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The 7 InsurTech Trends That Matter for 2021

The COVID-19 pandemic has triggered structural changes that have forced insurance players to become more competitive than ever. The pandemic has proved to be a catalyst, nudging insurers to prioritize their focus on improving customer centricity, market agility, and business resilience.

As per a report by Accenture, almost 86% of insurers believe that they must innovate at an increasingly rapid pace to retain a competitive edge.

‘Insurtech’, short for ‘insurance technology’, is a term being widely used these days to talk about the new technologies bringing innovation in the insurance industry. The digital disruption caused by technology is transforming the way we protect ourselves financially.

In this article, let’s explore the top insurtech trends for 2021 that will pave the way for the future of insurance. 

  1. Data-backed personalization

Insurance companies are increasingly drifting towards collecting data to understand customer preferences better. Using data collected from IoT devices and smartphones, insurance companies are trying to deliver customized advice, the right products, and tailored pricing. 

Personalization enables exceptional experiences for customers while offering them products and services tailored to their specific needs. The idea is thus to put customers at the core of their operations.

Some examples of data-backed personalization include the following –

  • Reaching out to customers at the right time. This involves pitching to customers when they are thinking of buying insurance like while making high-value purchases, during financial planning, or during important life events.
  • Reaching out to customers through the right channel. This involves reaching out to customers through appropriate platforms like a website or mobile app.
  • Delivering the right products to specific individuals. This involves delivering products to customers based on their specific needs like reaching out with auto insurance to a customer who travels often.

Take the example of the financial services company United Services Automobile Association. The organization collects data from various social media platforms and uses advanced analytics to personalize its engagement with customers. The company advises customers when they are buying automotive insurance or are looking to purchase a vehicle. The company also provides its customers tailored mobile tools to help them manage and plan their finances.

  1. Usage-based policies

One of the biggest trends in the insurance industry is the growth of usage-based policies. In the coming year, we are going to hear a lot more about the ever-growing popularity of short and very-short term insurance that needs to be activated quickly.

We are going to see the rise of dedicated apps that allow easily activating policies based on usage needs. For instance, one would be able to take insurance for a sports event or a travel plan.

  1. Robotic and cognitive automation (R&CA)

Both robotic process automation (RPA) and cognitive automation (CA) represent two ends of the intelligent automation spectrum. At one end of the spectrum, there is RPA that uses easily programmable software bots to perform basic tasks. At the other end, we have cognitive automation that is capable of mimicking human thought and action. 

While RPA is the first step in the automation journey for any industry, cognitive automation is expected to help the industry adopt a more customer-centric approach by leveraging different algorithms and technologies (like NLP, text analytics, data mining, machine learning, etc.) to bring intelligence to information-intensive processes. R&CA, therefore, encompasses a potent mix of automated skills, primarily RPA and CA.

In the insurance industry, there are vast opportunities for R&CA to ease many processes. Some of its use cases in the insurance industry include –

  • Claims processing – R&CA can help insurance companies gather data from various sources and use it in centralized documents to quickly process claims. Automated claims processing can reduce manual work by almost 80% and significantly improve accuracy.
  • Policy management operations – R&CA can help automate insurance policy issuance, thus reducing the amount of time and manual work required for it. It can also help in making policy updates by using machine learning to extract inbound changes from policy holders from emails, voice transcripts, faxes, or other sources.
  • Data entry – It can be used for replacing the manual data entry jobs, hence saving a significant chunk of time. There are still many instances where data like quotations, insurance claims, etc. is entered manually into the system.
  • Regulatory compliance – R&CA can be key in helping companies improve regulatory compliance by eliminating the need for human personnel to go through many manual operations that can be prone to errors. It helps reduce the risks of compliance breach and ensures the accuracy of data. Some examples of manual work that R&CA can automate include name screening, compliance checking, client research, customer data validation, and regulatory reports generation, etc.
  • Underwriting – It involves gathering and analyzing information from multiple sources to determine and avoid risks associated with a policy like health, finance, duplicate policies, credit worthiness, etc. R&CA can automate the entire process and significantly speed up functions like data collection, loss assessment, and data pre-population, etc.
  1. Data-driven insurance

Although insurance has always been driven by data, new technology means that insurers are likely to benefit from big data. Using valuable data insights companies can customize insurance policies, minimize risks, and improve the accuracy of their calculations.

Here are a few use cases of how insurance companies use big data – 

  • Shaping policyholder behavior – IoT devices that monitor household risk help insurers shape the behavior of policyholders.
  • Gaining insights on customer healthcare – Medical insurance companies are drawing insights from big data to improve recommendations in terms of immediate and preventive care.
  • Pricing – Companies are using big data to accurately price each policyholder by comparing user behavior with a larger pool of data.
  1. Gamification

Gamification is turning out to be a very interesting and promising strategy that may get a lot more popular in 2021. It involves improving the digital customer experience by applying typical dynamics of gaming like obtaining prizes, bonuses, clearing levels, etc.

Gamification has shown promise in increasing engagement and building customer loyalty. For example, an Italian insurance company was able to observe a 57% increase in customers (joining the loyalty program) due to a digital game created by the company.

  1. Smart contracts

Smart contracts are lines of code that are stored on a blockchain. These are types of contracts that are capable of executing or enforcing themselves when certain predetermined conditions are met.

The market for smart contracts is expected to reach a valuation of $300 million by the end of 2023.

The insurance sector can benefit from smart contracts because these can emulate traditional legal documents while offering improved security and transparency. Moreover, these contracts are automated, so companies do not need to spend time processing paperwork or correcting errors in written documents.

  1. Other key trends

Some other key trends that may be relevant in 2021 include – 

  • Extended reality – Although it’s still in its early days, extended reality can benefit the insurance industry by making data gathering much safer, simpler, and faster by allowing risk assessment using 3D imaging.
  • Cybersecurity – Since insurance companies are migrating towards digital channels, they also become prone to cyberattacks. That is why cybersecurity will remain a trend in 2021 as well.
  • Cloud computing – The year 2021 could witness cloud computing become more essential than ever before. 
  • Self-service – It allows customers to have an alternative path to traditional agents as per their need and convenience, and thus looks to pick up pace in 2021.

Conclusion

It can be concluded that the pandemic has accelerated the shift towards digital in the insurance industry. As for the trends for 2021, there seems to be a general inclination towards personalization, data mining, and automation in the industry.

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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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