I know it's a general question but what type of analytics can be done in this case? How can we apply machine learning models here?
Topic data-analysis methods parameter predictive-modeling machine-learning
Category Data Science
Common use cases include:
This is usually tackled with anomaly detection.
It requires information on the two transaction parties and using machine learning to figure out when a transaction is out of the norm and flagging as a potential case of fraud.
This is usually tackled with time-series forecasting.
The idea is to predict the amount of transaction that will be done in the next day/week/month for a large amount of users. It requires seasonal information and, in case you have a lot of C2B transactions, analysis on major business.
This is also usually tackled with time-series forecasting. However, this analysis can be done on individual levels and not necessarily on a group of users.
The idea is to predict the date that the next transaction will be executed. This can be useful because there are cases where users will perform a lot of transactions in a short period of time, which could may be grouped together.
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