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10 Most Impactful AI-based Insurance Innovations of 2019

5 minutes, 5 seconds read

The year 2019 has been a benchmark in insurance innovations that brought in new value propositions to the industry. What’s more remarkable is — both traditional Insurers and Insurtechs are striving to offer simple, convenient, and value-added customer-centric products coupled with technology initiatives. Here are 10 noteworthy insurance innovations that shaped the industry this year.

  1. Augmented Intelligence
  2. AI-based Smart Automation
  3. Digital Insurance Broker
  4. Services Beyond Insurance
  5. Blockchain in Reinsurance
  6. Unconventional Partnerships
  7. Understanding Customers and Delivering Tailored Products
  8. Insurance on Demand Services
  9. Risk Intelligence
  10. Customer Education

10 Most Impactful Insurance Innovations of 2019

According to a recent EFMA-Accenture report, the insurance industry has witnessed growth in digital sales & services, Artificial Intelligence trends — especially machine learning and natural language processing (nlp), big data and analytics, cloud, intelligent automation, and blockchain.

However, insurance players are not just adding convenience through technology but also understanding the ‘actual’ customer needs and developing the products accordingly. Let’s discuss the impactful insurance innovations with their use cases in detail.

#1 Augmented Intelligence

While most insurers are leveraging AI to understand customers and their requirements; another idea that hits the list is to complement the knowledge of insurance employees during sales pitches and customer services. 

For example, Zelros is Augmenting intelligence of sales and customer representatives through real-time best product recommendations, advisory, and pricing based on studying the customer profile.

Zelros - augmented intelligence - insurance innovations

Similarly, Nippon Life Insurance Company has introduced an AI-powered TASKALL tablet for its sales representatives. This tablet identifies suitable prospects from the set of entire salesforce activities, thus enhancing the sales and customer representatives’ services. 

#2 AI-based Smart Automation

Smart automation corresponds to deploying intelligent technologies to gain massive operational efficiency and at the same time create value for the end customer. 

For example, South Korean Kyobo Life Insurance Co. Ltd. has developed an AI system BARO (Best Analysis & Rapid Outcome) to automate underwriting. The system uses NLP to allow sales and customer interactions in natural language.

In the same way, Religare incorporated AI-based chatbot in their workflow. Through this bot, the company has automated a number of operations like customer query resolution, customer engagement, and lead and ticket management.

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#3 The Digital Insurance Broker

In 2018, in the US alone, nearly 1.2 million people worked for insurance agencies, brokers, and insurance-related enterprises. This indicates the prominence of the brokerage in insurance. Brokers might not be directly involved in product development, risk evaluation, etc.; but they play a pivotal role in insurance distribution. 

For example, Gramcover, an Indian composite insurance broking firm is leveraging mobile technologies to minimize the inefficiencies and transaction costs in distributing micro-policies.

Also read – The case for a digital brokerage

#4 Beyond Insurance

The year 2019 also witnessed the entry of technology giants like Alibaba entering the insurance space, and people welcoming them made the competition even more fierce. The World Insurtech Report 2019 states that nearly 30% of customers are interested in buying at least one insurance product from BigTech firms like Google, Apple, Facebook, Amazon, and Alibaba. 

Insurers have thus realized to embrace the ecosystem-based digital economy to deliver richer customer experiences. AG Insurance’s Phil at Home is an example of ‘beyond’ insurance services to support customers in their day to day life. The app provides house maintenance services like plumbing, electricity, etc. along with medication reminders, food delivery, etc. to its elderly customers.

Also read – The Belgian Insurance Landscape

#5 Blockchain in Reinsurance

Blockchain or distributed ledger technology (DLT) brings transparency to a range of insurance processes along with the secure sharing of information. The innovative use of blockchain in insurance is to reduce redundant efforts. 

For example, the US-based Aon Benfield along with partners have developed a blockchain-powered reinsurance placement solution to bring brokers and reinsurers on a collaborative platform.

Similarly, the Hong Kong Federation of Insurers in collaboration with CryptoBLK developed MIDAS (Motor Insurance DLT-based Authentication System) to authenticate motor insurance policy documents across the network in real-time.

#6 Unconventional Partnerships

Insurers’ partnerships with Insurtechs, Fintechs, and external players are presenting an opportunity to explore new customer base, test different business models, and get access to new technology frontiers. 

For example, AXA partnered with ContGuard, which provides real-time cargo tracking services. Their product — Connected Cargo Solution gives customers 24/7 monitoring and data to AXA’s risk engineers to develop loss prevention plans. This also helps underwriters to quote the price with increased accuracy.

#7 Understanding Customers and Delivering Tailored Products

Addressing the customers’ demand for personalized services, Insurers have started applying AI to understand their sentiments and requirements. They have realized that real-time digital services unlock values for both carriers and customers.

For example, the UK-based Bought By Many helps people find insurance for uncommon assets like pets, shoes, gadgets, etc. The company also negotiates with insurers for the best deals.

#8 On-demand Insurance models

The World Insurtech report 2019 reveals that nearly 41% of customers are ready to consider usage-based insurance and 37% want to explore on-demand insurance coverage. While usage-based insurance models provide as-you-go premium coverage based on customer’s potential for risky behavior; on-demand insurance allows customers to get cost-effective and convenient coverage depending on their needs.

For example, The Dinghy is an app-based on-demand freelancer insurer. It is also the world’s first on-demand professional indemnity insurance covering public liability, business equipment, legal expenses, and cyber liability.

#9 Risk Intelligence

Insurers are deploying machine learning models for risk assessment and mitigation. It not only makes the underwriting more accurate but also boosts profits by diminishing risks.

For example, ZestFinance uses automated machine learning tools to correlate current and traditional data. It helps to effectively gauge risks and outreach potential new customers.

#10 Customer Education

Pricing still presents a bigger competitive advantage than many other insurance features. Accenture’s 2019 Global Financial Services Consumer Study states – more than 75% of customers can share their personal information for better prices. 

Therefore, educating customers about potential risks isn’t sufficient. Coupling this information with available products’ prices and benefits is a must. For example, Jerry, a California-based personal insurance marketplace checks if the user is paying the best price for the insurance services. Based on an initial questionnaire, their AI-powered tools takes roughly 45 seconds to compare quotes from leading insurers and suggest optimum rate to the user.

Also read “Top 5 smartest AI-powered machines on earth.”

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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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