Ways to use machine learning in banking risk?
Trying to think of ways you could use exisiting machine learning techniques on a system-wide scale or individual bank level for the banking industry. Any ideas?
Are you talking about risk of fraud, risk of credit default or anything else? Either way there are already quite a few companies doing this at very large scale, which shouldn't discourage you - just means that you can find some good resources if you dig deep enough.
No idea how many data points you'd need in order to get a reasonably good model but the task at hand would be pretty daunting. After all you need to consider individual risk posed by employees all the way up to systemic and political. On the other hand if successful you'd hit a home run.
Anomaly detection for credit card fraud is a big problem.