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4 Key Takeaways from AI for Data-driven Insurers Webinar

5 minutes, 54 seconds read

The adoption of AI has increased exponentially across the business ecosystem in the past couple of years. Yet, Insurance still lags behind many industries due to the nature of its business. However, the ease of convenience that has come with AI implementations has made it indispensable to Insurers. So, where has the demand for the convenience come from? ‘Modern Insurance Customer’. The millennials today demand 24×7 service at their fingertips. They are keener towards information provided on digital channels and more likely to use social media and texting for Insurance interactions. To suffice the needs and demands of the modern insurance customer, AI integration is needed.

Role of AI in Insurance

Currently, AI is playing a pivotal role in transforming Insurance processes such as Claims, Underwriting, Customer Service, Marketing, fraud detection etc. For example, AI chatbots are being used to handle customer service which has led to a significant reduction in cost and optimization of human resources. According to a report by Deloitte on Unraveling the Indian Consumer, India has the world’s largest millennial population of 440 million in the age group of 18-35 years. Internet users in the country are expected to increase from 432 million in 2016 to 647 million by 2021, taking internet penetration from 30 per cent in 2016 to 59 per cent in 2021.

AI-based technologies will be needed to meet the evolving demands of modern insurance customers. 

According to the State of AI in Insurance 2020 report, nearly half of all Insurance executives surveyed believe that Automated processing can add value to their customer experience journeys. Nationwide is using artificial intelligence to help analyse customer interactions so it can solve customers’ problems earlier. Using AI and NLP, the insurer identified opportunities for reducing inefficiencies. And the result was more than half of all email enquiries could be resolved by guiding users towards digital channels instead. 

During the webinar, we polled the audience to gauge their motivation for implementing AI in their business processes. 44% felt that Claims Processing was the main reason to adopt AI into their business Insurance processes. 

The quick poll was in line with Mantra Labs’  State of AI in Insurance report 2020 which found that 74% of the respondents leaning towards the adoption of AI in Claims Processing. 

The webinar addressed some of the key challenges faced by Insurers, reasons behind these challenges and how we can approach these challenges to bridge the disconnect. 

Data in Silos

Most businesses that have data kept in silos face challenges in collaboration, execution and measurement of their bigger picture goals. Accumulating information in silos may not give accurate insights into improving engagement, which leads to impersonalized content that doesn’t speak to the customer. However, models well-trained on historic data, don’t necessarily perform better with live data. The challenge is that data is often needed before it is even possible to conduct a proof of concept — and sourcing the right data can be both time consuming and costly. The right approach to this issue would be to treat Data as the centrepiece for transformation. Insurers should engage with data scientists/consultants to review the quality of your data. Data exploration exercises need to be performed to challenge/validate the existing assumptions about data captured and stored within the org. 

[Related: 5 Proven Strategies to Break Through the Data Silos]

People, Expertise and Technical Competency

Many organizations face a challenge in finding the right ‘Skill and Talent’ for developing AI strategies and implementing them. Critical skill-sets like data scientists, cloud specialists, machine learning engineers, and AI engineers are essential to keep pace. Several Industry experts have also relayed that many AI-based projects and proof-of-concept work do not take off the ground due to lack of quality data at the disposal of such skilled professionals — derailing their availability/ usefulness for hiring purposes. Securing the right data science teams and training the right amount of data needed to support algorithm development can improve confidence levels for organizations.

Clear Vision, Process & Support from Executive Leadership

Often the reason for the failure of AI projects is due to lack of clear thought process from the top management. According to a recent BCG report, there is a big gap between expectations and planning. Most companies want to create a long-term competitive advantage with AI and expect to see a major impact from AI within 5 years. The big disconnect, however, is that only 39% of enterprises had an AI strategy to go with it. Insurers shouldn’t run headfirst into moonshot AI projects. Instead, they should take a more measured approach that identifies a simple problem or problems (use case) that AI can address. Insurers must ensure that the goals of AI projects must be in line with organization goals.

Technology and Vendor Selection

Many Insurers today fail to understand how AI can be leveraged for their business. There is a lot of unseen effort that goes behind any AI implementation project. They are not sure which AI-based technologies to be used for solving a particular problem. According to the State of AI in Insurance 2020 report, InsurTech funding in 2019 reached $6B revealing a stronger emphasis by insurance organizations to fast-track the progress and development made by startups in tackling age-old insurer ills with AI-fueled innovations. InsurTechs are seen as advantageous because they can add value by scaling their operating models at incredible speed owing to their nimble size.

There are tools, products developed harnessing AI-based technologies which have helped optimize several core insurance businesses. The Haven Life Risk Solutions team, in partnership with MassMutual, has developed a platform that uses both a rule engine and machine learning models to analyze the application and third party data in real-time. It can now help MassMutual make many underwriting decisions without human underwriter intervention, and in some cases also without a medical exam. Motor Insurance Claims is where AI is currently driving maximum efficiency. There are certain gaps that are being faced by insurers which can be resolved with AI platforms specific towards claims processing. FlowMagic, a visual AI platform developed by Mantra Labs focuses on streamlining Insurer workflows. 

[Related: FlowMagic — The Visual AI Platform for Insurer Workflows]

Concluding Remarks

In these challenging times, AI is already helping Insurance companies find their competitive edge, and stay operationally agile even during pandemics. Queries which are being addressed by chatbots help humans to handle more complex issues. It cannot be stressed enough that the next couple of months would be difficult for several businesses including Insurance. 

Companies across the world have already started making plans to ensure business continuity in this pandemic. AI or automation will play a crucial role in streamlining various processes and accelerate innovation to adapt to the dynamic environment and ensure long term stability.

Our host Parag Sharma interacted one on one with participants, during an interactive Q&A session where insights were shared with the audience. The discussions centred around some thought-provoking questions such as tracking AI performance once implemented, the role of AI in helping to reach Bharat, the potential for AI in telemedicine, etc. 

Articles from Parag:

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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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