This is the start of a series of blog posts where I try to build an online personal budgeting system.
The motivation for this was me trying to do a household budget usin
With Open Banking approaching a common service to classify income and expenditure for bank statement transactions is not currently available on the market.
The ability for a consumer to categorise their transactions sits within varied personal finance providers (eg. pocketbook, mint).
An API that uses Named Entity Recognition to identifies a consumers bank statement into the below groups:
Why –> classification (eg Holiday travel) & sub-category (flights)
Who –> company originating the charge
When –> Date time & location
How –> Method of transfer (EftPos, cash withdraw, direct debit, credit card)
What –> Type of transaction is it? Dishonour, Overdrawn, Interest, Fees, Credit, Debit
- Correct classifications
- API Usage
- Website signups
- Website classification requests
- Percent of statements marked as correct (accurately identified)
- Unique Value Proposition
- We offer an open solution to personal financial statement classification.
- pocketbook – automated classification
- pocketsmith.com – automated classification
- youneedabudget.com – manual classification
- myprosperity.com.au – – manual classification
- Mint – US based no AU presence
We use online machine learning to classify all transactions on a bank statement and categorise them into categories for visualisation.
Potential future features
- Personal finance advice
- Weekly email summary statement of financial portfolio
- Fraud alerts
- Better products alert (mortgage/insurance)
- Tax savings calculator
Free for consumers. Paid for financial institutions that want to use the API
Retail consumers wanting to understand their financial statement
Business consumers wanting financial insight into their customers
- Free for website use
- API cost
Posts in this series
Charge Id – scratching the tech itch [ part 1 ]
Charge Id – lean canvas [ part 2 ]
Charge Id – solution overview [ part 3 ]
Charge Id – analysing the data [ part 4 ]
Charge Id – the prediction model [ part 5 ]
Charge Id – deploying a ML.Net Model to Azure [ part 6 ]