Subledger Data Quality Machine Learning
Description
Ginnie Mae analyzes Master Sub-Servicer (MSS) transaction data on a monthly cadence. The AI solution allows Ginnie Mae to detect anomalies in this data that would not be detected via traditional methods.
Detailed example
Data anomalies
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Through its use of machine learning models, Ginnie Mae has enhanced its ability to identify data inconsistencies and exceptions associated with its MSS transaction data. Through the early detection of these anomalies, Ginnie Mae is able to reduce manual adjustments to financial reporting, which yields cost and time savings.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
This is not a self-learning or self-refining system. Although the data is not used to train, fine-tune, and/or evaluate performance, the following sources are used: - Transaction data from Ginnie Mae Master Sub-Servicers for the defaulted Single-Family non-pooled assets (this data does not include any PII).