We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals. For paycheck optimization, a quantitative methodology is missing, due to a lack of a suitable problem formulation. To deal with this issue, we formulate the problem as a utility maximization problem. The proposed formulation is able to (i) unify different financial goals; (ii) incorporate user preferences regarding the goals; (iii) handle stochastic interest rates. The proposed formulation also facilitates an end-to-end reinforcement learning solution, which is implemented on a variety of problem settings.

It has been known to create paintings, write poems and even learn languages on its own. But could Artificial Intelligence also make you richer? Last week, it emerged JPMorgan Chase is developing a service similar to the AI-powered ChatGPT which would help customers select investments and give financial advice. Separately banks Goldman Sachs and Morgan Stanley have started testing the tech internally as businesses speed up their apparent AI arms race. It begs the question whether financial advisors will be needed at all in a few years as computers offer a quicker (and cheaper) alternative.