Spending patterns at scale

Category Analytics breaks down spending behaviour into clear, standardised categories, making it easy to understand where money is going and how patterns change over time. Rather than reviewing individual transactions, teams receive aggregated insight across periods, portfolios and cohorts.

This provides fast visibility into cost pressure, behavioural change and category-level trends across large datasets.

How analytics are generated

Transactions are categorised using consistent classification rules and grouped across defined time windows.

Spend levels, frequency and movement are analysed by category to surface meaningful trends and changes suitable for reporting and review. An example structured output is shown below:

{
  "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 analysis removes the need for manual transaction review and provides a consistent view of how costs evolve over time.

It helps teams identify emerging pressure, understand behavioural change and compare spending patterns across cohorts using standardised metrics.

Get started

Bring clarity to spending analysis. Category Analytics delivers structured, behaviour-led insight to support reporting, benchmarking and oversight.

Compare plans or book a demo. to see it in action.