White paper: Keeping secrets secret – how to protect confidential documents in a global bank
- Accelerate management of confidential data with advanced Natural Language Processing (NLP) and Machine Learning (ML)
- Automate regulatory compliance with systematic classification and prediction of confidentiality categories
- Improve productivity in a single business unit by saving over 40 million dollars’ worth of employee time
A leading European bank wanted to identify confidential data to protect the privacy of its clients more consistently and efficiently. What makes this data difficult to find is that most of it is unstructured textual content from which the sensitive information must be mined. This content is spread across the bank systems, held in multiple formats, and constantly changes. Plus, much of this data is subject to regulations, especially, Non-Public Information (NPI) and Personally Identifiable Information (PII). To mitigate the risks, the bank’s compliance team defined multiple confidentiality categories to apply to all content across the bank. Each employee creating or modifying a document was responsible for evaluating its confidentiality category and tagging it correctly. Only a small percentage of the staff knew and fully understood these classification guidelines.
The resultant cognitive burden imposed on employees came at a high price in terms of lost time, while also distracting private banking staff from building customer relationships. This might have been worthwhile, however, compliance with the process was unsystematic and inconsistent.