Banking

Understanding the customer expectation and retaining customers and identifying potential customers to Cross/Up-Sell of different Banking products is one of the major threat in the Retail and Wholesale Banking industry. TechVantage's reusable framework helps in understanding the Customer Lifetime Value at an individual customer level through their banking behavior and also predicts their value into the future. This enables the business to allocate appropriate budgets for acquisition and retention.


Case studies

  • Fraud Detection Model

Problem Statement

 

A leading German Bank wants to identify their customers who are likely to fraudulent through their historical information of Fraudsters.

 

Solutions

 

Demographic data like Gender, Marital status, and Job level and transactional data likes.

 

Average monthly balance maintained in savings bank account and their purpose of the loan was found to be influencing.

 

Identification of Fraudulent customers

 

Random Forest Model was built for active and non-active customers by assigning the Status of being Fraudulent as Dependent variable and all other attributes as Independent variable

 

The accuracy of the model was assessed by checking the model results against the 20% customer base (Control Group)

 

Outcome

 

Most of the customers being Fraudulent are the customers who have taken vehicle loan mostly car loan.

 

Based on the analysis, it was observed that single customers who are skilled employees and who maintain an average monthly balance of less than 100 DM (Deutsche Mark) are most likely to be Fraudulent.

 

15 Percent of the customers were identified as potential Fraudsters from the current active customer base and the same has been communicated to their Credit Team.

 

 

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