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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

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The Essence of User-Centered Design: A Dive into Fundamental Principles

In a digital world where user experience reigns supreme, crafting designs that resonate has become a mission. Enter User-Centered Design (UCD), a philosophy placing users at the core of the creative process. In this exploration, we’ll delve into the fundamental principles of User-Centered Design and understand why they are the keystones of successful interfaces.

User-Centered Design

Introduction:

Imagine navigating a website seamlessly, effortlessly finding what you need. That experience is no accident but the result of intentional design. User-centered design (UCD) is the compass guiding designers toward creating interfaces that users not only navigate but embrace.

1. Empathy is Key:

  • Incorporate for a better approach: Start by stepping into the shoes of your users. What are their pain points? What delights them? By empathizing, designers gain insights that drive user-focused design decisions.

2. User Involvement Throughout the Design Process:

  • Real-life example or statistic: Apple’s iterative design process involves user testing at every stage. This constant involvement ensures that their products align precisely with user needs.

3. Holistic Approach to Design:

  • Visual content: Picture your design not as isolated screens but as a cohesive journey. Use diagrams to illustrate how each component fits into the larger user experience ecosystem.

4. Usability is Non-Negotiable:

  • Case studies or examples: Consider the success of Google’s homepage. Its simplicity and efficiency showcase the power of a user-centered approach, emphasizing usability.

5. Accessibility for All:

  • End with a clear call-to-action: Make your designs accessible. It’s not just a legal obligation; it’s an ethical imperative. Ensure your interfaces are usable by everyone, regardless of ability.

6. Consistency Across the Interface:

  • Formatting for readability: Consistency is not just a design principle; it’s a readability strategy. Use bullet points for clarity and short paragraphs for easy consumption.

7. Flexibility and Customization:

  • Inclusive language: Users are diverse, so should your designs be. Incorporate flexibility and customization options. This ensures your interface caters to a broad range of preferences.

Why User-Centered Design Matters:

A. Enhanced User Satisfaction:

  • Feedback mechanism: Prioritize user satisfaction. A satisfied user is an engaged user. Welcome reader input and questions to keep the conversation alive.

B. Reduced Learning Curve:

  • Clear call-to-action: Minimize frustration. Make your interfaces intuitive, reducing the learning curve. Invite users to explore with a clear call-to-action.

C. Increased Engagement and Retention:

  • Visual content: Engaging interfaces retain users. Visualize engagement with appealing images or infographics. Showcase how user-centered designs reduce bounce rates.

D. Effective Problem Solving:

  • Tangible proof: Case studies offer tangible proof. Explore how UCD’s iterative process allows for effective problem-solving. Real-world examples bring these concepts to life.

Conclusion:

In the grand tapestry of digital design, User-Centered Design is the thread weaving functionality, aesthetics, and user satisfaction into a seamless whole. By embracing these principles, designers transform mere interfaces into user-centric experiences. So, as you embark on your design journey, remember: User-centered design isn’t just a philosophy; it’s a commitment to excellence. Design with the user in mind, and success will follow.

About the Author: Mehul Chauhan is a seasoned Senior UI/UX Designer at Mantra Labs. With a deep understanding of design principles and a keen eye for detail, he brings creativity and innovation to every project he touches. When he’s not busy perfecting digital interfaces, you can find him seeking inspiration in art galleries or exploring the latest design trends across various industries.

Further Reading: Unveiling the Art of Emotional Design

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