Protect Financial Data When Using AI Tools
Finance teams can use ChatGPT to analyse transactions, identify patterns, and generate insights without exposing account numbers, customer financial information, or sensitive data.
The Finance Team's Challenge
Financial data analysis needs AI power, but regulatory compliance can't be compromised
The Scenario
A financial analyst needs AI help identifying patterns in transaction data, summarising customer account activity, or detecting anomalies. The export contains account numbers, customer names, transaction amounts, BSB/routing numbers, and reference details.
The Risk Without Protection
Uploading raw financial data to AI tools exposes:
- •Account numbers and banking details
- •Customer names and financial identifiers
- •Transaction history and payment patterns
- •BSB/routing numbers and reference codes
- •Serious regulatory violations and compliance breaches
With Redactli: Safe AI Financial Analysis
Protect customer financial data while getting powerful analytical insights
Export Financial Data
Download transaction or account data as CSV
Anonymize Identifiers
Transform account numbers and customer info
Upload to AI
Get pattern analysis safely with ChatGPT
Get Insights
Identify patterns without exposing data
Maintain Compliance
Analysis with regulatory protection
Example Workflow
You upload to ChatGPT: “Analyse these transaction records. Identify unusual spending patterns, flag potential fraud indicators, and summarise monthly trends by account.”
ChatGPT sees: Encrypted tokens like “Account_x5y9z3w1” and “Name_a3b7c9d2”, with transaction amounts and dates preserved for pattern analysis.
ChatGPT responds with: Pattern analysis identifying anomalies (e.g., “Account_x5y9z3w1 shows unusual spike in transactions”), trend summaries, and flagged accounts for review—all using encrypted tokens.
Result: Financial insights remain analytical and valid while customer privacy and regulatory compliance are maintained.
Financial Data Protected
Redactli anonymizes all financial PII while preserving analytical validity
Account Numbers
Real account numbers → encrypted tokens like “Account_x5y9z3w1”
Customer Names
Account holders encrypted to tokens like “Name_a3b7c9d2”
Transaction References
Payment references encrypted, patterns preserved
BSB/Routing Numbers
Banking identifiers encrypted with tokenized format
Transaction amounts, dates, and patterns remain analysable—only customer identifiers and account details are protected.
Real Finance Team Example
Transaction Pattern Analysis
Challenge:
Financial controller has 12 months of transaction data (15,000 records) and needs to identify spending patterns, detect anomalies, and prepare executive summary for board presentation—but can't manually analyse thousands of transactions.
Solution with Redactli:
- 1.Export transaction data with account numbers, customer names, amounts, dates, and categories
- 2.Upload to Redactli and anonymize Account Number, Customer Name, and Reference columns
- 3.Ask ChatGPT: “Analyse transaction patterns. Identify top spending categories, flag accounts with unusual activity, calculate monthly trends, and create executive summary with key insights”
- 4.Receive comprehensive analysis with anonymized account identifiers, trend charts, anomaly alerts, and executive summary
- 5.Present insights to board with confidence—no customer data was exposed to AI tools
Outcome:
Finance team gets sophisticated analysis in hours instead of weeks. Regulatory compliance maintained throughout. Board receives actionable insights backed by comprehensive data analysis.
Ready to Protect Financial Data?
Join finance teams using Redactli to safely leverage AI for transaction analysis without compromising customer privacy or compliance. Start free today.