Form 990N Machine Learning Model
Description
The goal of this model is to predict non-eligible Form 990N filers as part of a data driven approach to identify non-compliance within the population of exempt organizations (EOs) filling the Form 990N (Issue Control Number (ICN) 121004).
Detailed example
The model output is an Excel workbook with 34 columns including Employer Identification Number (EIN) and a PREDICTION column.
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
Form 990-N filings provide limited information to the IRS, making it difficult for Tax-Exempt Government Entities (TEGE) to detect non-compliance among that population of EOs. This use case provides improved ability to predict non-compliance.
Audit / financial statement impact
The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
Controls / human review
ATO: Yes; PIA: Not published
Data needed
3 years of transactional data for each entity is used with a combination of Hyper-parameter tuning as well as feature engineering to generate a Random Forest classification model. Model performance was assessed by primarily looking at recall and Receiver Operating Characteristic (ROC) curves.