Explainable AI
Assurtech AI delivers explainable AI for insurance brokers: controlled RAG, cited sources, traceability, human approval, governance and measurable operational value.
Artificial intelligence operates as a controlled assistance layer within the platform. It enables:
It does not make autonomous decisions that bind the firm. Final validation always belongs to the user. Accountability remains human.
The AI relies on a RAG (Retrieval-Augmented Generation) mechanism. This mechanism includes four main steps:
The user’s query is analysed in order to determine: its purpose, its business context (policy - claim - compliance - tender), and the relevant documentary scope.
The platform queries a structured corpus including: internal documents (procedures - policies), configured reference data, contractual documents and selected regulatory texts. The corpus is versioned and controlled. This is not an open and indeterminate search.
The AI model produces a response based on: the elements actually retrieved, the context of the file and the embedded business rules.
The generated response: cites the documents used, may refer to precise extracts, keeps a time-stamped record and links the query to a business object. The response therefore becomes documented and traceable.
In an environment such as insurance brokerage, an AI-generated response has operational value only if it can be tied to identified, consultable and relevant sources.
AI that produces an answer without indicating the documents, clauses, references or contextual elements used creates a zone of uncertainty that is difficult to reconcile with the firm’s business requirements.
By contrast, explainable AI relies on a structured corpus, returns the sources used and allows the user to quickly verify the origin of the proposed information.
This capability turns AI into a genuinely usable documentary assistance tool, because the answer is not isolated from its foundation.
Explainability is not merely a matter of user comfort. It is a condition of credibility in a regulated sector.
The goal is not to deploy generic AI. The goal is to support concrete insurance workflows within a controlled operating framework.
AI becomes an accelerator for reading, structuring and preparation without displacing business judgement.
The central principle of our approach is simple: artificial intelligence does not replace human decision-making. It prepares, clarifies, structures and accelerates, but final approval always belongs to the authorised user.
This principle is especially important in insurance broking, where an output may have contractual, regulatory, commercial or litigation consequences.
Mandatory human validation keeps accountability at the right level and avoids confusing a generated output with a system decision.
Explainable AI therefore does not reduce the role of the domain expert. It provides a clearer, faster and better-documented working framework.
Human control is not a limitation of the tool. It is a condition for using it responsibly.
Explainable AI does not rely on a model alone. It also relies on the quality of the corpus used, the governance of sources, the scope of authorized uses and the ability to frame generated outputs.
Control over the documentary corpus helps reduce drifts linked to unreliable, outdated or irrelevant sources.
Usage governance helps define what AI may assist with, what it must not do and when validation or additional review is required.
This discipline is essential for deploying AI in a sector where information reliability matters as much as speed of output.
Governance does not slow AI down. It makes it usable over time.
In a broker firm, time savings only matter if they come with better clarity, control and documentary quality.
The advantage does not come from promises of full automation. It comes from AI that is controlled, explainable and reliable in day-to-day use.
The RAG mechanism is complemented by a conceptual structuring of the domain. The platform integrates a conceptual model (knowledge graph) describing in particular:
This model makes it possible to avoid semantic ambiguities, correctly contextualize a query and structure associations between notions. AI does not merely search for keywords. It relies on explicit structuring of the insurance domain.
AI is not left uncontrolled. Governance includes:
Version of the models used, version of documentary corpora and history of modifications.
The instructions addressed to the model are: defined, tested, versioned and controlled. Changes are documented.
Every significant interaction retains: the initial query, the context used, the response produced, the user and the timestamp.
Certain categories of use require: explicit validation, mandatory human review and confidence thresholds.
Governance is intended to maintain consistency, traceability and control over scope.
AI has limits inherent to language models. The platform takes them into account.
AI does not validate a policy. It does not reject a claim. It does not make a regulatory decision.
AI does not query the whole web. It relies on a selected and structured corpus.
Any action that creates or modifies a business object requires explicit validation.
The user can identify: the sources used, the documents consulted and the elements not covered.
AI is a tool for preparation and structuring. It is not a substitute for professional analysis.
Explainable AI is used in particular to:
In every case, traceability is retained.
See detailed use cases