Generative AI, which is taking the business world by storm, is also opening new possibilities in the field of receivables management. AI-based systems can analyze vast amounts of data, automate many repetitive tasks, and even predict debtor behavior. In this article, we present two innovative projects undertaken by VSoft’s team: “Using OpenAI to Recognize Court Letters” and “Analyzing Messages for Offensive and Prohibited Content.”
Generative AI, a sub-discipline of artificial intelligence, enables the creation and interpretation of diverse forms of data—from text to images and video. In the context of receivables management, where daily handling of court documents requires significant labor, this technology offers groundbreaking solutions. Automated data recognition (OCR), document classification, and the extraction of key information greatly enhance workflow.
How to OCR Court Letters Using OpenAI
The primary goal of this project is to use AI models to automatically analyze and extract key information from court letters. The solution significantly extends the functionality of the VSoft Court Portal Connector, which is integrated with the Common Law Courts Information Portal. Through the use of Azure Document Intelligence and ChatGPT for intelligent processing of unstructured court letters, the system effectively identifies not only basic information, such as the type of judgment or the parties’ data, but also specific parameters, including awarded amounts, types of interest accrued, and payment deadlines. Importantly, this solution can handle various document formats (PDF, JPG, DOCX, scans) and the non-standard structure of letters resulting from the diverse styles of judges and registrars. Automating this process significantly reduces the time lawyers spend manually reviewing documents, streamlines response deadline scheduling, and enables more efficient reporting of case data. The project’s success in receivables management opens up prospects for broader application, representing a substantial step towards fully automated court letter processing.
Analyzing Messages for Offensive and Prohibited Content
This project aims to automatically detect potentially dangerous, offensive, or prohibited content in SMS and audio messages during client communications. Both incoming and outgoing content is analyzed for four main threats: hate speech, violence, sexual content, and self-harm. The Collection Safe Content Service has been integrated with Azure Content Safety, which analyzes content and issues alerts when threats are detected. The analysis results classify the content, and based on these results, the Collection system automatically tags the case and client in the system. Automating the content analysis process enables quicker identification of problematic cases and more effective risk management. The ability to promptly identify undesirable content allows for an immediate response and enhances employee safety, while also preventing potential damage to the company’s reputation if prohibited content originates from its employees.
Summary
By taking over repetitive tasks, AI systems allow employees to focus on the strategic aspects of their work, leading to increased efficiency and better business results. Implementing such solutions not only saves time and resources but also represents a significant step toward modern, automated receivables management.
