Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(146)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(21)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

AI Use Cases for Data-driven Reinsurers

Across the Insurance expansile, a special fraction within the industry is notable for its embrace of new technologies ahead of others. For an industry that notoriously keeps a straggling pace behind its banking and financial peers, Reinsurance has conventionally demonstrated a greater proclivity for future-proofing itself. In fact, they were one of the first to adopt cat-modelling techniques in the early ’90s to predict and assess risk.  This makes perfect sense too — ‘Insurance for insurers’ or reinsurance is the business of risk evaluation of the highest grade — which means there are hundreds of billions of dollars more at stake. 

Front-line insurers typically practice transferring some amount of their risk portfolio to reduce the likelihood of paying enormous claims in the event of unforeseen catastrophe losses. For most regions of the World — wind and water damage through thunderstorms, torrential rains, and snowmelt caused the highest losses in 2019.

In the first half of 2019 itself, global economic losses from natural catastrophes and man-made disasters totalled $44 billion, according to Swiss Re Institute’s sigma estimates. $25 billion of that total was covered by reinsurers. Without the aid of reinsurance absorbing most of that risk and spreading it out, insurance companies would have had to fold. This is how reinsurance protects front-line insurers from unforeseen events in the first place.

Yet, protection gaps, especially in emerging economies still trails behind. Only about 42 per cent of the global economic losses were insured as several large-scale disaster events, such as Cyclone Idai in southern Africa and Cyclone Fani in India, occurred in areas with low insurance penetration.

Reinsurance can be an arduous and unpredictable business. To cope with a prolonged soft market, declining market capital and shaky investor confidence — reinsurers have to come up with new models to boost profitability and add value to their clients.

For them, this is where Artificial Intelligence and the sisterhood of data-driven technologies is bringing back their edge.


Source: PwC – AI in Insurance Report

AI Use Cases for Reinsurers 

Advanced Catastrophe Risk Modelling

Catastrophic models built on machine learning models trained on real claims data, and ethno- and techno-graphic parameters can decisively improve the authenticity of risk assessments. The models are useful tools for forecasting losses and can predict accurate exposure for clients facing a wide range of natural and man-made risks.

Mining Data for behavioural risks can also inform reinsurers about adjusting and arranging their reinsurance contracts. For example, Tianjin Port explosions of 2015 resulted in losses largely due to risk accumulation — more specifically accumulation of cargo at the port. Static risks like these can be avoided by using sensors to tag and monitor assets in real-time.

RPA-based outcomes for reducing operational risks

RPA coupled with smart data extraction tools can handle a high volume of repetitive human tasks that requires problem-solving aptitude. This is especially useful when manually dealing with data stored in disparate formats. Large reinsurers can streamline critical operations and free employee capacity. Automation can reduce turn-around-times for price/quote setting in reinsurance contracts. Other extended benefits of process automation include: creating single view documentation and tracking, faster reconciliation and account settlement time, simplifying the bordereau and recovery management process, and the technical accounting of premium and claims.

Take customised reinsurance contracts for instance that are typically put together manually. Although these contracts provide better financial risk control, yet due to manual administration and the complex nature of such contracts — the process is prone to errors. By creating a system that can connect to all data sources via a single repository (data lake), the entire process can be automated and streamlined to reduce human-related errors.

Risk identification & Evaluation of emerging risks

Adapting to the risk landscape and identifying new potential risks is central to the functioning of reinsurance firms. For example, if reinsurance companies are not interested in covering Disaster-related insurance risks, then the insurance companies will no longer offer this product to the customer because they don’t have sufficient protection to sell the product. 

According to a recent research paper, the reinsurance contract is more valuable when the catastrophe is more severe and the reinsurer’s default risk is lower. Predictive modelling with more granular data can help actuaries build products for dynamic business needs, market risks and concentrations. By projecting potential future costs, losses, profits and claims — reinsurers can dynamically adjust their quoted premiums. 

Portfolio Optimization


During each renewal cycle, underwriters and top executives have to figure out: how to improve the performance of their portfolios? To carry this out, they need to quickly assess in near real-time the impact of making changes to these portfolios. Due to the large number of new portfolio combinations that can be created (that run in the hundreds of millions), this task is beyond the reach of pure manual effort. 


To effectively run a model like this, machine learning can shorten the decision making time by sampling selective combinations and by running multi-objective, multi-restraint optimization models as opposed to the less popular linear optimization method.  Portfolio optimization fueled by advanced data-driven models can reveal hidden value to an underwriting team. Such models can also predict with great accuracy how portfolios will perform in the face of micro or macro changes.

Repetitive and iterative sampling of the possible combinations can be carried out to create a narrowed down set of best solutions from an extremely large pool of portfolio options. This is how the most optimal portfolio that maximizes profits and reduces risk liability, is chosen. 

Reinsurance Outlook in India 

The size of the Indian non-life market, which is more reinsurance intensive than life, is around $17.7B, of which nearly $4B is given out as reinsurance premium. Insurance products in India are mainly modeled around earthquakes and terrorism, with very few products covering floods. Mass retail sectors such as auto, health and small/medium property businesses are the least reinsurance dependant. As the industry continues to expand in the subcontinent, an AI-backed data-driven approach will prove to be the decisive leverage for reinsurers in the hunt for new opportunities beyond 2020. 

Also read – Why InsurTech beyond 2020 will be different

Cancel

Knowledge thats worth delivered in your inbox

Design Systems: Building and Maintaining Consistent UI/UX

In the world of product design, consistency is the cornerstone of delivering a seamless user experience. As digital products evolve and scale, the challenge of maintaining design consistency across multiple platforms, teams, and touchpoints becomes more complex. This is where design systems come in. A design system is a comprehensive set of guidelines, components, and tools that help designers and developers create a consistent and cohesive user interface (UI) and user experience (UX). From a designer’s perspective, building and maintaining a design system requires a strategic approach to ensure scalability, flexibility, and alignment with business goals.

Why Design Systems Matter?

Design systems are more than just style guides or pattern libraries. They offer a unified language for design and development, bridging the gap between creativity and functionality. Here are a few reasons why they are critical:

  1. Consistency Across Products

      One of the primary goals of a design system is to ensure uniformity across different platforms and devices. When users interact with a product, whether on a website or a mobile app, they expect a consistent experience. A well-implemented design system ensures that visual elements, interactions, and behaviors are aligned, offering a familiar and intuitive experience for users.

2. Faster Iteration

      With a design system in place, teams can reuse components and patterns rather than reinventing the wheel for each new feature. This speeds up the design and development process, allowing teams to focus on solving new problems instead of reworking existing designs. For designers, it means less time spent on repetitive tasks and more time dedicated to innovation.

3. Scalability

     As products grow and new features are added, maintaining a coherent UI/UX without a design system can become chaotic. A design system provides a scalable framework, enabling teams to add new elements or modules without compromising the overall consistency of the product.

4. Improved Collaboration

      A design system fosters collaboration between designers, developers, and other stakeholders by providing a shared language and reference point. This ensures that design decisions are communicated effectively, reducing friction and misunderstandings between teams.

Building a Design System: A Designer’s Perspective

Designing a system from scratch can seem like a daunting task, but with a clear roadmap and understanding of core principles, it becomes a powerful tool for creating scalable and user-centered products.

1. Define the Core Principles

Before diving into visual components, it’s essential to establish the core principles of your design system. These principles serve as the foundation and guide decision-making throughout the process. Ask yourself:

• What are the key values of your brand?

• How should the design reflect the needs and emotions of users?

• What goals are you trying to achieve with your design system (e.g., accessibility, flexibility, innovation)?

   For instance, if accessibility is a top priority, you’ll need to ensure that your design system accommodates users with different abilities, by providing color contrast guidelines, clear typographic hierarchies, and accessible components.

2. Create a Unified Visual Language

Once the principles are established, the next step is creating a cohesive visual language. This includes defining elements such as:

Color Palette: A well-structured color palette ensures harmony across different UI components. It’s crucial to include primary, secondary, and accent colors, along with usage guidelines to avoid inconsistency.

Typography: Define a typography system that establishes hierarchies and clarity, including font styles, sizes, line heights, and spacing. Consistent typography helps users navigate content effortlessly.

Spacing and Grids: An established grid system ensures a balanced layout and harmony between different UI elements. Proper spacing guidelines prevent crowded or misaligned components.

Iconography and Illustrations: Design guidelines for icons and illustrations help maintain visual consistency. Icons should follow a standard style (outlined, filled) and size, ensuring they align with the overall visual language.

(I

Iconography and Illustrations: Design guidelines for icons and illustrations help maintain visual consistency. Icons should follow a standard style (outlined, filled) and size, ensuring they align with the overall visual language.

Image Source: https://designerup.co/blog/10-best-design-systems-and-how-to-learn-and-steal-from-them/)

3. Design Reusable Components

A design system is incomplete without reusable UI components. These components include buttons, forms, modals, and navigation elements that can be used across various features and pages. Each component should be:

Modular: Components should be flexible and adaptable for different contexts. For example, a button should have variations for different states (hover, active, disabled) and types (primary, secondary, destructive).

Documented: Each component needs comprehensive documentation that explains how it should be used, its variants, and the rules governing its application. This ensures consistency when different team members use the same component.

4. Implement Accessibility Guidelines

Accessibility should be at the forefront of every design system. As a designer, consider:

Color Contrast: Ensure sufficient contrast between text and background for readability.

Keyboard Navigation: Ensure that interactive components are navigable via keyboard for users who rely on keyboard shortcuts.

Responsive Design: Components should be responsive, adapting seamlessly across different screen sizes and devices.

Assistive Technologies: Design components should be compatible with screen readers and other assistive technologies to provide an inclusive experience.

Maintaining a Design System: Evolving with the Product

Once a design system is in place, the work doesn’t stop there. Maintaining it requires ongoing effort as the product evolves and new design challenges emerge. Here’s how designers can ensure the design system remains relevant and effective:

1. Regular Audits and Updates

A design system should be treated as a living document that evolves with the product. Regular audits are necessary to identify outdated or unused components. Designers should collaborate with developers to ensure new features are aligned with the existing design system, and updates should be documented to avoid fragmentation.

2. Version Control and Documentation

Version control is essential for keeping track of changes in the design system. Documenting why and how changes are made ensures that the design system grows in an organized manner. It also helps onboard new team members quickly, as they can reference updated guidelines.

3. Foster Collaboration Across Teams

Designers need to maintain an open line of communication with developers and other teams using the design system. Tools like Figma, Sketch, and Zeplin allow designers to share components and guidelines in real-time, fostering collaboration and ensuring that everyone is aligned.

Some well-known examples of design systems that have set benchmarks in the industry:

1. Google Material Design

  • URL: Material Design
  • Overview: Google’s Material Design is one of the most comprehensive design systems available. It provides guidelines on how to create visual, motion, and interaction design across platforms and devices. Material Design is known for its grid-based layouts, responsive animations, and depth effects like shadows and lighting.
  • Features:
    • Comprehensive color palettes, typography, and iconography.
    • Extensive component library (buttons, cards, etc.).
    • Customizable UI components for both web and mobile applications.

2. Salesforce Lightning Design System

  • URL: Salesforce Lightning
  • Overview: Salesforce’s Lightning Design System provides a comprehensive library of UI components and resources to help build applications on the Salesforce platform. It ensures a consistent experience across Salesforce products and enables developers to create custom apps easily.
  • Features:
    • Ready-to-use components, design tokens, and code snippets.
    • Comprehensive accessibility guidelines to ensure inclusivity.
    • Detailed documentation for developers to integrate components into Salesforce applications.

3. Microsoft Fluent Design System

  • URL: Fluent Design
  • Overview: Fluent Design is Microsoft’s design system, created to provide an engaging and scalable UI across all Microsoft products. It is heavily focused on interaction, animation, and motion while ensuring accessibility across various device ecosystems.
  • Features:
    • Focus on depth, motion, and light to create modern and interactive UIs.
    • Cross-platform components supporting web, desktop, and mobile applications.
    • Accessible components with detailed guidelines for developers.

Conclusion

Building and maintaining a design system is an investment that pays off in the long run. It ensures consistency across your product, improves collaboration between teams, and allows for faster iteration and scalability. For designers, a design system is more than a collection of reusable components — it’s a framework that enables you to create user-centric products with confidence and clarity. By embracing modularity, documentation, and accessibility, you’ll ensure that your design system remains a valuable asset as your product grows.

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot