FinApp: OK to Spend
OK to Spend goes far beyond traditional tools that merely provide static views of finances by applying patent protected machine learning and predictive data analytics to consumer transactions. The application identifies recurring income and forecasted financial obligations while accounting for fluctuations leveraged from historic data.
Financial Forecasting Application
OK to Spend dynamically identifies recurring and forecasted financial obligations and income.
Let's consumers know how much disposable cash they have between paydays
Personalized financial calendar provides a customized central view of upcoming financial events
Personalized and progressive experience offers actionable financial insights that empowers consumers
Timely Notifications and Alerts
Proactive tools help consumers stay on top of day-to-day finances
Best-in-class platform data intelligence and predictive analytics targetconsumers with the right products at the right time
Flexible design enables users to access their financial data across various devices, user interfaces, and platforms
Insights and Advice
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How OK to Spend Works
By applying machine learning AI techniques and data analytics to historical transactions, OK to Spend, part of our series of FinApps, provides an easy way to forecast monthly expenses and balances.
Forward-Looking, Engaging Financial Experience
By identifying recurring and forecasted financial obligations while accounting for fluctuations determined from historic data, OK to Spend goes far beyond traditional tools that merely provide static views of finances.
OK to Spend displays upcoming recurring expenses, paycheck deposits, and financial events in a centralized calendar view. The modern and visually engaging graphical format makes it easy for consumers to see when and how much they’ll be paid and when recurring bills are due. They can also view projected balances on future dates. Personalized insights, tips, and recommendations are provided that help consumers navigate their unique financial journey.