Efficient email categorization using NLP & ML technology to reduce response times and boost productivity, efficiently handling inbound emails by departments.

Client Profile

The client is a renowned and leading bank firm, with its presence all around the globe and more than 80k employees. They provide a wide spectrum of top-notch banking services globally.


  • Being a leading banking firm, it is no surprise that every single day it receives a gigantic amount of incoming emails for various departments. It became overwhelming for their accountable employees to keep track of the relevant email with business priority.
  • The client approached us with the requirement to develop a solution that enables filtering and categorizing a mass volume of emails.
Diagram illustrating ML-driven email categorization process: From unsorted emails to organized categories using advanced algorithms for improved productivity.


  • Created a data pipeline to clean and pre-process raw chunks of email data. The data is then fed into ML models to determine the output category of the email and forwarded to the concerned employee.
  • The pipeline contains nodes to standardize email formatting (Removing names, signs, numbers, punctuation) such that the privacy of the emails remains intact without hampering any security compliances.
  • Leveraged the Keras deep learning API of LSTM to determine the themes (Credit Card, loans, mortgage, etc.) within emails.

Key Benefits

  • Improved email turn-around time by at least 50% for customers
  • Increased personal productivity of employees. The pipeline allows filtering such that the accountable employee only receives the relevant email.

Used technologies

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