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Banking Plaid (OAuth)

Sync Plaid Data to Flat Files & Spreadsheets

Plaid

Automatically sync your Plaid data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.

Historical & Dynamic Data
One-way Sync
For Non-technical Users

About the integration: Flatly connects to over 10,000 financial institutions in the US and Canada via Plaid. Flatly integrates with Plaid's official APIs.

Connect Plaid Which Plaid API does Flatly use?

Available Datasets

Flatly can sync the following datasets from Plaid.

  • Balances
  • Balances-Recent
  • Transactions-AllAvailable
  • Transactions-Last X days Date-ranged
  • Transactions-AllAvailable+Accounts Combo
  • Transactions-Last X days+Accounts Combo Date-ranged
  • InvestmentTransactions-All
  • InvestmentTransactions-Last X days Date-ranged
  • InvestmentTransactions-All+Accounts Combo
  • InvestmentTransactions-Last X days+Accounts Combo Date-ranged
  • InvestmentHoldings
  • Liabilities-Credit
  • Liabilities-Mortgage
  • Liabilities-Student

Send Plaid data to

Flatly can sync Plaid data to any of these destinations.

Note: Some destinations are aliased, for all intents and purposes they are functionally equivalent.

Plaid API

Secure access to your financial data

Flatly connects to over 10,000 financial institutions in the US and Canada via Plaid's secure integration.

Authorization.
Plaid's OAuth flow walks you through connecting to your financial institution. The process takes about a minute and never exposes your credentials to Flatly.
Read-only scopes.
Flatly's access is limited to account data — words, numbers, and dates. It does not include money-movement, transfer capabilities, or account holder profiles or identities.
Combined data sets.
Pre-blended data sets combine related data automatically, giving you more useful data for analytics with less manual joining work.

FAQ: Plaid

Is it safe to connect my bank account through Plaid?

Plaid is one of the most widely used financial data networks in the US — it's the same infrastructure behind apps like Venmo, Robinhood, and Mint. Your banking credentials go directly to Plaid, never to Flatly. Flatly only ever receives read-only account data (numbers, dates, transaction text) — it has no ability to move money or access your identity.

Will Flatly be able to see my passwords or log into my bank?

No. You authenticate through Plaid's own secure interface, and Plaid alone communicates with your bank. Flatly receives only a limited, scoped data feed from Plaid — not your credentials or any direct bank access.

Can Flatly move money or make transactions on my behalf?

No. Flatly's access is strictly read-only. It cannot initiate transfers, payments, or any account actions whatsoever.

If I connect multiple bank accounts through Plaid, do they all appear in one file?

It depends on the file format you use. For example with Google Sheets, many datasets can be combined as tabs.

Can I choose a date range for transactions, or does it always pull everything?

You have options. Either Alltime or the "Last X Days" option which is date-rangeable, so you're not forced to pull your full account history on every sync.

Is Plaid available for my bank? What if I'm outside the US?

Flatly's Plaid integration covers 10,000+ financial institutions in the US and Canada. Banks outside those two countries are not currently supported through this integration.

What happens to my Plaid connection if I cancel Flatly?

Connections are automatically revoked if you cancel and stop using Flatly.

Querying Plaid - AI or ETL?

Many SaaS applications and databases can be directly queried within AI chat apps using connectors. For ad-hoc, single-user requests, this direct architecture is superior—it offers real-time visibility, high customizability, and bypasses the need for complex data pipelines.

However, ETL (Extract, Transform, Load) can be more favorable when certain requirements exist. You might consider transitioning to an ETL system and a centralized data store when:

  • Collaboration is required: Multiple stakeholders (colleagues, partners) need consistent access to the same shared single source of truth.
  • Downstream audiences: The queried data must be routed into other internal IT systems, published to team dashboards, or preserved in official records rather than remaining isolated in a chat window.
  • Data is complex: The raw data needs heavy cleaning or joining before an AI or non-technical user can accurately understand it.
  • Internal capabilities are limited: Your organization lacks the in-house programming resources or infrastructure necessary to reliably build, deploy, and maintain custom agentic workflows or complex data integrations from scratch.
  • Numerous accounts: When a high volume of identities or accounts within a data source need to be analyzed in bulk, a sophisticated connection manager is needed.
  • Concurrency & infrastructure: If many data sets need to be analyzed in parallel, concurrency can become an issue. The device hosting an AI agent can become saturated without concurrency controls in place, whereas an ETL pipeline is built to handle parallel workloads reliably.