Cash-flow prediction with machine learning
There are number of different time series forecasting algorithems other than LSTM's and other types of RNN's. In general the other methods that use statistical approaches take less time to run and are easier to understand.

From my experience some of the most important things is the data preprocessing rather than the algorithms (eg finding the seasonal period, finding how many seasonal periods there are identifying the trend to make a stationary series, removing outliers).
Once you have done this then you can evaluate different models with a test train split to see how they perform and choose the best model rather than starting off by choosing an algorithm.

You will probably find some interesting things will trying to account for the monthly seasonality due to the lengths of the months changing over time. One way to adjust for that is to use a dummy variable for the start of the month.

Depending on the problem there are a number of options.
Forecasting using statistical methods:


Naive (uses the mean as the prediction)
Seasonal Naive
Arima (a Class of algorithms)
Tbats


If there is a feature that is known in the forecasting window:


Regressing


If there are multiple Features:


Vector auto regression


I found this site very useful for learning what can be done with time series forecasting. (Its the html version of Forecasting: Principles and Practice Rob J Hyndman and George Athanasopoulos)