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InsurTalks Podcast with Steve Tunstall: The Role of Insurance in Restoring SMEs

8 minutes, 40 seconds read

The impact of COVID-19 has disrupted the trade and supply chain across the world and brought the world economy into a tizzy. Small and Medium Enterprises are especially in a difficult situation. They are facing huge business loss, cash crunch, and some even bankruptcy. Insurance will have a crucial role to play within SMEs in the post-pandemic world. 

To understand the importance of Insurance for SMEs and how various industries should pivot their mitigation strategies towards long term sustainability, we have Mr. Steve Tunstall, CEO and Co-founder of Inzsure.com, Singapore. 

The Inzsure platform is designed to transform the global commercial insurance industry by providing SME customers, initially in Singapore, Malaysia, and The Philippines. 

Steve has over thirty years of experience in owning, running, and future-proofing companies. He has been CEO, Managing Director, or equivalent in seven companies in four countries managing teams of up to 500 employees and based in Asia for over 20 years. Steve is also a contributing author to The InsurtechBook and author of “RISK and the Asian CEO” published on Amazon Kindle in 2016. He has deep domain knowledge in Insurtech, Fintech, commercial insurance, compliance, risk, and crisis management. He has been featured in the Top Global Influencer lists of Rising, InsurtechNews, Richtopia, and Onalytica in the areas of Insurtech, Fintech, and Blockchain. 

Connect with Mr. Steve Tunstall – LinkedIn

Here’s the excerpt from the interview:

The Impact of COVID-19 on SMEs in Asia

What’s the magnitude of the impact of COVID-19 in small and medium scale businesses — both globally and Asia specifically?

Steve: The entire world is facing the consequences of the current pandemic which is affecting everybody with no exceptions. 

Some sectors like the hospitality and travel industry have been hit the most. Along with these, service providers and manufacturers have also been affected. Oil industry unexpectedly also saw an all-time low in this crisis. The Global Supply Chain was an obvious sector to get disrupted. The supply chains have become shorter and duplicate. The whole concept of Just-in-time has gone for a toss. SMEs in those affected industries need to rethink their business and close down if necessary. 

The time is tricky and if there’s a short lock-down period, then it will have grave consequences to humankind. Massive spikes in infections will lead to a huge overload on the healthcare system. It’ll create a painful situation for medical professionals where they’ll have to make difficult life-death decisions based on the facilities available. 

However, on the other hand, longer periods of lockdowns will suffocate GDP and damage businesses. Most SMEs can survive if they are in the hot sectors. Once the lock-downs extend more than 2-3 months, it’ll be traumatic to the global GDP. We have already lost 25% of the global GDP. A lot of businesses might go bankrupt and the government cannot bail out everyone. 

These are gloomy times, but business managers and business owners need to think about how to pivot their business and find some sort of viable solution for this.  

The Rise of Digital Insurance Models

Insurers are taking the distribution process online. How are the Insurers adjusting to this new model and how has the customer response been in Singapore?

Steve: Transition towards online sales is a moving target. It has been happening for quite some time now and will accelerate all the more. In the UK around 70-90% of insurance policies are sold online before COVID-19 outbreak. If we split Life and Non-life and further split non-life into Personal and Commercial Insurance, there are three broad buckets-

Life Insurance- This line of insurance is not bought but rather sold. It’s a process to educate people and has a long sales gestation period. It involves a lot of interaction between insurance sales agents and an individual. Even big Life insurance companies are dependent on agents for sales. 

Personal Insurance- This line includes health, travel, motor, property insurance which is mostly sold online. Wealthier economics tend to have more online stuff than developing ones. It’s a bit patchy. But in personal insurance lines, many policies can be bought online in many countries.

Commercial Insurance- This line is the slowest of the three to adopt technology particularly the intermediaries. SMEs should be a good target for online but we have seen very little traction in Asia. Large companies are much slower in the adoption of digital technology and rely on face-to-face interaction with brokers. 

Covid-19 has become an accelerator for online especially for Life and Personal Insurances. Broadly speaking, 80% of the personal and life insurance are standardized. Only 20% need underwriting input. In Commercial lines, 20% is commoditized and 80% is bespoke. It is still a long journey. We have already seen insurance being sold online in the US and Europe and seem to go ahead in Asia. 

Many Insurers have been resisting online and commoditization for years. But giving customers choice, trust and transparency is the way to improve overall penetration in Insurance. 

The Importance of Insurance for SMEs

Since the pandemic started, fewer businesses (especially SMEs) are seeking insurance because of the loss of cash flow. How do you think your platform could help SMEs in this current situation?

Steve: It’s a common human tendency that you don’t need an umbrella during a light shower so you don’t buy one. But when the rain is hammering down, you go buy one only to find out that shops have run out of them. There are gaps in the knowledge about insurance. Not only within SMEs but also many businesses. 

In Asia, there’s less insurance required by the law and hence insurance does not tend to sell much. It’s the discerning and more naive one who gets sold insurance. The issue is that people do not know why insurance is a good thing and should be made a priority. Not all types of insurance perhaps, but businesses need to look at appropriate insurance which is tied to risks holding on their balance sheet. For example, fire is a big risk. Maybe not for a co-working space where data is on the cloud but for traditional businesses, you need to have insurance. 

Insurance in the New Normal

What are some new business models that Insurance Carriers are considering to meet the expectations of life in ‘The New Normal’? More specifically, where is the new business going to come from, for Insurance, over the next two years?

Steve: Around 30 years ago, businesses had their own properties for which they would need a cover, their machinery, they would operate out of a premise. But these days, most businesses do not own property, they are working in rented premises and have data on the cloud. 

There’s been a shift away from physical assets towards liabilities like loss of data, hacking, legal and regulatory obligations. All these different liability types are growing exponentially which creates a lower demand for property insurance. 

The traditional property and casualty insurance relies on historical data for calculating premiums. But for these emerging liabilities, it is difficult for insurers to get their head around its implications. Taking Cyber insurance policy for example. If businesses are not able to link the loss incurred due to cyber hacking, then insurers won’t payout. If an amazon web service goes down for the entire building, other businesses also have faced losses that accumulate losses to other companies as well. This accumulation of loss is worrying the CEOs now. This could be a huge opportunity for insurers to address these emerging liabilities in a meaningful way.

Speeding-up Claims during COVID-9 crisis

The pandemic has put a lot of pressure on health claims due to the increase in the volume of claims. What do Insurers need to do to speed-up their claims processes?

Steve: Out of all the processes in the insurance, claims appear to be the most painful and complained about. Surely, there will be an increase in claims related to COVID-19. In the US a typical COVID claim is looking somewhere between $20,000 to $100,000 but in Asia, it is much more bottom of that range. 

But on the other hand, another effect of COVID-19 is that since so many medical facilities around the world have seen a massive decline in regular doctor visits and elective surgeries. Therefore, there has been a reduction in the claims for other health ailments. We will see some of it coming in the upcoming months, probably in Q3 and Q4. For now, it has brought a balance in the number of claims.

Technology trends post COVID-19

How can technology help in sustaining the Insurance business and what are upcoming technology trends? Also, what industry will expect from technology service providers?

Steve: I believe that all the technology that is needed for insurers to work efficiently and perfectly online is already available. What is most needed is a huge change in mindset amongst the insurers. As an industry, people who build the products should not be separated from people who sell the products. 

On the customer side, insurance is not a product where you get instant gratification. Knowing the importance of insurance for SMEs, appropriate education about risk management can help. The change in mindset will impede the implementation of technology. 

Also read – 10 Most Impactful AI-based Insurance Innovations of 2019

Digitizing Insurance Processes

COVID-19 will propel insurers to increase the digitization of their operations and interactions with clients. We may also see insurers scaling back on their physical office networks and moving more people to remote working. More focus will fall on the automation of processes for greater cost efficiencies and resilience. What, according to you, are the crucial insurance processes where automation will disrupt first?

Steve: It depends upon where you are in the supply chain. The more insurers can automate their internal processes, the better. Underwriting is an area where AI plays a crucial role in making this process easy and cost-efficient. 

For insurers, when it comes to back-office functionality, cost-cutting will be a high priority due to the COVID-19 crisis. Technology can bring more efficiency to the intermediary processes making adoption of insurance for SMEs easier.

Also read – 5 Insurance Front Office Operations AI Can Improve

AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out FlowMagic— an AI-driven platform for Insurer workflows and Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us 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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