Transaction Categorization
Important Note
Credit Analysis must be enabled by Inscribe for your account before results are available. You can contact Inscribe via Intercom or by emailing [email protected] to inquire about enabling the service.
Most of our customers use Credit Analysis by inspecting the returned customer or bank account summary statistics. This is usually the easiest approach, as it provides a detailed overview of creditworthiness. However, it can still be useful to see the categorized transactions, either for inspection or to write your own custom logic on top of them.
Inscribe's Transaction Categorization enriches bank statement data and provides insights into the spending of the account holder(s). Raw transactions are parsed from each bank account, and subsequently analysed by our categorization engines to detect the relevant transaction category. Lenders can easily see the reasoning behind the flow of funds through accounts and make data-driven decisions about the credit worthiness of an applicant.
We have defined our Transaction Categorization taxonomy for the purpose of credit analysis. Our categories are split into the following groups:
- Bank Charges
- Interest Paid
- NSFs
- Overdrafts Fees
- Entertainment
- Gambling
- Income
- Interest Received
- Revenue
- Salary
- Loans
- Card Repayments
- Loan Principal
- Loan Repayments
- Mortgage Repayments
- Refunds
- Self-Transfers
- Tax
- Withdrawals
When no transaction category can be detected, the transaction remains uncategorized.
Retrieving Categorized Transactions
The quickest way to get these categorized transactions is to upload a bank statement(s), either via the Inscribe app or the /documents
API endpoint. You can get started with the Inscribe API here.
Once the documents are uploaded, categorized transactions belonging to a customer can be accessed via the /customers/{customer_id}/transactions
endpoint, while all categorized transactions associated with a particular customer bank account can be accessed via the /customers/{customer_id}/bank_accounts/{bank_account_id}/transactions
endpoint.
The documentation for the above endpoints describe the method to return transactions associated with specific categories. The section below describes the various transaction categories that we currently support.
Income - Salary & Revenue
Inscribe's income detectors categorize business and personal earning transactions as revenue and salary, respectively. The transactions identified as salary and revenue are individually grouped and later aggregated to provide more meaningful insights as part of the credit analysis summary statistics. Credit analysis provides cumulative amounts for the period, as well as estimations for the annual revenue and salary.
The list of these transactions can be fetched using the endpoints described in the above section and passing category=income
as an argument.
"data": [
{
"id": "ff91dc2c-5d98-4e12-9836-132678e11121",
"date": "2023-01-01",
"description": "Payroll SFC",
"amount": 40300,
"category": "SALARY"
}
]
Loan
Inscribe's lending activity detectors categorize both loan payment and repayment transactions, and identifies the lending institution or entity associated with each relevant transaction.
Transactions identified under the LOAN
umbrella are returned with the lender information as in the example below. The list of these transactions can be fetched using the endpoints described in the above section and passing category=loan
as an argument.
"data": [
{
"id": "ea88dc2c-5d98-4e12-4534-999886e97732",
"date": "2023-01-04",
"description": "BOI Loan Repayment",
"amount": -5000,
"category": "LOAN"
},
{
"id": "ff45dc4e-6f87-5f12-4114-543216e97766",
"date": "2023-01-05",
"description": "Mortgage Repayment",
"amount": -50000,
"category": "MORTGAGE"
}
]
Bank Charge - NSF & Overdraft
Inscribe's bank charge detectors categorize charges related to bank services, i.e., non-sufficient funds and overdrafts. We also detect refunds for these bank charges and, if a matching refund exists, that credit transaction is matched to the respective charge.
Transactions identified as a bank charge are returned as in the example below, each with corresponding refund information and grouped by the category type. The list of these transactions can be fetched using the endpoints described in the above section and passing category=bank_charge
as an argument.
"data": [
{
"id": "dd99dc2c-5d28-4e98-1260-999886e87680",
"date": "2023-01-04",
"description": "OD Charge",
"amount": -500,
"category": "Overdraft"
},
{
"id": "bc05dc1c-4d11-4d01-9460-631186e09808",
"date": "2023-01-03",
"description": "Insufficient Funds",
"amount": -100,
"category": "NSF"
}
]
Self-Transfer
Self-transfers refer to transactions where the account holder transfers funds between their own accounts, typically between varying account types. Some examples are when an individual transfers money from checking to savings, or from savings to their business account.
For the purpose of credit analysis, Inscribe detects transactions of this kind to provide more accurate insight into true income and expenditure, not just movement of funds. Self-transfer transactions can be debits or credits.
Transactions identified as a self-transfer are returned as in the example below. The list of these transactions can be fetched using the endpoints described in the above section and passing category=self_transfer
as an argument.
"data": [
{
"id": "dd99dc2c-5d28-4e98-1260-999886e23786",
"date": "2023-01-05",
"description": "Transfer from Savings",
"amount": 50000,
"category": "SELF_TRANSFER"
}
]
Withdrawal
Inscribe's withdrawal detectors identify transactions where there was a withdrawal of funds from the relevant bank account. The withdrawal can be any relevant removal of funds, for example, a cash withdrawal through an ATM, or a payment of funds through the Automated Clearing House
Transactions identified as a withdrawal are returned as in the example below. The list of these transactions can be fetched using the endpoints described in the above section and passing category=withdrawal
as an argument.
"data": [
{
"id": "dd99dc2c-3d18-4e07-1260-999886e199872",
"date": "2023-01-01",
"description": "ATM Main Street",
"amount": -10000,
"category": "WITHDRAWAL"
}
]
Tax
Transactions identified by Inscribe's Detectors as being of type "tax" captures those related to federal, state, and local taxation. Tax transactions can be debits or credits to account for both tax payments and tax refunds.
Transactions identified as TAX
are returned as in the example below. The list of these transactions can be fetched using the endpoints described in the above section and passing category=tax
as an argument.
"data": [
{
"id": "ac78dc1c-3d18-4d01-1260-631286e67009",
"date": "2023-01-01",
"description": "Car Tax Payment",
"amount": -70000,
"category": "TAX"
}
]
Entertainment
Inscribe's gambling detector identifies transactions involving casinos, lotteries, or betting. These transactions can be debits or credits to capture payments and earnings.
Transactions identified as being of type GAMBLING
are returned under the higher level ENTERTAINMENT
category and are labelled as in the example below. The list of these transactions can be fetched using the endpoints described in the above section and passing category=entertainment
as an argument.
"data": [
{
"id": "bc05dc1c-3d18-4d01-1260-631286e67533",
"date": "2023-01-01",
"description": "Poker Entry Fee",
"amount": -5000,
"category": "GAMBLING"
}
]
Refund
Inscribe's refund detector identifies credit transactions that are returning either all or part of a previous payment transaction. Note that refunds relating to a specific category are categorized under that category rather than the generic REFUND
. For example, tax refunds are identified from the credit transactions under the TAX
category.
Transactions identified as a refund are returned as in the example below. The list of these transactions can be fetched using the endpoints described in the above section and passing category=refund
as an argument.
"data": [
{
"id": "bc05dc1c-4d12-4d01-1260-631286e23781",
"date": "2023-01-01",
"description": "Accidental Charge",
"amount": 1300,
"category": "REFUND"
}
]
Updated 5 days ago