Introduction

Email processing is a critical aspect of modern communication, especially in business environments. It involves the extraction, classification, and management of information from emails. However, the process is not as straightforward as it seems, as it presents several challenges, including content segmentation, email theme classification, response generation, and email anonymization.

At the DataLab Group, we develop in-house solutions to address various technical and scientific challenges within the group.

Content Segmentation

Content segmentation involves separating the core email from its history and signature. This is crucial in understanding the context and purpose of the email, which may include forwarded messages or reply threads. To accomplish this, various techniques can be employed, ranging from regular expressions to advanced NLP models.

Classification of email content and attachments

Email content classification involves identifying the main topic(s) of the email. This can be achieved through multilabel classification. It may also require recognizing the categories of attached documents using techniques such as computer vision approaches or multimodal approaches that combine both image and text for document classification.

Response Generation

Generating responses to emails is a complex task that involves understanding the email’s content and context and formulating an appropriate response. One possible solution is to choose the appropriate answer from a finite set of possible answers based on expert hand-made rules.

However, an ongoing research topic is the use of Large Language Models for this purpose. The challenge lies in generating responses that are personalized, contextually and grammatically correct, and effectively convey the intended message.

Email Anonymization

Email anonymization involves removing sensitive data from emails, such as names, social security numbers, and other personally identifiable information. This process is crucial for maintaining privacy and complying with data protection regulations, especially when processing sensitive emails in the cloud, for example. It is important to ensure that the email’s overall context and meaning are not affected during this anonymization process.

The problem of email anonymization can be likened to a Named Entity Recognition (NER) task, where each word in an email is classified into a set of predefined personal data fields. Detected fields are then either deleted or replaced with a mask (e.g., [NAME] for names) or replaced with an equivalent fake entity (e.g., replacing the name “Antoine” with “Clément”).

Conclusion

Email processing is indeed a complex task that presents various challenges. However, with the right strategies and tools, these challenges can be overcome. The solutions we propose, which are co-designed with business banking experts to meet their needs, are already deployed within the Credit Agricole group. Additionally, some of these solutions are currently being utilized by back-office departments within the group. This demonstrates the practical application and effectiveness of these solutions in real-world business environments.