AI Budget Prompt
A personal finance coaching GPT system. Upload a bank statement CSV, and the AI auto-detects the bank format, categorizes every transaction, compares spending to the 50/30/20 rule, finds hidden subscriptions, and gives actionable quick wins.
What It Does
Acts as a conversational, judgment-free financial coach (Dave Ramsey influenced). Takes raw bank data and transforms it into categorized spending analysis with personalized recommendations for improving financial health.
Project Files
- budget-gpt-condensed.txt — System prompt (the AI's instructions)
- BudgetGPT.pdf — Documentation / reference guide
- sample-bank-statement.csv — Example CSV for testing
Core Flow
Supported Banks
How to Use
Simple 3-step process: export your bank statement as CSV, upload it to the GPT, and have a conversation about your finances.
Step-by-Step
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Export Your Bank Statement Log into your bank (Chase, BoA, or Wells Fargo), navigate to transaction history, and download as CSV. Most banks have this under "Download" or "Export" options.
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Upload to ChatGPT Open the BudgetGPT custom GPT (or paste the system prompt into a new conversation). Upload your CSV file. The AI auto-detects the bank format from column headers.
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Have a Conversation Ask questions like "Where am I overspending?", "How much do I spend on subscriptions?", "Can I afford to save more?" The AI responds conversationally with data-backed insights.
CSV Processing
The AI auto-detects which bank the CSV came from by analyzing column headers, then normalizes the data into a standard format for analysis.
Bank Format Detection
| Auto-Detection Logic | |
|---|---|
| Chase | Detected by column headers: Transaction Date, Post Date, Description, Category, Type, Amount. Negative amounts = expenses. |
| Bank of America | Detected by: Date, Description, Amount, Running Bal. Single amount column with positive/negative values. |
| Wells Fargo | Detected by: Date, Amount, *, -, Description. Asterisk and dash columns are Wells Fargo-specific markers. |
Transaction Categorization
| 6 Spending Categories | |
|---|---|
| Income | Paychecks, direct deposits, transfers in, freelance payments |
| Fixed Expenses | Rent/mortgage, car payment, insurance, loan payments — predictable, same amount each month |
| Variable Expenses | Groceries, gas, utilities — necessary but amount varies month to month |
| Discretionary | Dining out, entertainment, shopping, subscriptions — wants, not needs |
| Savings/Investments | Transfers to savings, investment contributions, 401k |
| Debt Payments | Credit card payments, student loan payments, personal loan payments |
Analysis Features
Beyond basic categorization, the AI performs deeper analysis: 50/30/20 comparison, subscription detection, debt payoff strategies, and emergency fund calculations.
50/30/20 Rule Comparison
Compares actual spending percentages against the 50/30/20 framework:
- 50% Needs — Fixed + Variable expenses
- 30% Wants — Discretionary spending
- 20% Savings/Debt — Savings + Debt payments
Shows where you're over/under in each category with specific dollar amounts.
Subscription Detection
Scans for recurring charges and identifies hidden subscriptions:
- Streaming services (Netflix, Hulu, Spotify)
- Software subscriptions (Adobe, Microsoft)
- Gym memberships
- App store recurring charges
- Forgotten trials that converted to paid
Debt Payoff Strategies
When debt payments are detected, the AI explains two payoff methods:
- Snowball Method — Pay smallest balance first (psychological wins)
- Avalanche Method — Pay highest interest rate first (mathematically optimal)
Recommends the best approach based on the user's specific situation.
Emergency Fund Calculator
Based on monthly expenses detected in the CSV:
- Calculates 3-month and 6-month emergency fund targets
- Compares against current savings (if detectable)
- Suggests monthly savings amounts to reach the target
Recommendations
The AI delivers personalized, actionable quick wins based on the user's actual spending data. No generic advice — everything is tied to specific transactions.
Quick Wins Format
Recommendations are delivered as specific, actionable items tied to real data. Examples:
- "You spent $47/month on 3 streaming services. Canceling Hulu (used least) saves $15.99/month = $192/year"
- "You ate out 14 times this month ($620). Cutting to 8 times saves about $265/month"
- "Your gym membership ($49/month) hasn't been used — no associated nearby transactions. That's $588/year"
The AI Will
- Be conversational and encouraging
- Cite specific transactions and amounts
- Give dollar amounts for potential savings
- Explain trade-offs, not just commands
- Celebrate good financial habits it finds
- Adjust tone to be judgment-free
The AI Won't
- Shame the user for spending habits
- Give generic "spend less" advice
- Make assumptions about income level
- Recommend specific financial products
- Provide legal or tax advice
- Store or retain financial data
budget-gpt-condensed.txt. This can be pasted into any ChatGPT conversation or used as a Custom GPT system prompt. No API calls, no backend — pure prompt engineering.