Let’s say you’re going on vacation with a friend, and you pay for the flight and hotel. Your friend transfers their share back to you – and now the spending stats in your personal finance management (PFM) app are out of whack. You have an inflated vacation expense and increase in income.
But the split transactions feature we just released uses a machine-learning algorithm to identify deposits tied to that expense. With a couple of clicks, those reimbursements you received can be assigned to that restaurant charge – and all is right in your PFM world again.
It works for any kind of transfer that’s received or refunds for returned purchases. Users can also apply a deposit to multiple expenses, and choose the amount that should be credited to a certain expense.
All of this means that all of a consumer’s PFM stats – like reported income, expenses, “left to spend” calculation – reflect reality. “Split transactions” is one of the most requested features we get from consumers because it helps account for the complexities of people’s financial lives. And makes it easier to budget and plan.
It also ensures that financial data is accurate – a must for the data-driven future we’re heading toward in which money is on autopilot. Anyone who plans to more deeply engage with consumers on their personal finances and offer tailored insights using their data – think: credit scores, mortgage interest rates, budget recommendations – needs that data to be as accurate as possible.
Split transactions is in beta mode and is currently being tested with a selected group of customers before fully rolling out in the spring.
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