The operations team at a leading asset management firm is responsible for reviewing the General Ledger maintained by their fund administrator.
Reviews were performed only twice a year, due to the team’s workload constraints.
Given the immense amount of data, the team had limited capacity to perform comprehensive reviews.
Errors accumulated for extended period, becoming increasingly problematic over time.
OnCorps configured its platform to continuously ingest General Ledger journal posting data from the fund administrator. The system automatically runs all rules-based checks (e.g., consolidation of sub-funds) and deploys Machine Learning algorithms (e.g., network mapping) to identify issues for which there may not be a predefined rule. Identified issues are presented to the team for review and documented within the system, creating a full audit trail and independent record of the fund administrator’s performance.
The OnCorps system ingests and analyzes data daily and presents issues to the team for review once a month. Within six months, OnCorps’ algorithms detected over 300 accounting issues, totaling over $19M in estimated materiality.
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