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ChatGPT for Financial Analysis and Accounting: Use Cases, Workflows, and Prompting Strategies

Every finance professional should consider the high-Impact that ChatGPT Applications have across Financial Reporting, Forecasting, Compliance, and Audit.


We will see, for example, that...


It can extract tables from PDFs, match entries for reconciliations, and flag issues in journal logs...


It builds variance analyses, simulates what-if scenarios, and generates close memos automatically. And more.



USES, FLOWS, PROMPTS:

Advanced Prompt Engineering for Data Extraction: Crafting precise prompts to parse and classify complex financial data—PDF statements, OCR-scanned tables, journal entries—into structured formats for downstream analysis;
Automated Reconciliation & Validation Workflows: Using ChatGPT to automate bank and intercompany reconciliation logic, reconstruct trial balances, map general ledger line items, and detect discrepancies across AR/AP aging and ledger exports;
Dynamic Analytical & Forecasting Models: Embedding chain-of-thought prompts to drive variance analysis, sensitivity tables, and what-if scenario simulations—adjusting EBITDA, cash-flow drivers, depreciation schedules, and tax implications in real time;
Compliance, Audit & Documentation Generation: Generating audit-ready logs, IFRS-compliant expense reclassifications, lease schedule extractions, and investor-ready close memos with embedded numerical commentary and plain-English summaries of notes to financials;
Integrated Knowledge Retrieval & Reporting: Leveraging vector-based document search over policy manuals and contract clauses, then translating KPI trends, ratio analyses, and segment-level profitability into slide-ready dashboards and markdown tables for diverse stakeholders.


✦ Advanced Prompt Engineering for Data Extraction


Precision Prompting for Unstructured Inputs

Using structured prompt templates, ChatGPT can accurately extract financial line items, booking details, or accrual adjustments from documents with low structure, including PDFs, images of tables, and OCR ledger dumps.


One use case: transforming an image of a trial balance into a usable Excel table with mapped accounts.


Layered Extraction Logic

Complex files such as consolidated financial statements can be processed by multi-turn prompts that first identify sections (Balance Sheet, P&L, Cash Flow), then parse headers and values, and finally reconstruct hierarchical relationships (e.g., parent/child entities or intercompany eliminations).


Such as: breaking down a 3-entity consolidated report into separate business unit totals and eliminations.


Journal Entry Interpretation

ChatGPT can parse transaction narratives or journal logs to identify accounting principles applied, categorize entries, and flag errors in debit/credit consistency or misclassifications—critical in cleanup workflows during pre-close.


Illustration: reviewing 300 lines of journal exports and returning flagged inconsistencies by account type.


✦ Automated Reconciliation & Validation Workflows

Bank and Intercompany Reconciliation

With logic-driven prompting, ChatGPT can match bank statement entries to ledger records, identify timing mismatches, and even classify unmatched entries by probability of type (e.g., pending deposits vs errors).


Similarly, intercompany balances can be validated across ledgers from different subsidiaries.

E.g., matching €23,800 from Entity A’s receivables to Entity B’s unposted payable via description patterning.


GL and Trial Balance Integrity Checks

By feeding multiple GL or TB exports, the model can trace variances, ensure cumulative totals reconcile, and surface anomalies in period-over-period changes.


This includes spotting ghost accounts or silent account reclassifications.


For instance: detecting a new suspense account created with one unbalanced transaction in Q3.


Aging Reports & Ledger Crosschecks

AR/AP aging can be audited across months through prompts that highlight inconsistencies in days outstanding, outliers in credit terms, or customers with broken aging cycles.


Helpful in working capital management and audit prep.


As in: identifying a customer with >90 days overdue across five consecutive months despite payment history.


✦ Dynamic Analytical & Forecasting Models

Scenario Modeling via Structured Prompting

ChatGPT can act as a forecasting co-pilot, building sensitivity tables from baseline financials (e.g., toggling pricing, volume, CAC, or marketing costs) and returning markdown tables or verbal insights.


These simulations are essential for planning under uncertainty.


Use case: modeling how a 15% drop in CPM with constant volume affects EBITDA margin.


EBITDA Adjustments & Reconciliation Commentary

Prompts can isolate non-recurring or normalized adjustments from a dataset (e.g., litigation, restructuring, FX impacts) and generate investor-style commentary that aligns with EBITDA bridge presentations.


Example: flagging €180k of one-off restructuring costs and reformatting them as excluded items.


Tax Planning Simulations

Using jurisdictional data and prompt injection (e.g., "Assume Italian tax law..."), the model can simulate tax impacts across multiple entities or for merger scenarios.

Helps inform high-level strategic structuring.

Scenario: estimating the tax shield effect from interest-bearing debt under three consolidation setups.


✦ Compliance, Audit & Documentation Generation

IFRS Reclassification & Memo Drafting

ChatGPT can reclassify expenses by nature vs function (e.g., SG&A vs COGS) for IFRS compliance through label training, and draft monthly close memos that embed variance commentary and numerical trend summaries.


Such as: reclassifying €40k of SaaS subscriptions from admin to marketing based on department tags.


Audit Log Construction and Review

The model can generate change logs from exported financial models, highlight manual overrides, and create audit narratives for period adjustments.

It’s useful for reducing manual prep during year-end review or audit walkthroughs.


Use case: highlighting all formula edits made to the “Cost Projection” sheet post-lock period.


Lease & Depreciation Accounting Breakdown

By feeding lease schedules or asset registers, prompts can guide the model to compute right-of-use assets, lease liabilities, and segregate depreciation and interest elements according to IFRS 16/ASC 842 logic.


E.g., computing the depreciation schedule of a 5-year equipment lease with escalating payments.


✦ Integrated Knowledge Retrieval & Reporting

Vector-Based Financial Policy Search

Using file attachments and semantic search prompts, ChatGPT can retrieve and explain specific clauses from accounting policies or audit manuals.

For instance, identifying how a company defines “significant risk” or “material error”.


As seen in: querying for impairment thresholds across archived finance policy documents.


Segmented Profitability & KPI Analysis

Prompts can be used to break down profitability by business unit, geography, or customer group, including mapping invoice-level data against cost drivers.

KPIs like DSO, Inventory Turnover, and Contribution Margin can be dynamically benchmarked.


For example: calculating contribution margin per app segment based on Tremendous and Affise exports.


Stakeholder Reporting & Slide Generation

Using markdown tables or even draft PowerPoint-style prompts, the model can output investor-facing summaries, management dashboards, or board-ready slides with narrative, metrics, and strategic recommendations.

All aligned with financial goals.


Illustration: generating a 3-slide summary for the board on Q1 profitability drivers with annotations.

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