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Banking Plaid (OAuth)

Sync Banking Data to Flat Files & Spreadsheets

Banking

Automatically sync your Banking 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 Banking's official APIs.

Connect Banking Which Banking API does Flatly use?

Available Datasets

Flatly can sync the following datasets from Banking.

  • 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 Banking data to

Flatly can sync Banking data to any of these destinations.

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

FAQ: Banking

What is the difference between "banking" and "Plaid"?

Plaid is a trusted proxy for banking integrations. Flatly uses "Plaid" and "Banking" synonymously because, importantly, many users are unfamiliar with Plaid and simply want to find a data integration solution that works with their bank. In the data sync context Banking, Finance and Investments are all aliases for Plaid.

More information:

Querying Banking - 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.