An 'AI'* is only as smart as the information you give it
You've got to add your own knowledge of the situation into this. Currently we have a transaction id which only really tells us that there is a transaction, a card number (identifying a user, I assume) and a date.
The date can probably tell you most - what day of the week was it? What season (most sales experience some seasonality)? What time of day?
Comparing several dates can then tell you things like the average gap - deviation on that average.
You can use machine learning models to tell you how good these variables are at predicting the next visit day but you have to create these variables first, the model won't know about seasonality or its effect on sales of ice cream, umbrellas or winter jumpers so you have to use your knowledge of your customer base to pass the right variables to the model.
You might also want to consider the product purchased - if you can see that information - someone who buys a pint of milk or a loaf of bread will probably return for the same goods on a weekly basis (or whenever they run out) but someone who bought a set of screw drivers and returned for a hammer a week later is unlikely to return for the same goods.
The vast majority of work done for most predictive systems is in creating your variables and providing something to train on which will hold the pertinent information.
*I'm assuming here that you're working with a machine learning model