Banking — User Intent Classification for Deliverable Automation

Overview

The primary purpose of this DAG is to classify user intents based on their requests, thereby automating the generation of deliverables such as reports and documentation. In the banking industry, accurate intent classification is crucial for ensuring compliance and governance, as it allows for timely and relevant responses to user inquiries. The data pipeline begins with the ingestion of user request logs, which serve as the primary input source. These logs are processed through a series of machi

The primary purpose of this DAG is to classify user intents based on their requests, thereby automating the generation of deliverables such as reports and documentation. In the banking industry, accurate intent classification is crucial for ensuring compliance and governance, as it allows for timely and relevant responses to user inquiries. The data pipeline begins with the ingestion of user request logs, which serve as the primary input source. These logs are processed through a series of machine learning models designed to identify user intents accurately. The processing steps include data cleansing, feature extraction, intent classification, and action triggering. The classification logic is based on trained models that analyze the textual content of the requests, ensuring high precision in intent recognition. Once classified, the appropriate deliverables are generated in either DOCX or PDF formats. Monitoring key performance indicators (KPIs) such as classification accuracy and deliverable generation time is essential for assessing the effectiveness of the DAG. Additionally, a fallback system is activated in case of classification failures, ensuring that user requests are still addressed. This not only enhances operational efficiency but also adds significant business value by reducing manual intervention, improving response times, and ensuring compliance with regulatory requirements.

Part of the Governance & Compliance solution for the Banking industry.

Use cases

• Reduces manual workload for banking staff
• Improves response times to user inquiries
• Ensures regulatory compliance through automated processes
• Increases accuracy of deliverable generation
• Enhances customer satisfaction with timely responses