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InsurTalks Podcast with Alex Jimenez: Now is the Time to Reevaluate Digital Customer Experiences

7 minutes read

The COVID-19 pandemic has brought upon an unprecedented change in our daily lives and routines. Consumer behavior is changing constantly. Lockdowns and social distancing have led to huge losses for businesses across industries. The world is heading towards an economic slowdown. Under these circumstances, organizations are facing many challenges to keep their businesses going. Insurers too are facing similar issues. Some insurance lines such as motor, travel, home have suffered a business loss due to low demand.

To understand the impact of this crisis, especially in the USA, we interviewed Mr. Alex Jiminez, Strategy Officer at Extractable from California, and learned more about creating better digital customer experiences in these testing times. 

Extractable is a strategic consulting, design, and data analytics agency focused on the future of financial services. His other recent experience includes leading technology strategic planning for the office of the CIO, at Zions Bancorporation, and managing Digital Banking and Payments Strategy and Innovation at Rockland Trust. Alex has been named to several industry influencer lists in the areas of FinTech, RegTech, Blockchain, InsurTech, Innovation, and Digital Marketing. He has been featured in the Irish Tech News and the Independent Community Bankers of America’s (ICBA) Independent Banker.

Connect with Mr. Alex Jimenez – LinkedIn

The excerpt from the interview:

The impact of COVID-19 pandemic in the financial services industry

What is the impact of COVID-19 pandemic in the financial services industry, and how is the industry responding to the ongoing crisis in the US?

In the wake of the current crisis, organizations are more focused on keeping the operation going, trying to set-up work stations for remote working, dealing with customers and working with them over digital platforms. But very few are focusing on the future which is preparing for the after-effects of this pandemic on the economy. 

In-person communication is still an important mode of interaction with customers in the US banking sector. But now the issue is how to provide good services to clients? Some of our customers are going to experience digital models for the first time. 

Organizations that have well-defined Digital Strategies and Customer-First approach will be able to provide good support to their customers. Organizations that are late into this space are more likely to face problems in the future.

[Related: The Impact of Covid-19 on the Global Economy and Insurance]

Changing customer preferences

How can companies reach out to their customers in this New Normal world?

We have already started to move towards a digital-centric world which is just going to accelerate. We will see businesses who have earlier ignored their digital capabilities will now build more on them. 

The first video call was invented in the 60s and was not so appreciated as everybody thought it was expensive and complicated. Today we have FaceTime, Zoom but adoption has not happened on a larger scale. But this will soon accelerate. Customers will be comfortable dialing into a video chat with their Insurance agent. 

I don’t believe there’ll be a New Normal. For example, in the US after 9/11 people thought that life will never get back to normal but except for rigorous security screening at the airports, there hasn’t been much change in the behavior. 

In Israel, amidst all the constant disturbance, people in Tel Aviv and Jerusalem are living normal lives. There’ll certainly be some specific changes post the pandemic such as more adoption of digital technologies, more focus on customer needs but I believe there won’t be an entirely new world with a drastic change in consumer behavior.  

The need for personalization

What are some Attention hacking lessons for Insurers operating in ‘the New Normal’?

We are moving towards the personalization of products in general. Generally in Life Insurance, we insure people based on their date of birth or medical history. But what if we insure people based on their behavior? If we did that, would people change their more risky behavior to get a better rate? A non-smoker can be given a better rate as opposed to a smoker. If we get down to individuality, saying that this is your individual (your own) rate; it makes a difference. 

There is a lot of data available and AI is needed to mine that data and derive analytics. Just by building a relationship with customers, we are not doing a great job with personalization. It’s important to apply a human touch to the communication which makes customers feel like you know them. Thus, retaining their attention.

Digital customer experience in Insurance

For the insurance industry, what steps can help in delivering the right digital customer experience in terms of UX and visual design?

A lot of organizations practice Design Thinking but Financial Services don’t. They are of the opinion that they know what is needed as they themselves are customers and they have data from the surveys. But that’s a wrong approach. Design Thinking is about empathy. It is important to get into the shoes of your clients to design better solutions.

To enhance digital customer experience, Insurers need a thorough understanding of users — who are the ultimate clients, their needs, what they expect from this experience, etc. After comprehending how they engage with technology and financial services, start venturing into the solution and test the solutions with actual users.

Innovations in the financial services industry

What technology-based innovations are being explored within the financial services industry? And, do you see AI playing a role in the short term? 

AI has already affected Financial Services in a positive way and will make it better. In insurance, IoT has been very impactful and will continue to be. Some applications have already been applied in reality like sensors in cars to detect speed and ensure that you are under the speed limit. This helps in getting reduced premiums. 

However, some basic processes are still done in the old school way of shuffling papers. Straight though-out processes have not yet happened. Now RPA is being applied to this but it is more like a band-aid. What is more important is how we can build processes through true automation with AI.

[Related: 5 Insurance Front Office Operations AI Can Improve]

Adoption of AI in Insurance

Speaking about more adoption of technologies, do you think there’ll be more investment in AI now?

Absolutely! We have already seen that investment in technologies like AI, cloud computing, quantum computing has been ramping up. Businesses will invest much more in AI than before. It might be for better decision making, underwriting, understanding the behavior of clients, etc. Also, from a marketing standpoint, financial services have never focused much before but will now invest in AI for this area too.

[Related: How is AI extending customer support during COVID-19 pandemic]

In your recent article in Extractable – “Deploying third-party financial service technology to mitigate crisis” you talk about what tech vendors are doing wrong. Please expand on how to encourage resources to be innovative change agents?

There were two points that I made in the article-

First is about what companies are doing incorrectly when it comes to innovation. Risk management is consulted only after developing the product. The product release is stalled until the legal compliances are adhered to. Instead, companies should involve the risk management at the beginning of the process (while defining the problem and solution). Involving risk management at every step of the innovation process will make it much easier to push out innovation.

The second was about vendor management. Many small vendors such as tech vendors, InsurTechs want to sell solutions to financial service companies but are often surprised by the tedious vendor management process. There’s a lot of documentation. Once the first process of selling is done, vendors should package the documentation in a way that when the next prospect asks for it, the due diligence package is ready to offer. 

Read article – Deploying third-party financial service technology to mitigate crisis 

Wrapping up

Alex shared interesting insights on how Design Thinking and Visual Design can create better digital customer experience. The design vertical at Mantra Labs too believes in the same and has designed UX for various applications for its customers. Here’s an article to understand the role of Customer Experience (CX) and User Experience (UX): Creating Amazing Digital Customer Experiences


AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com. 

Podcasts in this series:

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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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