledgers.
  2. Blockchain transactions are irreversible, therefore every click from an authorized user should be mindful.
  3. Instead of mimicking a trend, insurance companies can deploy the distributed ledger technology to best suit their business requirements.

Conclusion

MarketsandMarkets expects blockchain technology’s share in the insurance market to reach $1.4 billion by 2023. 

The insurance industry has already deployed distributed ledger components for insuring flight delays, lost baggage claims, and is expanding to shipping, health, and consumer durables domains. 

The future can also witness blockchain, AI, drones, and robotics disrupting the insurance industry together.

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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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