How knowledge graphs are revolutionizing risk analysis in insurance

Introduction

Imagine trying to understand a complex story by reading only a few random words on a page. You might get some information, but the deeper meaning, the relationships between characters, and the timeline of events would completely escape you. For a long time, risk analysis in insurance was much like that: a collection of valuable but isolated data. Today, generative artificial intelligence offers a connected vision, capable of assembling the pieces of the puzzle to reveal the full story of a claim. It radically transforms how insurers understand and, more importantly, prevent risks. This article will explain, in simple terms, what a risk is, the limits of traditional methods, and how this new technology works through the fascinating example of knowledge graphs.

1. Risk in Insurance: A Fundamental Definition

Before diving into technology, let’s go back to the basics. What is a risk in insurance?
It is a precise concept based on several pillars.

• Definition: A random event that may cause a loss (most often financial) and that can take various forms: financial, operational, technical, etc.
• Characterization: The risk is studied from two angles: its frequency (the probability of occurring) and its severity (the extent of damage). These estimates are generally based on historical data analysis.
• Applications: Understanding a risk is not a theoretical exercise. The final goal is concrete: calculating a fair insurance premium, evaluating the financial reserves to cover future claims, or projecting complex financial scenarios.

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Although this approach is fundamental, it faces major limitations in a world where events are increasingly interconnected and complex.

2. The Limits of the Traditional Approach

Traditional risk analysis is often hindered by how information is organized and used.
Insurers face several major challenges that prevent a comprehensive and in-depth understanding of claims. These challenges are not isolated; they feed each other. Data silos prevent connections from being seen, making any global visualization of risk impossible.

• Knowledge in silos: Information is often scattered among different teams (experts, lawyers, managers) and stored in various formats such as PDF reports, numerical databases, expert opinions, etc. Getting a coherent overall view then becomes a Herculean task.
• Difficulty seeing relationships: It is extremely complex to reconstruct the exact chronology of events and, more importantly, to distinguish between simple correlation and true causality. Without this understanding, we address symptoms rather than root causes.
• Complex visualization: A risk has multiple dimensions, financial, technical, operational, human. Representing all these aspects simultaneously to grasp the overall dynamics is a real challenge.

To overcome these obstacles, a new approach is needed, one capable of extracting and connecting the hidden information buried deep within unstructured documents.

3. Generative AI: The Key to Revealing Hidden Connections

This is where generative artificial intelligence (AI) comes into play. Its main goal in this context is simple but powerful:
to have more precise knowledge and make better use of it.

Instead of limiting itself to spreadsheets, this new generation of AI can read and understand hundreds, even thousands, of unstructured documents such as claims reports, technical analyses, or legal opinions. It acts as a tireless expert synthesizing all available knowledge.

The AI analyzes everything: the client's context, the sequence of events, financial impacts, expert opinions, legal actions, and much more.

But extracting information is not enough. The real strength of AI is its ability to organize it into an intelligent and usable structure. This is where the concept of the 'knowledge graph' comes in  the intelligent structure that AI builds to make sense of this flow of information.

4. Knowledge Graphs: Mapping Risk

Imagine a subway map. Each station is an event (a node), and each subway line is the relationship that connects them (an edge). A knowledge graph is exactly that: a map of events and their links. These relationships can be chronological ('this happened after that')
or causal ('this caused that').

This structure, which captures the direction of cause-and-effect links, is called a Directed Acyclic Graph (DAG). The process for building this risk map takes place in four main stages.

1. Step 1: Reading the Claim. The AI starts by reading a claims report (for example, a PDF file). It intelligently splits it into relevant chunks of text. For each chunk, it identifies key events and their relationships, thus creating several mini-graphs.
2. Step 2: Assembling the Puzzle. The AI then gathers all the mini-graphs from the same claim file. It merges them to create a single, complete graph — the 'Claim Graph.' This provides an overview of all the events related to that specific claim.
3. Step 3: Data Standardization. Before aggregating thousands of graphs, the AI performs a crucial cleaning step. It normalizes event labels to ensure that 'electrical fire' and 'fire of electrical origin' are treated as one and the same category, ensuring consistency across the dataset.
4. Step 4: The Big Picture. Finally, all the 'Claim Graphs' from thousands of claims are aggregated into one single 'Meta-Graph.' This global view is revolutionary. It reveals recurring patterns, root causes shared by multiple claims, and trends that were previously invisible.

Now that we have this incredibly detailed map of risks, what can insurers actually do with it?

5. Practical Applications: From Understanding to Action

The true value of knowledge graphs lies in their ability to transform understanding into concrete business actions. This detailed mapping of risk unlocks immediate business value in three strategic areas:

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Beyond these applications, this technology changes how we interact with data.
It becomes possible to ask natural language questions to an AI agent, such as:
'What are the most frequent types of incidents leading to a fire?' The agent directly queries the graph to deliver a clear, concise answer, making complex analysis accessible to all.

Conclusion: A New Frontier for Risk Management in Insurance

Generative artificial intelligence and knowledge graphs mark a turning point for the insurance industry. They enable a shift from a static, siloed view to a dynamic, connected understanding of risk. By revealing the cause-and-effect relationships hidden in mountains of documents, this approach offers considerable advantages:

• A valuable time saver for experts.
• An unprecedented overview of risk portfolios.
• Improve prevention by tackling the root causes.
• The creation of a strategic data asset for the company.

Of course, this transformation is not without its challenges. The cost of analysis (estimated at between €1 and €9 per claim, depending on the source) and the need to support teams in adopting these new tools are realities. However, the potential is immense. This technology paves the way for more proactive, more accurate, and ultimately more effective insurance that prevents risks rather than simply compensating for them.

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