August 2022 AGENCY INVENTORY OF USE CASES

U.S. DEPARTMENT OF THE TREASURY

AI Use Case Name AI Use Case Summary Production Stage

Inventory Item 

Replenishment MLR 

Modeling POC - Phase 1

Build and evaluate a multiple linear regression model to predict to 

determine if the replenishment of an inventory item was received before 

or after the need by date to predict the likelihood that an item will be 

received on time in the future.

Planned (not in production)

Inventory Item 

Replenishment MLR 

Modeling POC - Phase 2

Built and evaluate a multiple linear regression model to predict to 

determine if the replenishment of an inventory item would receive by the 

standard Need By time of 128 days set for all inventory items. 

Planned (not in production)

Collection Chat Bot

The Natural Language Understanding (NLU) model will be located inside 

the eGain intent engine. This NLU will take customer typed text input aka 

– Utterances. It will map the utterance to a specific intent and return the 

appropriate knowledge article.

In production: less than six months

Collection Voice Bot

The NLU model will be located inside the Automated Collections IVR (ACI) 

main menu. This NLU will take customer speech input aka – Utterances. It 

will map the utterance to a specific intent and direct the taxpayer down to 

a certain call path.

In production: less than six months

Evaluate Multilingual BERT 

for Software Translation 

Use Case Evaluations

Project is evaluating the cost-effectiveness of training a multi-lingual BERT 

model on IRS corpora and using the model as means to evaluate software 

translation output of IRS content. The framework is leveraging COMET, 

ROGUE, and BLEU measures. Furthermore, the product will also be 

assessed for English-Only and Spanish-Only content content classification. 

In production: less than six months

Large Corporate 

Compliance

Large Corporate Compliance is a machine learning model for classifying 

corporate taxes. 

In production: more than one year

Large Partnership 

Compliance

Large Partnership Compliance is a machine learning model for stratifying 

Partnership data and score risk of potential non-compliance. In production: more than one year

LB&I Text Analytics 

(including Appeals Case 

Management)

Trained text extraction and tax domain-specific BERT models (called 

TaxBERT) using about 190,000 documents including Internal Revenue 

Code, Internal Revenue Manual, and PDFs from irs.gov, Revenue Rulings, 

Private Letter Rulings, Revenue Procedures, Treasury Decisions, and other 

legal tax-related documents. The extracted text was decomposed into 21 

million sentences with 1 million unique tokens. Further filtering 

refinement resulted in 11 million unique sentences and 31 thousand 

In production: less than six months
August 2022 AGENCY INVENTORY OF USE CASES

U.S. DEPARTMENT OF THE TREASURY

AI Use Case Name AI Use Case Summary Production Stage

unique vocabulary tokens which are then used to train domain-specific 

NLP models which can be used for targeted analytics.

Line Anomaly 

Recommender

This use case seeks to identify a workload selection model that uses two 

recommender system models to measure overall compliance risk and 

identify anomalous tax returns and line-item values. The delivered pipeline 

capabilities can supplement the core case selection model processes by 

providing additional insight to IRS LB&I reviewers through the use of 

advanced deep learning techniques for anomaly detection.

In production: less than six months

NRP Redesign

Deploy innovative active learning methods to provide a lower opportunity 

cost method of estimating a compliance baseline to support tax gap 

estimation, improper papyments reporting, development and validation of 

workload identfication and selection models, and inform policy analysis. 

System inputs require existing NRP data which provide an acceptable level 

of precision and quality for an acceptable level of data quality output.

In production: less than one year

Projected Contract Award 

Date Web App

Projected contract award dates are generated with a machine learning 

model that statistically predicts when procurement requests will become 

signed contracts. Input data includes funding information, date / time of 

year, and individual Contract Specialist workload. The model outputs 

projected contract award timeframes for specific procurement requests. 

'When will a contract be signed?' is a key question for the IRS and 

generally for the federal government. This tool gives insight about when 

each request is likely to turn into a contract. The tool provides a technique 

other federal agencies can implement, potentially affecting $600 billion in 

government contracts. Weblink: https://www.irs.gov/newsroom/irsannounces-use-of-projected-contract-award-date-web-app-that-predictswhen-contracts-will-be-signed 

In production: less than one year

SBSE Issue Recommender

Developed an AI-based recommender for detecting potential noncompliance issues which makes training returns selection more efficient In production: less than 6 months
August 2022 AGENCY INVENTORY OF USE CASES

U.S. DEPARTMENT OF THE TREASURY

AI Use Case Name AI Use Case Summary Production Stage

and scalable, which has been applied to the process for selecting training 

returns and field work.