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                    "Vendor Name": "N/A- not a vendor model",
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                    "Platform": "Actimize on-prem",
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                    "Model Owner Support": "Engels, Lisa; Schuele, John",
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                    "Model Purpose": "This is a supervised Machine Learning (ML) Model and was developed to predict an AML Alert\u2019s likelihood to be submitted for further AML review. The data ETL, clean up, preparation, feature curations and transformation were done in Python. The model was developed using an open-source tool called Auto Gluon. The model is deployed in SAM (GCO AML Operations system) with the help from Technology using the Pickle scoring code generated from Auto Gluon and will be scored daily in Production. The model prediction score is used to rank AML alerts and classify them into four Risk Tiers (High, Medium, Low, and Very Low) for alert review triage and prioritization to enhance AML operations efficiency.\n\nThe AML Analyst team reviews hundreds of alerts per month, and most of these alerts are false positives.  Our intent in developing the Risk Severity Model was to look at other factors about the clients and accounts involved in an alert and attempt to predict how likely a given alert is to be referred to the Investigations team for further review and potential SAR filing.  The first version model was successful in its risk classification of alerts; we were able to reduce cycle time of alerts the team needs to review each month for approximately 20% of the alerts. For the alerts in Very low bucket, the AML team will spend less time in the review process. This is the third version of the model where we have used the similar but smaller set of features to retrain the model on latest data to reduce the drift in the model. The second version was implemented in Feb 2023.",
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                    "Model Sponsor": "Flansburg, Brett A",
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                    "Model Purpose": "The Actimize AML: SAM Security Blankets Model is a pair of business rules that evaluate aggregated client activity for risk scenarios aligned with potential financial crime/unusual activity scenarios. There are two rules within the model, Account Security Blanket and Party Security Blanket. The Account Security Blanket rule checks for out-of-pattern peer group account activity and the Party Security Blanket rule checks for out-of-pattern peer group client activity. Both rules cover all clients and all money-in and money-out transactions that are not reversed.\nThis model is a regulatory-related model, as transaction monitoring for suspicious activity associated with financial crimes is a pillar of an effective Anti-Money Laundering (AML) program.  The model is owned by Financial Crimes Compliance and covers activity associated with clients of Ameriprise Financial, Inc. ",
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                    "Date of Last Annual Review": "2026-03-31",
                    "Date of Independent Validation": "2024-11-27",
                    "Model Sponsor": "Flansburg, Brett A",
                    "Open Policy Exception": "No",
                    "Open Findings": "Yes",
                    "Vendor Name": "NICE Actimize",
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                    "Vendor Company": "ACTIMIZE",
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            "List of AI Non Model for Certification": []
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    }
}