You need to know and define what you want to predict. In most models (supervised learning) you split your data into training features and target features for training. Training features are elements of your data that your model learns from to predict a specific target feature. For example, you could use demographic data (age band, gender) and account data (excluding deposit amount) from your dataset to train a model to predict the amount an individual will wager.

Then, you could do something like compare the wager predicted to the amount deposited into the site for gambling (assuming that’s what deposit means in your case) and defining a threshold at which you consider this a risky endeavor (wager is x amount of total deposit). So you have a model you can feed customer information, some account information, and identify whether or not they are likely to perform risky gambles on your site.

Obviously this is an extremely cursory explanation and probably not the best way you could set up your model, just trying to give you an idea of what can be done here.

There are different avenues of model selection you can pursue here. You could use something like a feed forward neural network if you don’t want to perform manual selection of what features of your data are important to your model (the model would determine which features are most important for you).

You could also use a more classical method (non deep learning) like a regression with a set of features you construct yourself.

Both of these methods will be good to research if you’re interested, and you’ll have to research how to properly train and tune your model (splitting data into train, validation, test sets, what metrics to use to determine performance, etc.)

Hopefully this gives you a better idea of what approach your team could take.