Investment Bank Records and Retention Management Case Study

This case study examines the records and retention management practices of a major investment bank that faced legal consequences due to its inadequate approach. The bank was found in contempt of court and fined for failing to properly classify its records. Prior to the analysis, data stewards were responsible for tagging documents with classification information, but the study revealed that only a small fraction of the bank's extensive databases and content repositories had been classified. The analysis highlighted the importance of contextual information, which was scattered across various systems and often obscured by complex terminology. By extracting and organizing this contextual data, including financial reporting structures and employee information, the study achieved a significant improvement in classification accuracy. Utilizing lightweight natural language processing techniques, the classification rate increased to approximately 25%. This initial success has paved the way for further advancements using machine learning to enhance classification processes.