Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(153)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

What can APAC insurers learn from each other in an After-COVID World

The Pandemic has forced Insurance carriers, both legacy and new, to adapt their business models, re-evaluate risk modelling and pricing strategies and conceive fresh ways to interact/engage with prospective buyers. Especially within the APAC region, there are many businesses that have adapted to the new business normal successfully and carved out specialised tactics to thrive. 

In this blog we will take a look at some instances that reflect these changes and the measures taken by Insurers that will provide insights for other carriers. But before hopping into those points, let’s have a look at few trends which are worth mentioning:

Changing Trends To Be Considered for a Longer-Term

  1. More Consumer-Centric Solutions:

Most APAC insurance companies use a one-size-fits-all approach; wherein, offering similar packages or products to a broader audience. But that is not the case anymore. In times like this, customers are picky and have become more aware than ever and expect solutions customized for their requirements. 

Therefore, to meet their expectations, many APAC insurers started offering tailor-made policies that meet individual requirements. This is one of the trends that we will see going forward on a longer-term.

  1. Enhanced Claim Settling:

Along with companies focusing on providing customer-centric solutions, they’ve also been focusing on enhanced claim settlement mechanisms. This will aid customers to get financial backup under challenging times.

Now, claims can be raised faster, and policyholders can simply upload the documents required. Insurers can use this to increase their efficiency and settle claims faster and with more efficiency.

  1. Digital Operations:

Since the government has alerted people to follow the guidelines and maintain social distancing, people were rarely stepping out. And this gave rise to more online transactions and deals made online. An increasing amount of people are buying things online, and that goes for insurance as well. 

More number of APAC Insurers started offering insurance online and made other processes feasible online as well. This empowers policyholders to make their payments online and upload their documents from their own homes’ safety and comfort.

Essential Learning for APAC Insurers to Adapt Quality Change

The unappreciated effects of the pandemic have shaken the whole economy and businesses on a large scale. Indeed, all the business models have experienced its effects, but for some, it went positive, and for others, it was negative. 

However, the insurance sector stands in the middle of the ground. Under this umbrella, businesses have used various tactics to cope with the negative aspects and paved their way from surviving to thriving.

To win more customers in the post-covid world, a proper action plan is required. The following points are what successful APAC Insurers are up to; you can use them as inspiration to power-pack your business for post-covid scenarios. 

  1. Telemedicine in Health Plans

Telemedicine is the distribution of clinical and health-related services remotely in real-time two-way communication. This concept had just begun to grow in India but COVID catalyzed the process as the nation went under lockdown and social distancing became the norm.

Since telemedicine became widely popular in the post-COVID world, the IRDAI instructed insurance companies to cover the medical costs incurred on telemedicine as well if their health plans offer coverage for doctor’s consultations. Therefore, health plans are now more inclusive in the Post-Covid world as they cover telemedicine costs too.

APAC insurers can adapt to this concept as it will power-pack their health plans even more. 

  1. Replacing Physical Signatures

Because of social distancing norms, many insurance industries have adopted the elimination of physical signatures on proposal forms. This can be acquired by APAC insurers for the long term. 

Now, individuals are liberated to purchase insurance plans with online proposals which are verified by the confirmation mails or OTPs rather than physical signatures of policyholders. This also saves individuals to travel to the workplace and save a lot of time. 

  1. Implementing Virtualised Outreach

Being the new normal, remote working has made us comfortable with having virtual meetups and conferences. Many companies in Asia-Pacific have adopted this method in outreaching and having a potential conversation with new customers. It enables them to get their work done by sitting in their comfort zones. This is a good adaptation in the long run and saves customers from traveling to a location. 

Therefore, make sure you are active in this digital world to remain visible to your clients. Utilize various ways to keep them warm. It may be email newsletters, videos, social media, and even interactive webinars so that your business remains at the forefront of your clients’ minds.

  1. Adoption of AI-driven systems

Acquiring the power of AI is not a business decision anymore, it has become a survival strategy. And Covid-19 has helped more APAC insurers to understand this. Therefore, the insurance industry is undergoing a swift and tremendous transformation, driven by the burning need to improve customer experience and smooth interaction.

AI can majorly help APAC insurers in the following ways:

  • Managing risk easily and efficiently with the help of neural networks. It can detect red flag fraud patterns and minimize fraudulent claims
  • Makes the agent-customer interaction better and smooth 
  • It helps in making the claim process easy and fast by eliminating the manual efforts from document processing to fraud flagging. 
  • Liberates APAC insurers with 24/7 customer service. 
  • AI can help insurance companies in determining business-critical aspects appropriately such as the maximum possible loss, probability, and pricing more.
  • Efficiently recommends the most beneficial products to their customers based on previous behaviors.

Wrapping Up

These lessons have been gathered through analysing and studying the business impact of responding to economic, political, and public health crises in the region and the global insurance industry at large. Coping up with the pandemic is a significant achievement in itself. But to grow sustainably through a Global crisis takes significant planning, effort and resources to quell the tide. APAC carriers will have to adopt fast, be nimble and navigate swifty for the uncertain road ahead in an After-COVID World. 

Cancel

Knowledge thats worth delivered in your inbox

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot