Our client found 74% of their purchase order transactions were missing an assigned cost code. The burden of correcting or re-classifying those transactions fell squarely on the shoulders of project managers.
By leveraging an ensemble of machine learning models, we were able to first process and bulk categorize past financial transactions, then clean error-filled and inconsistent fields to finally label the data into consistent categories.
At 99% accuracy, this data cleansing and classification pipeline allowed project managers to bypass the data clean-up work that would previously cost them hours of tedious labor to go directly to the value-add work of analyzing expenditure patterns.
Our work optimized spend classification, saved countless hours of manual effort and increased data consistency.
The same techniques leveraged to classify unstructured transaction data have already proven to be valuable across other areas of R&D where the presence of unstructured data was previously limiting opportunities for analytics and robust insights.
We helped our client continue their AI journey, delivering immediate value while building the framework for continual improvement in the future.