Synthetic data is artificially created to simulate the structure, relationships and scenarios of real information. In finance, it lets you test how an AI tool analyzes receivables aging, journal entries or budget versus actuals without starting by uploading a sensitive file. However, dummy data does not automatically guarantee privacy or success: check how it was generated, whether real information was supplied and how well it reflects the system's complexity.
Who is this guide for?
This guide is for finance, audit, IT and information security teams that want to:
- Demonstrate a use case to management before connecting to ERP.
- Compare AI tools using the same file.
- Test prompts without exposing customers, employees and suppliers.
- Practice detecting exceptions and duplicates.
- Train employees in a safer environment.
- Build a proof of concept before a full permissions process.
Synthetic data does not replace information security approval. It reduces the need for real data during learning and experimentation.
Synthetic data, anonymization and masking are different
Synthetic data is created anew. The aim is to generate artificial records with useful structure and behavior.
Masking replaces selected fields in a real record, such as a name or identity number. The rest of the record may remain real.
Anonymization attempts to prevent identifying someone from existing data. Success depends on method and context; removing a name is insufficient when a combination of fields can identify someone.
For an initial experiment, it is generally better to generate data from scratch using a schema and business description, without uploading real examples. If source data is used to learn distributions, privacy risks need professional assessment.
How do you build a useful dummy financial file?
Good dummy data is more than random numbers. It should preserve the logic the system or model will encounter later:
- Field types and date formats.
- Currencies and conversion rates.
- Relationships between customers, invoices and payments.
- Opening and closing balances.
- Positive and negative amounts.
- Payment terms and due dates.
- Duplicates, missing values and exceptions.
- Foreign-key relationships between tables.
The closer a test is to a business decision, the more important it is to define a correct result in advance.
Worked example: testing receivables aging
Suppose you want to test whether an AI tool can analyze receivables aging and suggest collection priorities.
Step 1: Define the schema
Create a table with these fields:
customer_id, customer_name, invoice_id, invoice_date, due_date, currency, invoice_amount, open_amount, payment_terms, account_manager, risk_level.
Do not use real customer names or examples copied from the system.
Step 2: Define generation rules
For example:
- 500 invoices for 80 artificial customers.
- 70% of invoices paid or not overdue.
- 20% overdue by up to 30 days.
- 8% overdue by 31–90 days.
- 2% overdue by more than 90 days.
- Ten duplicate invoices.
- Five unusual amounts.
- Two customers with high exposure concentration.
Step 3: Add known answers
Keep a separate list of planted exceptions. This is the “gold answer” against which you compare the model's output. Without known answers, it is hard to tell whether the analysis is good or merely convincing.
Step 4: Run a consistent task
Ask the tool to:
- Verify that total outstanding receivables match the file.
- Classify balances by age.
- Identify duplicates and exceptions.
- Show the ten customers with the highest exposure.
- Suggest and explain a collection order.
Step 5: Measure
Check numerical accuracy, exception recall, false positives, work time and manual corrections. Do not move to real data simply because the output looks professional.
Using Tonic Fabricate
Tonic Fabricate can generate data from scratch using a Data Agent, or define a rule-based database on suitable plans. You can define tables, columns and relationships and export formats such as CSV, JSONL or SQL.
Important: Fabricate is a cloud service. According to Tonic's documentation, it may use external AI models. Do not supply a confidential schema, real examples or sensitive information before reviewing the terms and obtaining organizational approval. Synthetic output does not automatically make the input safe.
What does dummy data not prove?
A synthetic-data pilot does not prove the solution will:
- Handle real data volumes.
- Understand inconsistent fields from legacy systems.
- Maintain accuracy across every currency and company.
- Meet security and regulatory requirements.
- Connect to ERP with the right permissions.
- Remain stable across different month-end closes.
Move to real data gradually, in an approved environment, on a limited sample and with full reconciliation.
Risks and controls
- Leakage through input: Do not paste real data into the generation description.
- Insufficient similarity: Entirely random data may create an easy, unrealistic test.
- Source replication: A generator trained on sensitive data may retain details or identifying patterns.
- Bias: Artificial distributions may hide rare cases.
- Environment confusion: Clearly label every file SYNTHETIC and prevent loading into production.
- Incomplete documentation: Keep the schema, generation rules, seed and version date.
Alternatives
- Generate data using a simple internal script when the structure is well defined.
- Enterprise synthetic-data tools with privacy and quality metrics.
- Manually prepared practice files for a small workshop.
- An ERP vendor's sandbox, if available.
For testing models on the same file, see ChatGPT, Claude or Gemini for finance professionals. To turn the experiment into a recurring process, see Skills and AI agents in finance.
Frequently asked questions
Is synthetic data always anonymous?
No. It depends on how it was created and the information used to create it. Conduct a privacy assessment rather than relying on the word “synthetic.”
Can it demonstrate ROI?
You can measure time, accuracy and process capability in a pilot, but should state that performance on real data may differ.
Do we need a dedicated tool?
No. For a small experiment, a script or language model can create a file, provided you define the schema, rules and known answers.
Sources and verification date
Information checked on August 4, 2026:
- Tonic Fabricate — User Guide
- Tonic Fabricate — Data generation processes
- Tonic — Fabricate Trust Center
- UK Government — AI Insights: Synthetic Data
- NVIDIA — Generating Safe Synthetic Data
Want to build a proof of concept without risking real data?
In AI Finance workshops, we define a use case, build a practice environment and measure results before connecting to real information. Learn about AI workshops for finance teams.
