Risk Engineering Relies on Tribal Knowledge: Why That’s a Problem

The specialty and re-insurance sector is defined by the highly specialised and complex nature of its workflows. Within this landscape, risk engineering plays a critical role, assessing intricate, unique, or high-value risks that standard insurance models simply cannot handle.

 Unlike routine personal insurance lines, these cases demand customised underwriting, deep subject-matter expertise, and significant human judgment over predefined templates. 

However, a significant challenge persists: a substantial amount of the critical expertise required for effective risk assessment resides undocumented within the minds of seasoned professionals, often referred to as 'tribal knowledge'. This over-reliance on undocumented human intelligence presents several profound problems for insurers.

The Nature of Risk Engineering in Specialty Insurance

Risk engineering in specialty insurance involves evaluating complex and emerging risks, such as cyber threats, renewable energy projects, marine transport, or those related to climate change. These assessments often require a nuanced understanding and situational awareness that can only be gained through years of experience. The judgments made by risk engineers directly impact pricing strategies and the overall viability of covering multifaceted risks. This dependence on individual human expertise makes the capture and dissemination of knowledge particularly crucial, yet often challenging.

The Problem with Undocumented Expertise (Tribal Knowledge)

When essential knowledge is locked within individuals, organisations face significant inefficiencies and risks.

Fragmented Knowledge and Silos

Experts, including risk engineers, frequently operate in silos. This makes accessing collective knowledge across different departments or geographical regions exceedingly difficult. Valuable insights gained in one area might not be available to teams working on similar risks elsewhere, leading to inconsistent assessments and missed opportunities.

Missed Opportunities and Inefficiencies

Key insights that could significantly improve risk assessment methodologies, enhance pricing strategies, or streamline processes remain inaccessible. This lack of readily available knowledge leads to inefficiencies, suboptimal decision-making, and potential revenue loss. 

Without a system to surface relevant past experiences or expert opinions, risk engineers may spend excessive time researching or, worse, make decisions without the benefit of collective organisational wisdom. On average, it takes between an hour to two-plus days to resolve an incident for nearly 80% of enterprises. This can lead to significant gaps in efficiency, ultimately affecting profitability in the long term.

Dependence on Key Personnel

Organisations become heavily dependent on a few key experts who hold critical, undocumented knowledge. This creates a significant vulnerability: when these individuals retire or leave the company, a wealth of invaluable experience walks out the door with them, leaving a knowledge gap that is difficult and time-consuming to fill.

Difficulty with Complex and Unique Cases

Specialty and reinsurance cases are rarely identical, demanding high judgment based on situational awareness and prior experience. When this experience is undocumented tribal knowledge, accessing relevant insights to inform decisions on unique cases becomes a manual, often inefficient, process of tracking down the right expert.

Weakening Competitive Edge

In the specialty and reinsurance markets, competitive advantage is heavily reliant on institutional knowledge and expert capability. When this knowledge is undocumented and fragmented, it hinders an organisation's ability to leverage its full intellectual capital, potentially undermining its competitive position.

Bridging the Gap: AI and Human Intelligence

To overcome these challenges, organisations in the specialty and re-insurance sector must actively bridge the gap between human intelligence (HI) and artificial intelligence (AI). The goal is to ensure that human expertise is effectively captured, shared, and leveraged across the organisation.

A Solution: Leveraging AI to Operationalise Expertise

Technologies like Starmind offer a pathway to addressing the tribal knowledge problem. By analysing internal communications and shared documents, such platforms create a real-time network of an organisation's internal expertise. This intelligent system can identify key experts and provide instant access to situational knowledge, driving informed decision-making.

Specifically for risk engineering, these AI-driven systems can surface field insights on emerging risks, such as those related to climate or cybersecurity, enhancing risk assessment capabilities and ensuring consistent evaluation and pricing. They provide real-time access to people with relevant experience and surface the nuanced, undocumented knowledge that experts rely on. 

Such solutions help to streamline workflows, enhance collaboration across teams and regions, and crucially, safeguard critical expertise, reducing the risks associated with employee turnover. The knowledge provided becomes permanently stored and globally accessible, preserving institutional memory. This allows for activities like analysis on climate risk mitigation planning and stress testing to draw on the full depth of the organisation's expertise.

The Path Forward

Moving beyond reliance on undocumented tribal knowledge is essential for insurers to thrive in the complex specialty and reinsurance landscape. By integrating AI with human intelligence, organisations can operationalise their collective expertise, enabling more accurate risk pricing, faster claims resolution, and smarter underwriting decisions. Leveraging platforms that can effectively capture and share this critical knowledge transforms it from a vulnerable, siloed asset into a dynamic, accessible resource that drives efficiency, enhances decision-making, and builds resilience.

Transform Your Undocumented Knowledge into An Asset

The heavy reliance on tribal knowledge in risk engineering within specialty and re-insurance poses significant threats, from operational inefficiencies and missed opportunities to critical vulnerability when experts depart. In a market defined by complexity and the need for deep, nuanced expertise, failing to capture and leverage this internal intelligence is a strategic oversight. 

By embracing AI-driven solutions designed to connect, capture, and operationalise human expertise, insurers can transform undocumented knowledge into a powerful, accessible asset. This not only mitigates the risks of knowledge loss but also unlocks the full potential of their expert teams, fostering innovation, enhancing decision quality, and securing a vital competitive advantage in an increasingly challenging global market.