Reinsurance reconciliation doesn’t fail because teams lack data.
It fails because the data is fragmented, inconsistent, and difficult to interpret without introducing new risk.
Gritmind partnered with an AI platform company serving reinsurers and carriers to deliver a production-ready bordereaux reconciliation agent that exposes material data-quality issues faster—and with greater transparency—than traditional audits.
Executive Summary
The company engaged Gritmind to deliver a reconciliation capability that succeeds where traditional audits fall short: operating on fragmented, real-world reinsurance data while producing results stakeholders can trust, review, and act on.
Together, we delivered the Bordereaux Reconciliation Agent—an AI-assisted system that reconciles treaty terms, bordereaux data, and unstructured bank statements with full traceability and financial rigor.
The solution:
- Enables reconciliation cycles that are ~90% faster than traditional audit approaches
- Surfaces material data quality issues that prior audits do not clearly identify
- Contributes to uncovering drivers behind a 12% uplift in true profitability for the reinsurer
Rather than replacing financial controls with opaque automation, the agent applies AI only where interpretation adds leverage, while preserving deterministic calculations and human review. The result is faster, data-driven resolution grounded in shared, reviewable evidence.
The Business Challenge
Reinsurance settlements require reconciling:
- Treaty files defining contract terms
- Bordereaux files detailing premiums and losses
- Bank statements showing actual cash movement
In practice, these inputs arrive in inconsistent formats. Bordereaux vary by counterparty, and bank statements often arrive as long, unstructured PDFs containing a mix of insurance and non-insurance activity.
In one sample dispute, a traditional audit has already been completed, yet neither party trusts the results. The issue is not missing data, but the absence of a trusted, traceable source of truth.
The Solution
The Bordereaux Reconciliation Agent is designed for real inputs, financial rigor, and human review.
Design principles
- AI is applied only to interpretation tasks where ambiguity exists
- All financial calculations remain deterministic and reviewable
- Outputs are designed for human validation, not blind automation
Key characteristics
- Programmatic ingestion of treaty and bordereaux files
- Extraction of unstructured bank data using Azure Document Intelligence
- Selective application of AI for interpretation tasks (classification, mapping, relevance)
- Deterministic code used for all financial calculations
Rather than relying on a single, monolithic “AI audit,” the agent uses narrowly scoped prompts that make failures visible and iteration controlled.
This approach achieves 100% reconciliation coverage across available data and identifies 35+ data quality issues that traditional audits do not clearly surface.
Results & Impact
Instead of months-long audit cycles with unclear outcomes, stakeholders are able to:
- See discrepancies directly
- Trace results back to source documents
- Focus discussions on resolution rather than trust
The result is faster insight, improved confidence, and a clearer path forward for enterprise decision-making.
Every discrepancy surfaced by the agent can be traced back to its source document and transformation step.
Why work with Gritmind
The company needs to prove value quickly and credibly in a high-stakes, enterprise context. They require a partner who can operate under a hard deadline, work directly with messy, real-world financial data, and deliver a system that can withstand financial and enterprise scrutiny.
Gritmind is selected not simply for AI capability, but for a disciplined approach to applying AI in regulated, data-intensive environments. Rather than optimizing for novelty or automation volume, Gritmind prioritizes traceability, precision, and reviewability—ensuring results can be trusted by all parties involved.
This approach aligns directly with the company’s needs: move fast without sacrificing rigor, surface insight without introducing new risk, and produce outputs that stand up to review rather than require interpretation or defense.
The goal is not automation for its own sake—it is trust, traceability, and speed.