# Synthetic Financial Data Generator (`jungle_synthesizer/synthetic-financial-data-generator`) Actor Generate realistic synthetic financial transaction data with category-aware amounts, temporal spending patterns, running balances, and configurable fraud labels for ML training and fintech testing - **URL**: https://apify.com/jungle\_synthesizer/synthetic-financial-data-generator.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Developer tools, Business, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Synthetic Financial Data Generator Generate realistic synthetic financial transaction data for ML training, fintech testing, and data pipeline development. Produces bank-statement-quality records with category-aware amounts, temporal spending patterns, running balances, and configurable fraud labels. ### What it does This actor generates synthetic financial transactions that mimic real banking data. No web scraping is involved -- all data is computed locally using statistical models. Each transaction includes: - **Account details** -- holder name, account type (checking, savings, credit, investment), account ID - **Transaction data** -- amount, date, category, merchant name, MCC code, description - **Running balance** -- accurate per-account balance tracking across all transactions - **Fraud labels** (optional) -- binary fraud flag, fraud type classification, anomaly score #### Categories and amount distributions Transactions are distributed across 12 spending categories with realistic amount ranges: | Category | Range | Distribution | |----------|-------|-------------| | Groceries | $15 -- $250 | Log-normal (mean $65) | | Rent | $800 -- $3,500 | Normal (mean $1,500) | | Salary | $2,000 -- $8,000 | Normal (mean $4,500) | | Dining | $8 -- $120 | Log-normal (mean $35) | | Coffee | $3 -- $9 | Normal (mean $5.50) | | Shopping | $10 -- $500 | Log-normal (mean $75) | | Transport | $2 -- $100 | Log-normal (mean $25) | | Utilities | $40 -- $350 | Normal (mean $150) | | Entertainment | $5 -- $80 | Log-normal (mean $25) | | Healthcare | $15 -- $600 | Log-normal (mean $120) | | Subscriptions | $5 -- $50 | Normal (mean $15) | | Transfers | $50 -- $2,000 | Log-normal (mean $500) | #### Temporal patterns - **Weekday/weekend bias** -- coffee and transport spike on weekdays; dining and entertainment spike on weekends - **Recurring transactions** -- salary deposits (1st and 15th), rent (1st), utilities (15th), subscriptions (variable day) - **Seasonal multipliers** -- spending increases in November (1.15x) and December (1.30x), dips in January (0.85x) - **Time-of-day realism** -- coffee purchases at 6-11 AM, dining at 11 AM-10 PM, salary at 8 AM #### Fraud injection When enabled, a configurable percentage of transactions are flagged as fraudulent with: - **Fraud types**: card_stolen, account_takeover, card_not_present, synthetic_identity - **Anomaly pattern**: fraudulent amounts are 2-8x the normal category maximum - **Fraud score**: 0.7-1.0 for fraudulent transactions, 0.0-0.3 for legitimate ones ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | 100 | Number of transactions to generate | | `numAccounts` | integer | 5 | Number of unique financial accounts | | `currency` | string | USD | Currency code (USD, EUR, GBP, JPY, CAD, AUD) | | `dateRangeMonths` | integer | 6 | Months of history to generate | | `fraudRate` | number | 2 | Percentage of fraudulent transactions (0-100) | | `includeFraudLabels` | boolean | true | Include fraud detection fields in output | | `seed` | integer | 0 | Random seed for reproducible output | ### Output Each transaction record contains: ```json { "transaction_id": "397b9202-8ace-4fc4-9fa2-464893c3bc34", "account_id": "ACCT-0001", "account_holder": "Brenda Upton", "account_type": "checking", "currency": "USD", "date": "2025-10-03T09:25:27.000Z", "amount": -65.42, "type": "debit", "category": "groceries", "merchant_name": "Whole Foods", "merchant_category_code": "5411", "balance_after": 4231.58, "is_recurring": false, "description": "Whole Foods - groceries purchase", "is_fraudulent": false, "fraud_type": null, "fraud_score": 0.12 } ```` When `includeFraudLabels` is false, the `is_fraudulent`, `fraud_type`, and `fraud_score` fields are omitted. ### Use cases - **ML model training** -- fraud detection, transaction categorization, anomaly detection - **Fintech testing** -- payment processing pipelines, accounting software, budgeting apps - **Data pipeline development** -- ETL workflows, data warehouse testing, API mocking - **Demo data** -- realistic financial dashboards and reports ### Reproducibility Set the `seed` parameter to any positive integer to get identical output across runs. This is useful for: - Consistent test fixtures - Reproducible ML training datasets - Deterministic integration tests ### Performance - Sub-second generation for 1,000 transactions - 256 MB memory sufficient for up to 50,000 transactions - No network requests -- pure computation # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of transactions to generate ## `numAccounts` (type: `integer`): Number of unique financial accounts to generate ## `currency` (type: `string`): Currency code for generated transactions ## `dateRangeMonths` (type: `integer`): Number of months of transaction history to generate (counting back from today) ## `fraudRate` (type: `number`): Percentage of transactions flagged as fraudulent (0 to disable fraud labels) ## `includeFraudLabels` (type: `boolean`): Whether to include fraud detection fields (is\_fraudulent, fraud\_type, fraud\_score) ## `seed` (type: `integer`): Random seed for reproducible output (leave empty for random) ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 100, "numAccounts": 5, "currency": "USD", "dateRangeMonths": 6, "fraudRate": 2, "includeFraudLabels": true } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 100, "numAccounts": 5, "currency": "USD", "dateRangeMonths": 6, "fraudRate": 2, "includeFraudLabels": true, "seed": 0 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/synthetic-financial-data-generator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 100, "numAccounts": 5, "currency": "USD", "dateRangeMonths": 6, "fraudRate": 2, "includeFraudLabels": True, "seed": 0, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/synthetic-financial-data-generator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 100, "numAccounts": 5, "currency": "USD", "dateRangeMonths": 6, "fraudRate": 2, "includeFraudLabels": true, "seed": 0 }' | apify call jungle_synthesizer/synthetic-financial-data-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/synthetic-financial-data-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Synthetic Financial Data Generator", "description": "Generate realistic synthetic financial transaction data with category-aware amounts, temporal spending patterns, running balances, and configurable fraud labels for ML training and fintech testing", "version": "0.1", "x-build-id": "qFkZP7g8XXmklxpgb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~synthetic-financial-data-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-synthetic-financial-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~synthetic-financial-data-generator/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-synthetic-financial-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~synthetic-financial-data-generator/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-synthetic-financial-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Transactions", "type": "integer", "description": "Maximum number of transactions to generate", "default": 100 }, "numAccounts": { "title": "Number of Accounts", "type": "integer", "description": "Number of unique financial accounts to generate", "default": 5 }, "currency": { "title": "Currency", "enum": [ "USD", "EUR", "GBP", "JPY", "CAD", "AUD" ], "type": "string", "description": "Currency code for generated transactions", "default": "USD" }, "dateRangeMonths": { "title": "Date Range (Months)", "type": "integer", "description": "Number of months of transaction history to generate (counting back from today)", "default": 6 }, "fraudRate": { "title": "Fraud Rate (%)", "type": "number", "description": "Percentage of transactions flagged as fraudulent (0 to disable fraud labels)", "default": 2 }, "includeFraudLabels": { "title": "Include Fraud Labels", "type": "boolean", "description": "Whether to include fraud detection fields (is_fraudulent, fraud_type, fraud_score)", "default": true }, "seed": { "title": "Random Seed", "type": "integer", "description": "Random seed for reproducible output (leave empty for random)" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```