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5 Revolutionary AI-driven Marketing Trends in Insurance for 2020

5 minutes, 41 seconds read

Insurance consumers around the globe are seeking convenience and expecting better customer experience. From millennials to Gen Z, with the agile connectivity, irrespective of the industry has numerous options to choose from. As the competition intensifies the insurance industry has to jump into the bandwagon of technovation in order to provide improved accuracy, cost-saving and excellent customer experience. 

Here is a list of the marketing trends in insurance that will prove to be a game-changer in the year 2020.

1. Robo Financial Advisors

According to a Business Insider Intelligence forecast, by the year 2020 Robo-advisers will manage investment products worth $1 trillion, which will spike up to $4.6 trillion by as early as 2022.

Robo advisors have been around for quite some time. In the year 2008, during the financial crisis, Jon Stein, a 30-year old entrepreneur launched “Betterment”, the first Robo-advisor. In recent years due to its low investment rates and data input based research results, it has increased in popularity. 

It is basically designed for the people who want to manage their finances with low management cost. Based on respective data inputs, the Robo-advisors offer any advisory services. 

The main purpose behind the making of the Robo-advisor is to bring the financial services to the wide range of population with lower investment cost as compared to the traditional human advisors. Upwardly.com, 5Paisa.com and Goalwise.com are some applications of Robo-advisors.

Behind the scenes of the software of Robo-advisors are actual human beings who track the market regularly and adjust the algorithms based on the current market condition. Robo-advisors are a boon to the end-users as they can invest in direct plans of mutual funds without shelling any commission. However lack of personalization and one-size-fits-all products are the areas of improvement.

2. Data Integration: The Future of Marketing

IDC estimates that, by the year 2020, the digital cosmos will reach 44 zettabytes, further complicating the lives of marketing professionals.

Integrating data sources is vital for any company, whether B2B or B2C to successfully meet Customer Experience expectations thereby drive accelerated sales revenue.

With an integrated source of information, retailers can administer and optimise marketing through KPI’s, metrics and dimensions that would not have been possible with the separate source system. In order to upscale marketing operations, a connected viewpoint is essential to evaluate the campaigns, audiences, events and channels, and drive the strategic goals.

From an operational viewpoint, CRM solution provides the organization with new business and the ERP system allows to manage and drive businesses around obstacles. A good place to start with the data integration is by Integrating these two systems shall provide marketers and the organizational sales-force with vital information, that can be shared with the stakeholders.

3. AI-driven Copywriting

Artificial intelligence can create cancer combating drugs, control self-driving cars, defeat the best brains at incredibly complex board games, but one realm it can’t perform flawlessly is communicating.

To help solve the issue, Google has been feeding it’s AI with more than 11,000 unpublished books, including 3,000 steamy romance titles. 

Autoencoder, a type of AI network, uses a data set to reproduce a result (in this case copywriting) using fewer steps. Insurers can harness this AI capability to create sentences and suggest the best-optimised language to approach the customers.

AI copywriting is evolving to a whole new level. Google granted  €706,000 (£621,000) to the Press Association, to run a news service with computers writing localised news stories. AI with the help of human journalists can write up to 30000 news stories a month and scale up the volume of the stories that would otherwise be impossible to produce manually.  

“Skilled human journalists will still be vital in the process, but Radar allows us to harness artificial intelligence to scale up to a volume of local stories that would be impossible to provide manually. It is a fantastic step forward for PA.”

  • PA’s editor-in-chief, Peter Clifton 

4. Gamification of Insurance

At the nexus of marketing trends ranging from social networking to the IoT to behavioural science and wearable tech;  gamification is a powerful lever for insurers and insurance agents. It creates an enriching digital experience and customer-centric business model.

Gamification offers great potential value to the insurance business process in the realm of consumer engagement and customer experience. From millennials to Gen Z, it has emerged as a useful practice and effective means to target early technology adopters by:

  • Transforming mundane tasks into interesting and fun experiences that keep users returning.
  • Increases brand awareness, brand penetration and affinity.
  • Increase sales by educating customers about product suitability and guide them to buying the product.
  • Motivating people to act in areas of healthcare and wellness, safe driving, financial planning and sustainability.

Ingress and AXA redefined the world of gaming and advertisement. December 5th, 2014, Niantic Labs the creator of ‘Ingress’ partnered with AXA. In the game, AXA Shield was initially only obtainable from AXA Portals, leading you to AXA business locations in person.

5. Advanced AI Capabilities in Insurance

Innovation and technology are the next frontiers in the insurance industry. While automation and IoT are already a reality for insurance, with the advent of AI there has been a holistic approach to Insurance automation. With insurance leveraging AI, it has expanded its reach to more ecosystems than ever before. Deploying AI capabilities in insurance can help make smarter underwriting decisions, fraud detections, risk assessment and create a better customer experience.

AI is driving significant change in business with insurance being no exception. It has the potential to enhance the insurance business model by-

  1. Improving the speed of the workflow: AI and RPA in insurance reduce redundancy of task. Automation of day to day tasks would reduce cost and time consumption thereby increasing accuracy, quality and competency.
  1. Customizing the services for better customer experience: One size no longer fits all, and the same goes for the insurance industry. With focus on individual markets, insurers can create niche usage-based products to sell the packages in a variety of ways.

Parag Sharma, CEO, Manta Labs and AI thought leader is going to speak about the Internet of Intelligent Experiences™: CX for the Digital Insurer at India Insurance Summit and Awards 2020 on March 12, 2020. Catch him live at IISA 2020.

Details

  1. Providing new insights: Insurance is no guessing game. Data in silos is the biggest drawback for any industry. AI in insurance can integrate this data and provide analytics to help actuaries have a better insight while making a decision about a product.

Marketing Trends in Insurance: The Bottom Line

Today, at the core of marketing in Insurance, lies AI, Machine Learning and advanced data analytics to foster better experiences for the end-user. We’ve listed 5 most important trends that have the potential to shape marketing business models for Insurance and InsurTech firms. Be it Robo financial advisors or gamification, impressing customers remains the prime goal for Insurers.

Have thoughts and queries regarding upcoming marketing trends in Insurance? Please feel free to drop us a word at hello@mantralabsglobal.com.

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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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