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Is AI Disruption on the way for Kenya’s Insurance Space?

The earliest known reason for introducing insurance protection in Kenya, came during the time of the Colonial British — when they insured their farms and crops against loss, damage etc. Today, Kenya has 70% of the East African Insurance market (among Burundi, Uganda, Tanzania & Rwanda). Still, African Insurance is relatively nascent in terms of size. Only 6 major markets dominate the landscape in a serious way — Egypt, Tunisia, Morocco, South Africa, Nigeria & Kenya. Infact, the number of insurtech startups in the continent altogether is a paltry 50 something. 

The looming political climate coupled with a slowly recovering economy and some fierce competitive tactics used by traditional incumbents places the industry far from ideal in terms of marketplace conditions, including the slowdown in uptake of insurance products by an income-sensitive population.

Yet, Kenya offers a sense of growing appeal for young insurtechs in this region. The market remains largely undisrupted, since insurance penetration is only about 3% (insurance penetration for the African continent is only at 0.3%), attracting large international insurers like Allianz and Swiss Re who have recently entered the market. Kenya, like other countries in the region, has enormous potential similar to South-East Asian economies that also remain largely undisrupted with lower penetration rates.

The positive sentiment surrounding Kenya’s potential for deep tech disruption is not surprising — According to the 2019 Government AI Readiness Index published by the  IDRC and Oxford Insights — Kenya is the most AI ready country in Africa.

Buying Behavior

Insurtech startups are exploring avenues using AI that large, traditional players have less incentive to exploit, such as offering ultra-customized policies, social insurance, and using behavior data from devices to dynamically price premiums.

The Millennial experience is entirely technology driven, while their attitudes and perceptions as consumers will shape the future of how insurance as a service continues to remain relevant.


According to a Kenya Insurance Industry Report, 65% of millennials compare prices across different websites before making a purchase, 68% only buy a product through referrals from friends and social media. Interestingly, 84% of them are opposed to traditional advertising. 

For insurers, loyalty comes at a price — often dictated by the pain point the product/service can eliminate for impatient classes of customers. Analysing buying or browsing behavior can lead to an immense amount of ethically siphoned data. Using ML models and regression algorithms, insurers can create a unified view of their prospect, and realize a multi-targeted approach to create opportunities for upselling or cross-selling.


The report also highlights the importance of making sense of social media behavior — since 41% of millennials use social networking sites to pass on recommendations of products and services to friends and family.

Unlocking market potential requires targeting the uninsured growing middle class in creative ways. In addition to better pricing models, insurtech startups are testing the waters on a host of potential game-changers, such as using deep learning trained artificial intelligence (AI) to handle the tasks of brokers and finding the right mix of policies to complete an individual’s coverage.

Insurtechs are using AI to solve for Kenya’s distribution challenges, by looking at vital consumer needs that have previously been unmet or glossed over. At the same time, there is scope for improving the average consumer’s awareness of artificial intelligence technology, and how they can take advantage of it to solve priority-first issues related to convenience, cost and range of choice.
Nairobi-based Jubilee Insurance, the largest insurer in East Africa is making the most of AI tools like chatbots and automated messaging platforms for streamlining simple customer feedback & support operations. They have also launched forward-thinking products like “Recover in Style” which provides hair and make-up services to Jubilee patients who are hospitalized — services that go beyond the financial needs and into the realm of delivering superior customer experiences.

These efforts highlight a trend pointing towards the growing interest in the use of apps to pull policies into one platform for management and monitoring, creating on-demand insurance for micro-events like borrowing a friend’s car, and the adoption of the peer-to-peer models to create customized coverages. Bluewave, for example, is an insurtech startup offering low-cost insurance products, as low as US$4 a week, aimed at low-resource, low-income users in last-mile environments.

The expanding middle class and growth in mobile phone penetrations will be critical to widening distribution and getting more people to buy micro-insurance sized products for the first time. Badalaa is an on-demand insurtech startup focussed on bringing insurance at the point of transaction where the user needs it. Turaco, a recently funded insurtech, with premiums for as little as US$2 — leverages mobile financial services to provide hospital cashback to customers who have sought treatment at any nationally-accredited hospital in the regions where they operate. These innovations further the consumer’s awareness of AI-enabled insurance coverage and protection in general, in an otherwise underpenetrated marketplace.


Bismart is another example — an insurtech aggregator that allows customers to not only buy the best-in-class insurance products but also make claims directly from their portal as well. 

The biggest learnings for young insurtechs in this space from more mature markets, are about getting the basics right – having a single view of the customer, being able to launch rates and change pricing in real-time, offering customers a multichannel experience without requiring them to fill in the same information over and over again, and settling claims quickly without the need for multiple touchpoints.

Demand-driven models, built on sufficiently large data-sets will be instrumental in driving individual customisation at mass-scale for the sector at large.

webinar: AI for data-driven Insurers

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders and decision-makers on April 14, 2020.

We help young insurtechs, build and scale AI-driven products and solutions for last-mile environments. Reach out to us on hello@mantralabsglobal.com, to learn more.

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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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