Case Study · Financial Close

Month-End Close in BC: From 8 Days to 36 Hours Using an LLM Analysis Layer

11 min read · Business Central · LLM · Financial Operations

Month-end close in Business Central is not a data problem. The data is already posted, already accurate, already in the ledger. The problem is an analysis problem: someone has to read the data, understand what it means relative to expectations, explain the variances, and produce the commentary management needs before they can approve the close.

That someone was taking 8 days. We gave them 36 hours back.

The Client's Situation

A multi-entity distribution group with three legal entities in BC, consolidated reporting in a parent currency, and a finance team of six. Month-end involved reconciling three trial balances, producing variance analysis against budget, drafting management commentary for the CFO, and chasing six department heads for accrual confirmations. One person was responsible for all of it.

What We Built

Step 1: Data Extraction Layer

AL queries that pull posted GL entries for the period, budget entries, prior year comparison, open purchase orders not yet invoiced (for accrual estimation), and outstanding sales orders. All available in BC — just structured correctly for AI consumption.

Step 2: Multi-Stage Analysis Chain

Variance analysis first, then anomaly detection, then accrual estimation, then management commentary generation. Each stage uses the output of the previous one as context. The final output is a structured review document that matches the client's standard management pack format.

Step 3: Human Review Interface

The AI output does not post anything. It generates a draft document the finance controller works through section by section — confirming AI explanations or overriding them with manual commentary. The final document is a blend of AI analysis and human judgment, with every section clearly attributed.

Results

  • Close timeline: 8 working days to 36 hours
  • Management commentary first draft: 4 hours to 12 minutes
  • Accrual estimation accuracy: improved 23% vs. prior manual estimation
  • Audit queries in the following external audit: down 40%

The finance controller's reaction at the end of the first AI-assisted close: "I feel like I am doing my actual job for the first time. I am making decisions about the numbers instead of finding them."

Is Your Month-End Close Taking Too Long?

The architecture is portable to any Business Central environment. Let us talk about your current close process and where the time actually goes.

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