Retail banking classification models

Retail banking classification models
on 30 Aug 2016 13:57 PM
  • Rang Technologies
  • Data Science

Now days the retail banking is one of the important business in banking sector, to improve the customer base, retain the existing customer, improve the banking revenue by offering different product to customer.

Today's world is digital, data is raising like population and you have to identify different customer in term of market opportunity, their risk and profit for bank.
Of course, statistics play a key role in this situation. Following are the different model approach for the retail banking model.

1) Cross sell: It is process where you are offering the different product to the customer, let us assume if you are customer having saving/current account and bank want to offer the credit card or demat account.

2) Up sell : It is process where bank is interested to improve the customer relationship, assume you're good customer and bank want to increase your credit limit.

3) Retention technique: Here, problem is bank want to retain their customer to close their account.

Internet resource: https://www.capgemini.com/resource-file-access/resource/pdf/Customer_Cross-Sell.pdf

Data Procedure strategy:

Cross sell, it is customer level model. So, we need to summarize all data with respective customer level. Data is process in such way that single customer having single row in the final data. If model is account level then data is summarize at customer and account level.
Below is the model strategy

retail banking 1

R code and detail process are given in below link:

Summaries the data at customer or customer account level.

https://github.com/Bwarule/Demo

Data( simulated): https://github.com/Bwarule/Demo/blob/master/Transaction_file_output.zip

Code used to simulate data, you can go through this too!

https://github.com/Bwarule/Demo/blob/master/ReatilBanking.R

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