Bank Statement Analytics

Analytical metrics for bank statement transactions

What is Bank Statement Analytics?

Bank statement analytics involves analyzing the data within bank statements to gain insights into financial activities. This includes categorizing transactions, identifying trends, and generating reports to help users understand their financial habits and make informed decisions.

Analytics API: API Reference

Why Use Bank Statement Analytics?

  • Lending: Lenders can assess applicants' creditworthiness, evaluate their income stability, and verify their financial information. This helps make informed lending decisions, such as determining the loan amount, interest rate, and repayment terms.
  • Expense Tracking: Offers a comprehensive breakdown of expenses, simplifying the tracking of expenditure details.
  • Financial Planning: Helps individuals and businesses plan their finances better by understanding their spending patterns and cash flow.

Analytics API Example Data

Key Features

  • Transaction Categorisation: Metrics are generated based on predefined categories within transaction data, such as income, expenses, groceries, and rent, among others. The categorization set within the transaction tables forms the basis for these calculations.
  • Expense Tracking and Budgeting: Provides a detailed breakdown of expenses, helping users track where their money is being spent. It would also help users create and manage budgets by providing insights into their spending habits and suggesting areas for improvement.
  • Cash Flow Analysis: Tracks the money flow in and out of an account, helping users understand their cash flow trends.
  • Financial Health Assessment: Offers metrics such as the debt-to-income ratio, savings rate, and other financial health indicators, aiding users in evaluating their overall financial health. Lenders or other financial institutions could also employ these metrics to evaluate an individual's creditworthiness.

API Parameters

  1. Formats Supported: (?output=csv)
    • JSON (default)
    • CSV
    • Excel
  2. Depth of Metrics: (?mode=full)
    • Basic (default)
    • Full / All

The basic metrics will not encompass daily cash flows, monthly analytics, credit-debit analysis, or deposit and withdrawal analysis.


Metrics Description

There are two types of analytical metrics the ones based on the transaction data like amount, date, description, balance, and transaction-type, and the other is based on the transaction data as well as the transaction categories like category, subcategory, and merchant fields.

Transaction data-related metrics

  • Basic Analytics
    • Average Daily Balance: The average balance for each day, mathematically calculated total balance for a given date divided by the total number of transactions on that particular date.
    • Average Weekday Balance: The average balance for weekdays, excluding Saturdays and Sundays, is calculated by dividing the total balance for non-weekend days by the total number of transactions occurring on those days.
    • Average Amount: The total number of amounts divided by the total number of transactions in the bank statement.
    • Max Credit: The maximum value of a credit transaction across all transaction tables for a given document..
    • Max Debit: The maximum value of a debit transaction across all transaction tables for a given document.
  • Active Dates: A key-value pair returning the total number of days between starting and ending date, and the total days the transactions have happened in the bank statement.
  • Monthly Analytics:
    • Month-wise analytics for transactions with the following metrics:
      1. Basic Metrics
      2. Total Amount
      3. Average Amount
      4. Minimum Amount
      5. Maximum Amount
      6. Credits
        1. Total Credit Amount
        2. Average Credit Amount
        3. Minimum Credit Amount
        4. Maximum Credit Amount
      7. Debits
        1. Total Debit Amount
        2. Average Debit Amount
        3. Minimum Debit Amount
        4. Maximum Debit Amount
      8. Average daily balance
      9. Starting balance of a month
      10. Ending balance of a month
      11. Deposits
      12. Withdrawals
  • Quarterly Analytics
    • Quarter (3 months) wise analytics for the transactions with the following metrics:
      1. Total Amount
      2. Total Debit
      3. Total Credit
      4. Average Transaction Amount
      5. Months
  • Days with Negative Balance
    • The number of days the bank statement has a negative balance
  • Abnormal Amount Transaction Analytics
    • Daily: The number of days with details(balance, date, amount) there were transactions with amounts exceeding 50% of the median amount of all the transactions.
    • Weekly: The number of days with details (balance, week-date and range, amount) there were transactions with amounts exceeding 30% of the median amount of all the transactions in that particular week.
    • Monthly: The number of days with details (balance, month, date, and amount) there were transactions with amounts exceeding 30% of the median amount of all the transactions in that particular month.
  • Balance Growth
    • Daily: The percentage of increase in balance for each day with the previous day (it would be 0% for the first-day transaction).
    • Monthly: The percentage of increase in the average monthly balance with the previous month (the balance growth for the first month will be 0%).
  • Daily Cash flows
    • The net amount of transactions for each day.
      • Example: total debit = 1000, total credit = 1200, cashflow = +200

Transaction Category-related Metrics

Metrics related to category-labeled data (categories/subcategories/merchant)

Requirements on the document type/document:

  • Document Type Additional Parameter: enable_category: true
  • Document: Categorisation Result: Successful (enabled: success)

Transaction Category Setup

So the transaction line items in the document should be populated with the categories, subcategories, and merchant values.

  • Non-Sufficient Fund Analytics: Metrics like total numbers of transactions, total amount, average, minimum, and maximum amounts in transactions labeled as Non-Sufficient-Funds related payments which can be in Fees/Debits categories.
  • Expenditure vs Income Analytics
    • Total Income: The sum of the amounts in the document which are categorized as Direct Deposits, Interest,Investments.
    • Total Expense: The sum of the amounts in the document which are categorized as ATM Withdrawals, Debit Card Purchases, Credit Card Purchases, Fees, Foreign Transactions.
    • Flag of is expense greater than the income which compares the total income and total expenses amount and sets as either true or false.
  • NSF Fees Count: The total occurrences of Non-Sufficient Fund Fees type of transactions in the document.
  • Overdraft Fees Count: The total occurrences of Overdraft Fees type of transactions in the document.
  • Deposit Analytics: Metrics for deposit or Direct Depositscategorized transactions with total deposits, maximum deposit amounts, and average deposit amount. Also a specific metric for Cash Deposit with the same metrics for total, average, and maximum deposit amounts.
  • Withdraw Analytics: Metrics for Withdrawalcategorized transactions with total withdrawal amount, maximum withdrawal amount, and the average withdrawal amount.
  • Credit Analysis
    • Intra-Account Transfer: The analytics for credit-type transactions categorized as Intra Account Transfers will involve calculating metrics like the average, minimum, maximum amount, and total amount for transactions falling under this label.
    • Sales: Calculates the average, minimum, maximum, and total amounts for credit transactions labeled as Sales.
    • Loans: Calculates the average, minimum, maximum, and total amounts for credit transactions labeled as Loan Payments.
    • Salaries: Calculates the average, minimum, maximum, and total amounts for credit transactions labeled as Salary Payments.
  • Debit Analysis
    • Credit Card Payments: Illuminates the average, minimum, maximum, and total amounts for debit transactions identified as Credit Card Payments.
    • Taxes: The analytics for debit-type transactions which are categorized as Tax Payments. Metrics with the average, minimum, maximum amount, and the total amount for the transactions with the mentioned label will be calculated.
    • Investments: The analytics for debit-type transactions which are categorized as Investment-related Payments. Metrics with the average, minimum, maximum, and total amount for the transactions with the mentioned label will be calculated.
    • Loan Repayments: Gauges the average, minimum, maximum, and total amounts for debit transactions denominated as Loan Repayments
    • Rent: The analytics for debit-type transactions that are categorized as Rent-related Payments. Metrics with the average, minimum, maximum, and total amount for the transactions with the mentioned label will be calculated.
    • Discretionary Payments: The analytics for debit-type transactions are categorized as discretionary payments. Metrics with the average, minimum, maximum, and total amount for the transactions with the mentioned label will be calculated.
    • Utility Bills: Determines the average, minimum, maximum, and total amounts for debit transactions linked to Utility Bill Payments.
  • Category-based Analytics
    • Basic metrics, such as total, average, minimum, and maximum amounts, will be calculated for each category across all transactions.
    • Credit and Debit fields with basic metrics like total, average, minimum, and maximum amounts.
    • Monthly: Metrics like total, average, minimum, and maximum amount for each category in the transactions in that particular month.
  • Merchant-based Analytics
    • Basic Metrics like total, average, min, and max amount for each merchant in all the transactions.
    • Credit and Debit are based on basic metrics like total, average, minimum, and max amounts.
    • Debit-based metrics will track spending with each merchant
    • Recency: The maximum number of days between two transactions with the same merchant.
    • Continuity: The number of days the transaction with a particular merchant is carried on consecutive days.
  • Average Monthly Income
    • The average amount for transactions labeled as Salaries, Wage, etc.
  • Fixed Cost
    • The basic metrics for transactions related to cost occurring as fixed for each month like Groceries , Money Transfer, Rent, Annual Fee, with a specific merchant.
  • Affordability Analytics
    • The total amount used in a transaction with categories/subcategories like Groceries, Gas, Restaurants, Online shopping, Entertainment, Healthcare, Transportation, etc.
  • Debt Coverage Ratio
    • The ratio of total income to the debt categorized transaction amount in the document.
      • The total amount as income categorized transactions like Salaries, Direct Deposits, Interest, etc.
      • The total amount as debt categorized transactions like Loan, Fees, Overdraft, etc.