Need Help: Criteria to create a Risk score

r
machine_learning
data_science
python

#1

I am working to create a Risk score on a data where i have variables -
Invested_amount, Profit Amount, Age of Account in days,Total Trading Transactions,Profit per Transaction & Investment per transaction. Basically i want to derive a method to calculate the Risk score , where a person profit is more (most of the time he is wining) . Here the score should be higher, so that i can classify them as High Risk (Mostly wins and create a big proft) , medium Risk and low Risk customers (Always makes Loss).

Any suggestion on how to create a score would be helpful.


#2

You could create one more column which gives the difference between Invested_amount and Profit Amount per transaction. High Risk customers are the ones who have a huge difference between those two columns. When the difference is of lesser margin, the low Risk customers come in.


#3

Thanks , As your suggestion we can segment the customer in three groups.

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 in three class.

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