Counterparty Risk Anomaly Detection
Description
Ginnie Mae is responsible for analyzing counterparty risk profiles of mortgage issuers who participate in Ginnie Mae’s program. Ginnie Mae analyzes data from multiple sources to identify potential risks and areas of focus.
Detailed example
Data patterns
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
To enhance the identification of data patterns, Ginnie Mae uses machine learning algorithms, specifically clustering and genetic techniques. These algorithms detect potential risk areas, enabling a focused approach to subsequent analysis by Ginnie Mae staff.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
This is not a self-learning system. Although the data is not used to train, fine-tune, and/or evaluate performance, the following sources are used by the ML model to perform analysis: - Ginnie Mae (GNMA) Investor Reported Mortgage-Backed Securities (MBS) portfolio data aggregated on an issuer level - MBFRF (Mortgage Banking Financial Reporting Form) Data for counterparty financial information