Problem Statement:

Managing and categorizing expenses can be a time-consuming and error-prone task for businesses, especially when dealing with large volumes of transaction data. Manual classification processes often lead to inconsistencies and inefficiencies, hindering cost transparency and strategic decision-making.

Input:

The input to our text classification and mapping solution comprises expense descriptions extracted from transaction records, invoices, and other financial documents. These descriptions vary in language and format, making it challenging to accurately categorize expenses based on traditional rule-based systems.

Output:

The output of our solution is the automatic mapping of expense descriptions to the correct expense categories. Leveraging the power of NLP and the BERT model, our solution achieves high accuracy in classifying expenses, significantly reducing manual labor costs and time associated with manual categorization processes.

Challenges Faced:

One of the main challenges in this project was accurately classifying expense descriptions that exhibit variations in language, syntax, and context. Additionally, ensuring high accuracy on both training and test datasets posed a significant challenge, requiring careful fine-tuning of model parameters and handling imbalanced class distributions.

Proposed Solution:

Our solution leveraged the BERT (Bidirectional Encoder Representations from Transformers) model, implemented using Python and TensorFlow on AWS infrastructure. By fine-tuning the pre-trained BERT model on expense description data, we trained a highly accurate text classification model capable of automatically mapping expenses to the correct categories. The model’s performance, with approximately 96% training accuracy and 90% test accuracy, significantly reduces manual labeling efforts and provides valuable insights into IT spending patterns.

Summary:

The successful implementation of our NLP-based text classification and mapping solution represents a significant milestone in expense management efficiency and cost transparency. By automating the categorization process, our solution eliminates weeks of manual, low-value, error-prone labeling, enabling businesses to allocate resources more effectively and make data-driven decisions with confidence. With enhancements and fine-tuning expected in future revisions, our solution will continue to deliver even greater accuracy and efficiency, providing businesses with a powerful tool to optimize expense management processes and drive cost savings.