Riskscore creation on Numerical Data I am working to create a Risk score on data where I have variables -
Invested_amount, Profit Amount, Age of Account in days, Total Trading Transactions, Profit per Transaction &amp; Investment per transaction. Basically, I want to derive a method to calculate the Risk score, where a person's profit is more (most of the time he is winning). Here the score should be higher so that I can classify them as High Risk (Mostly wins and create a big profit), medium Risk, and Low-Risk customers (Always makes Loss).
Basically, my problem is to assign a Risk score to each customer, the higher the score more the customer is Risky. Once this score is derived then using this score we will segment the customers into three classes.
Below are the variables currently I am using…
|Variable|               Definition|
|username|               UserId to identify the records|
|Total_Freq_transaction| Total Number of trades done|
|Total_Freq_win|         Total number of trade won|
|Account_age_days|       Account activated date - Last trade date |
|Invest_Amount|          Total Amount invested in trade|
|total_profit|           Profit made on trade (pay out amount - Investment Amount)|
|Trade_per_day|          Total Number of trades done / Account age|
|Win_prob%|              Wining probability from total number of trades|

Any help would be greatly appreciated.
There are traditional ways of creating a risk scorecard using linear regression techniques. It's a whole topic in itself. A good book for beginners to read in-depth about it would be this. 

For sure, you can also tackle it as three-class classification problem if you have labelled data for that. Or consider it as binary classification problem and create three buckets out of class probability output by the classifier.