Efficient and Smart Email Categorization with NLP and ML

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Overview

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

At A Glance

Industry

FinTech

Region

USA

Duration

12 Weeks

Technical Stack

Python

NLP

pandas

TensorFlow

Keras

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.

Challenge

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.

Solution

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.

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