Recurrence detection automatically identifies and groups transactions that follow predictable patterns over time. This powerful feature helps you understand subscription patterns, regular expenses, and recurring income streams by analyzing transaction histories and detecting periodic behaviors.

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How Recurrence Detection Works

Our advanced machine learning algorithms analyze transaction patterns to identify recurring behaviors by examining temporal patterns, entity consistency, amount variations, and frequency analysis. The AI model handles real-world scenarios like gaps in patterns, amount variations, and irregular intervals while still detecting true recurring transactions.

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Recurrence Groups

When our system detects recurring patterns, it automatically groups related transactions into “recurrence groups” with shared characteristics like same counterparty, consistent timing, and similar categories. A single merchant can have multiple recurrence groups (e.g., Netflix monthly subscriptions vs. annual renewals), while one-time purchases remain separate.

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Recurrence Types

Our system identifies fixed subscriptions (streaming services, software), variable recurring expenses (utility bills, insurance), and income patterns (salary, dividends) to provide comprehensive recurrence detection across all transaction types.

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Accuracy and Reliability

Our recurrence detection achieves high accuracy through advanced pattern recognition, noise filtering, temporal analysis, and entity intelligence, with models that continuously improve with more transaction data.

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Getting Started

Ready to implement recurrence detection? Check out our API Reference for detailed endpoint documentation, or visit our Quick Start Guide to begin detecting recurring patterns in your transaction data.