May 2023 AGENCY INVENTORY OF USE CASES

U.S. DEPARTMENT OF THE TREASURY

1 of 4

AI Use Case Name AI Use Case Summary

Production Stage

Inventory Item 

Replenishment MLR 

Modeling POC - Phase 1a

The Bureau of Engraving and Printing wanted to establish a proof of 

concept (POC) for Predictive Analytics at the BEP. This POC consisted of 

developing a Multiple Linear Regression (MLR) model to predict Processing 

Lead Times for the Office of Supply Chain Management (OSCM). 

Processing Lead Times are the numbers of days it takes an item to be 

delivered to the target facility from the time the purchase order (PO) was 

approved. The model utilizes historical requisition, vendor, and item 

specific data to come up with numerical predictions, which are then used 

to determine whether an item will be delivered on-time or if the OSCM 

should expect a delay. If a delay is expected, the OSCM can be proactive in 

their decision making to prepare for a potential inventory shortage.

Development and Acquisition

Inventory Item 

Replenishment MLR 

Modeling Pilot - Phase 1b

The Bureau of Engraving and Printing wanted to establish a proof of 

concept (POC) for Predictive Analytics at the BEP. This POC consisted of 

developing a Logistic Regression model for the Office of Supply Chain 

Management (OSCM), to predict whether an item would be delivered by 

the specified "Need by Date". This is the date that the BEP needs the 

material in its facility and is set automatically to 128 days when a purchase 

order (PO) is approved in the system. The model utilizes historical 

requisition, vendor, and item specific data to come up with binary (0 or 1) 

predictions, which are then used to determine whether an item will be 

delivered on-time or if the OSCM should expect a delay. If the model 

outputs a 1, we expect that the item will be delayed and the OSCM can be

proactive in their decision making to prepare for a potential inventory 

shortage.

Development and Acquisition
May 2023 AGENCY INVENTORY OF USE CASES

U.S. DEPARTMENT OF THE TREASURY

2 of 4

AI Use Case Name AI Use Case Summary

Production Stage

Inventory Item 

Replenishment MLR 

Modeling POC - Phase 2

The Bureau of Engraving and Printing wanted to operationalize a model 

using their newly deployed Cloudera Data Science Workbench (CDSW) 

application to predict whether an item would be delivered by the vendor 

Promised Date. This date is the date the vendor promises an item to be 

delivered to BEP. The model utilizes historical requisition, vendor, and 

item specific data to come up with binary (0 or 1) predictions, which are 

then used to determine whether an item will be delivered on-time or if the 

OSCM should expect a delay. If the model outputs a 1, we expect that the 

item will be delayed and the OSCM can be proactive in their decision 

making to prepare for a potential inventory shortage.

Development and Acquisition

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 Nuance Natural Language Understanding (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

DATA Act

The Digital Accountability and Transparency Act (DATA) Act Bot automates 

verifying that IRS Federal Procurement Data System (FPDS) reporting 

matches the information in contract documents (e.g. dollar amounts, 

dates, location of work). Natural language processing is used to extract 

unstructured information from contract documents. F1 scores are used to 

measure performance of validation models for each specific data element.

Planned (not in production)

NRP Redesign

Deploy state-of-the-art AI machine learning methods to provide a lower 

opportunity cost method of estimating a compliance baseline to support 

tax gap estimation, improper payments reporting, development and 

validation of workload identification 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
May 2023 AGENCY INVENTORY OF USE CASES

U.S. DEPARTMENT OF THE TREASURY

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AI Use Case Name AI Use Case Summary

Production Stage

TAS Virtual Assistant

The TAS Virtual Assistant Chatbot will capture utterances from 

taxpayers/end-users to direct them to helpful resources on IRS and TAS 

public websites.

Initiation

Account Management 

Chatbot

The Accounts Management Chatbot leverages a natural language 

understanding model within the eGain intent engine. This NLU maps 

utterances to a specific intents, and returns the appropriate knowledge 

article.

Operation and Maintenance

Taxpayer Accessibility -

Machine Translation (MT) 

Taxpayer Accessibility Machine Translation (MT) is a SaaS based 

Commercial Off-the-Shelf (COTS) product that uses Amazon Translate, a 

neural machine translation (NMT) service. The MT solution implements 

customization features in the product which will have capabilities to 

integrate existing Linguistics Policies Tools and Services (LPTS) translations 

and workflows through a centralized repository formed by a collection of 

existing and customized IRS glossaries to return translations from English 

to Spanish (and Spanish to English) that more accurately reflect nativetongue verbiage.

Implementation

CX Analytics

IRS' Customer Experience (CX) Analytics is a capability that uses multiple, 

customer service-related data sources to identify 

issues/anomalies/improvement opportunities across the customer service 

channel modes. 

Implementation
May 2023 AGENCY INVENTORY OF USE CASES

U.S. DEPARTMENT OF THE TREASURY

4 of 4

AI Use Case Name AI Use Case Summary

Production Stage

Appeals Case 

Memorandum 

The Appeals Case Memorandum (ACM) leverages natural language 

processing capabilities to assist with extraction, consolidation, and labeling 

of unstructured text from IRS ACM documents, automatic identification of 

key information, and processing results into a structured format. The 

outcome of this processes is for IRS staff to review appeals information for 

insights, which can be used upstream to enhance case quality, consistency, 

and performance. Summary of results involve detailed analysis on text 

relationships, issues, and citation narrative text paragraphs to provide 

insight on issues commonly adjusted during the appeals process.

Implementation

Coin quality inspection 

system

Automated coin visual inspection tools to search for defects on production 

lines. Currently each coining press operator manually inspects coins for 

quality. Improve quality and eliminate waste. Researching feasibility and 

tools.

Initiation

Predictive equipment 

maintenance system

Predictive maintenance to increase equipment uptime, improve safety, 

lower maintenance cost. Researching feasibility and tools. Initiation