A well-known law firm in the US overcomes the limitations of its current template-based batch process of redacting PII information from credit card statements and various other statements by adopting end-to-end process automation.
Industry & Region: Legal, US
Tech Stack:
Cloud Platform: Azure; Workflow Automation: Power Automate; Storage: Blob Storage; Document Processing: Form Recognizer; Programming Language: Python; Named Entity Recognition (NER); Azure OpenAI
Key Results
Redaction accuracy and improving
Reduction in manual effort
Faster Processing with real-time updates
Our client is a full-service law firm dedicated to meeting the legal and operational needs of the financial sector. They specialize in various practice areas including foreclosures, litigation, bankruptcy, eviction, licensing, regulatory compliance, and replevin.
Our client’s current process of identifying and redacting the PII entities from credit cards and other statements was template-driven, posing multiple challenges. One of the major issues was that the multiple PDF formats were typically mapped to a single template, leading to errors and mismanagement of sensitive information. Any changes in the template or the need to introduce a new statement format required manual intervention where the user had to manually create or update a corresponding template. This slowed down the overall process and left room for human errors.
Another challenge was that the Region of Interest or ROI-based redaction presented a significant risk of partial redaction cases. In some instances, sensitive information would still appear, posing substantial data safety concerns.
To overcome this challenge, we designed an AI-powered solution that used Generative AI and Named Entity Recognition (NER) technologies to improve the process of identification of PII data and redaction. This allowed for a more accurate PII data redaction. The AI solution learns and adapts by analyzing the existing document types, enhancing the overall efficiency and accuracy of the redaction process and removes the dependency over the templates and types of documents.
Another key feature of this solution is its functionality of supporting any new type of documents, along with a real-time model training process. This dynamic capability makes sure the system does not have to undergo heavy manual work each time there’s a need to add another new document type. It also increases accuracy with an added feedback loop. Users can provide their perceptions or flag concerns about redactions, which the model utilizes for ongoing improvement of its algorithms.
This innovative approach was aimed at transforming the end-to-end PII data identification and redaction workflow.
Are you looking to simplify your document processing and protect your sensitive data? Discover how our tailored data and AI solutions can transform your workflows!
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