Digitization as the key to underwriting evolution

The emergence of advanced technology presents a significant opportunity for the evolution of underwriting practices. Digitisation, particularly through the implementation of underwriting workbenches, has already improved underwriting quality and further professionalised the underwriter’s role. An underwriting workbench is a digital platform that uses automation and AI to streamline the insurance underwriting process by centralising data, enhancing risk assessment and supporting better decision-making.

Furthermore, it is crucial for enhancing consistency and transparency within insurance organizations, ensuring a more standardised and reliable approach to risk assessment and pricing. Additionally, improved reporting capabilities enable more informed management decisions, supporting strategic planning and operational efficiency.

The journey toward digital transformation in underwriting is not without its obstacles. Insurers face numerous challenges, including legacy systems that pose integration difficulties and hinder the adoption of new technologies. Inefficiencies resulting from manual data entry and fragmented workflows, along with data silos that prevent a holistic view of risk, contribute to high operational costs and a lack of automation.

To address these challenges, Sollers is currently supporting London Market insurers in implementing several underwriting workbenches. These initiatives aim to improve the digital work environment, which can often be impacted by legacy systems, fragmented platforms, and complex workflows that manage data from multiple sources.

A well-designed underwriting workbench addresses these issues by automating data acquisition from multiple channels, utilizing AI-driven extraction and data standardization, and enriching data in real-time through integrations with external entities. It also centralizes the entire process in one place, allowing all parties to monitor the status of individual items while maintaining a comprehensive view of all submissions being processed.

The solutions available in the market are often based on flexible and scalable platforms such as Appian or Salesforce, which allow the development of customised solutions tailored to the specific needs of individual insurers. There is also a group of off-the-shelf platforms, such as Send or Optalitix, that offer a rich set of key OOTB capabilities that address the needs of specialty insurers in specific markets. What's more, key core platform vendors such as Guidewire see a growing need among their customers and are building the required capabilities or strategic alliances with key workbench vendors.

While underwriting workbench implementations are often multi-faceted, with each insurer and underwriter having its own needs and requirements, Sollers identifies the following key objectives:

• establishing a single point of data entry for the entire underwriting process,
• enabling comprehensive tracking of all underwriting stages on a centralized platform,
• simplifying and automating underwriting processes, freeing up underwriters’ time to focus on complex risk assessment,
• enhancing reporting quality and providing deeper insights, enabling more informed management decisions that support strategic goals,
• building a modern layer on top of complex legacy PAS architectures, ensuring seamless integration and improved system agility – a critical factor for CIOs navigating digital transformation.

By aggregating and transforming data from various sources, UWWBs provide underwriters with a structured view of risk, including real-time insights from external providers, enabling faster and more accurate decision-making. Additionally, they enhance the tracking of key underwriting metrics, providing deeper visibility into portfolio performance and profitability.

Despite significant benefits, implementing underwriting workbench is not without challenges. These projects are innovative and require specialised expertise in insurance domain knowledge, process optimisation and automation. Implementing an effective underwriting workbench involves navigating the complexities of integrating it with policy administration systems (PAS), as underwriting processes are typically collaborative and capabilities are shared between the two. Success depends on a deep understanding of underwriting workflows and seamless coordination between the workbench and PAS to ensure efficiency and accuracy.

Other difficulties in implementing underwriting workbenches often stem from legacy system constraints, data quality issues, and a lack of clear vision for process transformation. While workbenches are designed to handle complex workflows and API integrations effectively, ensuring seamless adoption requires strong business alignment and iterative refinements. However, compared to large-scale system overhauls like policy administration system (PAS) implementations, workbench deployments are significantly faster, typically measured in weeks or months rather than years.

Artificial intelligence (AI) in underwriting is rapidly evolving. While automation tools have been available for some time, the emergence of large language models (LLMs) and natural language processing (NLP) enables significant advancements, in commercial underwriting. By processing more data than ever before, AI can now extract, interpret and summarise voluminous policy documents, perform semantic and logical analysis of policy wording, and assist underwriters in risk assessment.

Looking ahead, underwriting is likely to be characterised by:

• automated underwriting processes that streamline routine tasks while keeping underwriters focused on risk assessment,
• a unified work environment, serving as a central hub for all underwriting activities,
• AI-assisted risk assessment and decision-making, providing deeper insights and greater accuracy,
• quicker response times, leading to improved customer satisfaction,
• seamless compliance and reporting, ensuring regulatory adherence,
• consolidation of all insurance data in one place, eliminating silos and supporting a holistic view,
• comprehensive monitoring of underwriting performance, enabling data-driven improvements.