A Large private sector bank automates customer swipe-in process with TruBot leading to 80% cost savings
Case Study

A Large private sector bank automates customer swipe-in process with TruBot leading to 80% cost savings


The client is one of the key players in the Indian banking industry. They have their presence in Asia Pacific, Europe, and North America. It offers a wide range of banking products and financial services for corporate and retail customers


Banking & Financial Services

Bank automates customer swipe-in process with RPA Case Study


The bank had thousands of customers visiting every day in multiple branches across the country. For every customer visit, the swipe-in and swipe-out requests came in due to customer visits at branch, customers calling the call center, and customer accessing the digital banking channels i.e. online banking.

The bank wanted to automate the entire process in order to avoid the duplication of the customer requests and save the additional operational cost.


The Bank has taken major initiatives at all levels to digitally transform their core operation process. Datamatics’ experts conducted the feasibility study to assess the client’s readiness for automation; and then navigated through a well-orchestrated strategy plan by implementing its AI-enabled proprietary RPA solution, TruBot.

In order to make the process error proof, Datamatics designed a web interface to collect all the incoming requests and linked it to the unified desktop system at the bank’s branch / contact centre / CPC from where TruBot could capture customer requests fed in physically by the bank’s representatives.


Reduced Time Taken To Process and Update Customer Request

3 - 4 minutes

The time taken to process the request and update the customer which took 12 hours earlier

Real-time updates - RPA in Banking Case Study

Real-time updates

Received by customers whether it is on execution or asking for more details based on the request.

100% Accuracy - RPA in Banking Case Study

100% Accuracy

In executing the request since there is no manual intervention

80% Reduction

80% Reduction

In resource costs with only one employee handling the requests that get rejected by robots due to some business validation failure or due to system exceptions.

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