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Category Analytics breaks down spending behaviour into clear, structured categories, making it easy to understand where money is going and how patterns are changing. Instead of reviewing individual transactions, teams see aggregated insight across time periods and cohorts.

This provides fast clarity on cost pressure, behavioural shifts and category-level trends at scale.

How analytics are generated

Transactions are categorised and grouped using consistent rules, then analysed across defined time windows.
The engine tracks changes in category spend, frequency and trend to surface meaningful insight for reporting. Below is an example output:

{
  "categories": [
    {
      "category": "groceries",
      "total_spend": 428.60,
      "change_vs_previous_period": 0.09
    },
    {
      "category": "utilities",
      "total_spend": 192.30,
      "change_vs_previous_period": 0.17
    },
    {
      "category": "subscriptions",
      "total_spend": 79.99,
      "change_vs_previous_period": -0.06
    }
  ],
  "period": "last_30_days",
  "currency": "GBP"
}

Why it matters

Category-level insight helps teams identify emerging pressure, understand behavioural change and track the impact of costs over time without manual analysis.

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Understand spending patterns with clarity.
Category Analytics delivers structured insight to support reporting, benchmarking and decision-making.

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