LatAm Synth — Synthetic Financial Savings Data Generator

Generate realistic synthetic test data for fintech applications, calibrated on 506,311 real LatAm records (2015–2024). Use it to generate synthetic datasets for ML training, mock financial transactions for integration testing, realistic fake user data for testing pipelines, or seed data for fintech development — all with full referential integrity, 100% synthetic, zero PII.

Each run produces linked users, savings goals, and deposit/withdrawal transactions that behave statistically like real Latin American savings-app data.

What you get

Each run produces three linked tables of synthetic dataset output:

Table	Description
users	Synthetic users with country, scores, join date
goals	Savings goals with category, required amount, status (achieved / overdue / in_progress)
transactions	Mock financial transactions — deposits and withdrawals within each goal's time window

Calibrated against real data: lognormal mixture distributions (KS=0.032), 69.5% round-value snap, 73.8% overdue rate, monthly seasonality, 8 goal categories with realistic amounts and horizons. Suitable as a synthetic dataset for ML training or as realistic fake user data for testing any fintech system.

Pricing — Pay Per Event

This actor uses Apify's Pay Per Event model. You are charged per synthetic user generated, regardless of output format or destination.

Event	Unit	What triggers it
users-generated	per user	Each run, before data is written

Example: generating 1,000 users = 1,000 × unit price.

The charge fires before output is written, so it applies equally whether you use CSV files, JSON, or the Dataset export — there is no cheaper path. If your spending limit is reached mid-run, the actor stops cleanly and reports the limit in the log.

Set your spending cap in Apify Console → Run → Max total charge before starting a run.

Input parameters

Parameter	Type	Default	Description
users	integer	1000	Number of synthetic users (1–50,000)
seed	integer	null	Random seed for reproducibility. Same seed → identical output
countries	string[]	null	Restrict to specific countries. Empty = full LatAm mix (Mexico 46.6%, Colombia 14.4%, …)
format	csv | json	csv	Output format for Key-value store files
push_to_dataset	boolean	true	Push transactions to Apify Dataset (enables native JSON/CSV/Excel export and integrations)
start_date	string	2023-01-01	Start of generation period (YYYY-MM-DD)
end_date	string	2024-12-31	End of generation period (YYYY-MM-DD)

Where to find your output

Every run writes to two places:

Key-value store — all three tables

1. Open the run → Storage tab → Key-value store
2. Download files:
   • users.csv / goals.csv / transactions.csv (CSV mode)
   • OUTPUT_DATA — single JSON with all three tables (JSON mode)
   • OUTPUT — always present; JSON summary of the run (parameters, row counts, file list)

Dataset — transactions (Apify-native export)

When push_to_dataset=true (default), all transactions appear in the run's Dataset tab:

• Export as JSON, CSV, or Excel in one click
• Connect Google Sheets, webhooks, or any Apify integration
• Disable with push_to_dataset=false for runs > 10K users where you only need the KVS files

Use cases

• Fintech testing & QA — realistic fixtures for payment pipelines, budget apps, savings engines
• ML training data — bootstrap churn, recommendation, and segmentation models with real LatAm patterns
• Demos & POCs — dashboards with publicly shareable synthetic data
• Education — unlimited datasets for data science courses with real business narrative

For AI agents

This actor is designed to be invoked programmatically. Minimum input to generate synthetic test data:

{
  "users": 500,
  "seed": 42
}

Full input with all options:

{
  "users": 1000,
  "seed": 42,
  "countries": ["Mexico", "Colombia"],
  "format": "csv",
  "push_to_dataset": true,
  "start_date": "2023-01-01",
  "end_date": "2024-12-31"
}

Reading the output after the run completes:

• Dataset items (transactions): GET {run.defaultDatasetId}/items — paginated JSON, directly iterable.
• CSV files (users, goals, transactions): GET {run.defaultKeyValueStoreId}/records/users.csv, .../goals.csv, .../transactions.csv.
• Run summary (row counts, parameter echo): GET {run.defaultKeyValueStoreId}/records/OUTPUT — JSON, always present.

The seed parameter guarantees deterministic output: the same seed always produces the same rows, useful for reproducible test fixtures. Omit seed for a random run.

Privacy & compliance

Output is 100% synthetic — no record derives from a real individual. No PII, no re-identification risk. GDPR / LGPD / CCPA / LFPDPPP non-applicable.