I have a dataset of customer transactions (multiple customers,multiple transactions) and based on the historical data, I want to know when a new credit(+ve) transaction arrives if its unusual for that particular customer .
The transaction data comes with 4 fields
customer_no, transaction_type, date_vale,amount
some transactions_types such as
interest (with similar amounts ,not exactly same everytime ) occur in regular interval and
other transaction_types dont have any regular behaviour for any particular customer .
for now what I am doing is calculating mean,median,standard deviation and mad for each customer from the historical data and calculating z score and robust z score for each transaction (for each customer_no ) .
Few of the caveats that I want to mention
all customers dont have same no of data and dont have data spanning everyday of the time period (you can think of this like a normal bank statement for a person , you dont have transactions everyday )
The historical data is not labelled ,that is no field pointing which transaction are unusual and which are not
so my goal is to find characteristic of unusual credit transactions from historical data and find if new transactions are unusual or not based on those characteristics
I am looking for suggestions on building this model.