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Rethinking Insurance: Data Modelling's Crucial Role in Tech Transformation

December 18, 2023

The insurance industry, with its rich history, is rapidly aligning itself with the ongoing digital transformation and adoption that has already reshaped various industries.

The industry has undergone a transformative shift, notably marked by the incorporation of data analytics, APIs, and new quote-and-bind platforms. This change is underscored by the adoption of platforms employing targeted marketing, streamlined distribution, data analytics, and an intuitive user journey. This integrated approach has played a pivotal role in optimising the entire process - from product build and underwriting to distribution, operations, and ongoing policy lifecycle management. Moreover, the industry has embraced data analytics, witnessing companies leverage AI and machine learning to assess risk, detect fraud, and personalise policies. This technological progression extends to customer interfaces, featuring the introduction of chatbots and mobile apps, enhancing accessibility for policyholders in obtaining information, quotes, and filing claims. The application of digital tools to facilitate relationships between underwriters and brokers/policyholders is driven by a shared goal: elevating service levels and securing more business opportunities.

As the digital transition gains momentum, with an increasing number of policies being placed online each month, brokers seek platforms and portals that are not only simple to navigate but also easy to use and designed with intuitive interfaces. Simultaneously, underwriters insist on a sophisticated platform with functionality capable of handling the inherent complexities of the policy lifecycle and diverse books of business. Amidst these considerations, factoring in human judgement and the necessity for effective communication, the journey to propel B2B insurance marketplaces from their current state to where they ought to be is undeniably challenging.

The industry’s lag behind the banking sector, for example, can be attributed to several factors, including:

Legacy Systems: Numerous insurance companies continue to depend on systems built with outdated programming languages, necessitating significant investments for system upgrades. For instance, the global workforce of COBOL programmers (approximately 2 million), illustrates the scarcity of expertise in maintaining and enhancing systems written in this language. In stark contrast, modern programming languages like Javascript boast a worldwide workforce of around 14 million programmers. This significant disparity highlights the challenges in recruiting and maintaining talent proficient in older languages, impacting the speed of development and integration capabilities, especially when striving to connect with other systems through APIs. This scarcity of skilled engineers in legacy languages adds complexity and time to the process of incorporating new features, emphasising the recruitment challenges faced by companies maintaining systems with outdated technologies.

Complexity: Older systems are more stringent in terms of development criteria and that doesn’t align well in terms of compatibility with the multitude of products, regulations, and underwriting criteria to abide by. Newer technologies are more flexible to accommodate in terms of API integration from back-office system to external broking portal, for example.

Talent shortage: The number of experienced/senior underwriters is declining and with young underwriters relatively scarce, the industry is running out of time to effectively train up new recruits, and better equip those already in the field.

An obvious way to get more out of existing resources is to leverage the latest technology, and this starts with aligning incentives. But what do we mean by this? In essence, any new initiative should contribute positively to insurers’ and MGA’s EBITDA. This involves optimising current teams by leveraging automation to enable underwriters to handle a higher volume of Gross Written Premium (GWP) per underwriter. Technology can also empower Business Development Managers (BDMs) to potentially double their capacity by focusing on brokers with a higher likelihood of conversion. This targeted approach allows BDMs to manage a larger number of brokers efficiently. Consequently, the need for data entry roles diminishes, freeing up resources for high-value functions like marketing campaigns and product development. In simple terms, both underwriters and BDMs can double their capacity, processing more premiums and increasing revenue without expanding the team.

We know that building scalable insurance software that is fit-for-purpose, API-ready and with a seamless user experience is difficult, expensive and time-consuming. However, you don’t have to look far to see that it can be done, and moreover, become the norm. Banking and fintech industries offer two such examples, and valuable lessons for insurance. The banking sector has embraced open banking APIs, enabling seamless data exchange between financial institutions, fintech companies, and customers. Fintech companies have thrived by adopting a digital-first approach, making it imperative for participants to invest in user-friendly digital interfaces. Both sectors also embrace an agile development mindset that allows companies to adapt quickly to changing market dynamics, and this was achieved well over a decade ago. The insurance industry is quickly catching up in terms of improving data accessibility, interoperability and how to iterate and evolve rapidly.

Thankfully, with the emergence of digital insurance marketplaces and aggregators simplifying the experience for consumers and businesses, insurers are starting to spend significant amounts on R&D in order for their systems to be able to integrate via APIs, fostering partnerships with insurtechs and enabling a more interconnected insurance ecosystem. AI and automation, although still in their infancy, also continue to be at the forefront, making underwriting, claims processing, and fraud detection more efficient. But there is clearly a lot of room for opportunities.

At the heart of this digital transformation lies a critical concept: data modelling. For brokers and underwriters navigating these platforms, understanding the significance of data modelling is like having a reliable compass in uncharted waters. In simple terms, let's break down what data modelling is and why it matters for professionals in the insurance sector.

Data modelling is like creating a roadmap for information. In the context of insurance, data modelling involves structuring information about policies, clients, and risks in a way that makes sense and is easy to use. It helps keep track of policies, client details, and risk factors, making it easy for brokers and underwriters to find the information they need quickly. In short, accurate data modelling allows for the precise pricing of risks and correct management of a policy as it progresses through its lifecycle while keeping the user journey very simple.

To go a little deeper, the foundation of effective data modelling begins with a comprehensive understanding of insurance data structures. With the wide array of products, each with unique features, pricing structures, and underwriting requirements, data modelling enables insurance companies to catalogue these products systematically, creating a blueprint that links data from initial quote requests through to policy issuance. This transparent and traceable customer journey not only enhances customer satisfaction and engagement but also facilitates regulatory compliance by maintaining an audit trail of all relevant transactions.

Data modelling empowers insurance companies to enhance predictive analytics and customer engagement through data-driven insights derived from transaction histories. By organising and analysing data, insurers can better understand customer behaviour and preferences, enabling them to offer tailored insurance products and services. A well-structured data model ensures an exceptional customer experience, transparent user journeys, and streamlined policy lifecycle management.

Identity and Access Management (IAM) is another critical aspect of data modelling. Insurers must implement data structures that allow for intricate definitions of user roles, access levels, and hierarchical permissions. By doing so, insurance companies can control access to sensitive data, maintain data accuracy, and enforce security protocols effectively. The incorporation of comprehensive logging and audit data models further enhances transparency, ensuring regulatory compliance and providing a detailed record of data access and modifications. Implementing state-of-the-art IAM within data modelling not only fortifies the control over sensitive data but also establishes a resilient defence against evolving cyber threats.

A well-structured data model underpins the user journey by providing a seamless experience for policyholders and facilitating interactions at various touchpoints. With an integrated data model customers can receive quotes, purchase policies, and file claims with ease. For underwriters, they can automatically calculate premiums, adjust coverage, and generate renewal notices, streamlining the entire process. This not only saves time and resources but also minimises the risk of errors, leading to higher customer service levels. Furthermore, the model ensures that relevant information is readily available and consistently presented, resulting in an exceptional customer experience.

Employing the correct data model alongside integration with data enrichment providers and other third party applications via APIs are crucial components for ensuring a seamless and efficient policy lifecycle and forms the backbone of operational success. It is helpful to take a brief look at each stage in turn to see what this means in context.

New Business Quotations: Correct data modelling begins with the accurate capture of data during the New Business Quotations stage making it easier to correctly and quickly capture the information from brokers/policyholders while enriching it with helpful risk information providing a solid foundation for subsequent stages in the policy lifecycle.

Policies: Once a policy is activated a secure and complete audit trail reflecting any changes is kept for each policy that would serve the basis for its entire lifecycle while maintaining a record.

Mid-Term Adjustments (MTAs): Any changes to a policy is easy with a correct data model making an MTA process quick and painless.

Renewals: Using data models and new technologies can help the holding insurer retain as much of the business as possible by staying ahead of the curve with issuing on time and accurate renewal invitations by a seamless automated experience.

Expired Quotations: Some quotations may not transition into policies, leading to expired quotations. A robust data model captures and categorises this information, providing valuable insights into the reasons behind non-conversion. Analysing this data can inform strategic decisions for refining underwriting processes and understanding how to win more business within appetite and increase GWP.

As we have seen, the journey towards digital transformation in the insurance industry is an ongoing journey, marked by significant strides and evolving challenges. The integration of data modelling with the latest in technology and platforms points the course for insurers through the complex landscape of products during the user journey. By focusing on a comprehensive understanding of insurance data structures, insurers can streamline their operations, enhance customer experiences, and stay resilient in the face of evolving market dynamics.

As we contemplate the future, it's evident that leveraging sophisticated data models is not merely a technological choice but a strategic imperative. These models, meticulously constructed, form the backbone of a seamless policy lifecycle, from initial quotations to mid-term adjustments, renewals, and beyond. The power of data-driven insights derived from these models empowers insurers to adapt rapidly, fostering customer satisfaction and compliance.

The insurance industry, much like its counterparts in banking and fintech, is quickly on the path to embrace an agile development mindset, drawing valuable lessons from open banking APIs and digital-first approaches. By breaking down silos, fostering partnerships, and investing in user-friendly digital interfaces, the insurance sector has embarked on a journey of rapid iteration and evolution.

In the broader context, the road to a fully interconnected insurance ecosystem is paved with challenges, including the upgrading of legacy systems, and an increase in the number of insurtech partnerships. However, armed with the right data models, insurers have the tools to navigate this road with agility, ensuring a future where customer-centricity, financial performance and innovation harmoniously coexist. As the industry continues its digital transition, the role of data modelling and adoption of the latest technologies will stand out as a critical driver of success, steering insurers towards a horizon where technology seamlessly intertwines with the art of risk management.

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