Trading Partner Code Completion Anomaly Detection
Description
Detect unusual patterns in trading partner code completion using transaction features, user behavior, timing, amount, fund/account, and historical peer benchmarks. The MVP would connect Advana, data catalog, ERPs, feeder systems, data dictionaries, lineage tools and produce read-only recommendations for CDAO, OUSD(C), Component Chief Data Officers, system owners.
AI / analytics pattern
ML anomaly detection
Automation level / stage
human-in-the-loop alert triage
Expected benefit
Higher detection coverage, fewer missed exceptions, better prioritization of high-risk items.
Audit / financial statement impact
Audit traceability and all statements dependent on source data quality
Controls / human review
Human review for exceptions and recommendations; maintain evidence packages, lineage, source citations, model cards, data-quality checks, and periodic QA sampling. Do not use alerts as sole basis for adverse action; require sampled validation and feedback loop.
Data needed
Advana, data catalog, ERPs, feeder systems, data dictionaries, lineage tools; master/reference data; audit logs; policy/control requirements; prior exceptions; relevant document evidence.
Possible metrics
precision/recall of alerts; dollars reviewed; false-positive rate; high-risk exception closure time
MVP scope
Start with one Component/reporting entity and one subprocess (trading partner code completion) for two close/audit cycles; read-only outputs first.
Related material weakness / control objective
Universe of Transactions, data quality and system modernization