Research, Applied Analytics & Statistics

IRS Office of Research, Applied Analytics and 

Statistics 

COVID-19 related support

Federal Committee on Statistical Methodology 2020 Fall Conference 

Sensitive But Unclassified
Research, Applied Analytics & Statistics

Detecting Suspicious Economic Impact 

Payment (EIP) Returns

August 17, 2020

Sensitive But Unclassified
EIP Statistics

EIP Overview

3 08/17/2020

Economic Impact Payments (EIP) returns present challenges for ID Theft 

detection. These returns include little information and filers generally have 

no consistency or third-party data to help validate legitimate returns. 

Total Return Population

Unnecessary Filing

Specific Suspicious Activity

• About 7M EIP returns filed to date and almost 

half were filed by mid-April 

• Between 5k and 20k EIP returns come in each 

day since filing season ended

• 700k EIP returns had filed a TY 18 return and 

thus did not need to file an EIP return 

• 800k EIP returns had a filing requirement 

based on third party data

• IRS has explored new methods of aggregating 

returns using various linking factors and 

developing new features to protect against IDT

7M 

Returns 

to Date

50k 

Suspicious 

Returns

800k with 

filing req.

700k PY 

returns

Sensitive But Unclassified
EIP Statistics

Finding a Signal 

4

Given the minimal amount of information, we employed a new approach to detect 

ID Theft

➢ Most EIP returns should have few connections to other returns - If they share linking 

factors with many returns it raises suspicion

➢ The number of groups, types of groups, and characteristics of the groups helped 

inform selections

➢ Characteristics included previous ID Theft selection activity, reject rates, leads from 

various sources, and developed features such as e-mail characteristics and zip to IP 

distance

➢ Once we had enough selections, we used those as targets in a selection model to 

help automate the selection process

Some clusters created false signals that required manual review to detect

➢ Homeless shelters, rehab facilities, convents, and other non-profits had groups of 

returns that looked suspicious but were likely not IDT

➢ These returns shared the same address, e-mail and, in some cases, bank account, 

but appeared to be serving populations that perhaps had no address or bank 

08/17/2020

Sensitive But Unclassified
Presentation Title | Office/BOD

Bad Cluster Example

5 Date

Device IP IP-Zip 

Dist.

Bank 

Account

Bank E-Mail Address

1A1A1... 1.1.1.1 605.9 1234 Bank A ag123@mail.com 56 Elm 

1A1A1... 1.1.2.1 608.3 1234 Bank A ag126@mail.com 57 Elm 

1A1A1... 1.1.2.3 610.2 1234 Bank A ag133@mail.com 56 Elm 

1A1A1... 1.1.1.3 601.3 1238 Bank A ag136@mail.com 62 Elm 

1A1A1... 1.1.1.5 605.3 1238 Bank A ag155@mail.com 52 Elm 

• Returns are clustered by Device and, in this case, seem to be using a dynamic IP 

address limiting clustering capability for IP address 

• All returns are using an IP address that seems to be far away from the taxpayer’s zip 

code 

• Returns are going to different accounts but associated with the same bank 

• E-mail addresses have a consistent structure across the cluster 

• Addresses are very similar across the cluster
Presentation Title | Office/BOD

Good Cluster Example

6 Date

Device IP IP-Zip 

Dist.

Bank 

Account

Bank E-Mail Address

2A1A1... 2.1.1.1 3.9 2234 Bank A jerry@help.org 22 Pine

2A1A1... 2.1.2.1 3.9 2234 Bank A jerry@help.org 22 Pine

2A1A1... 2.1.2.3 3.9 2234 Bank A jerry@help.org 22 Pine

2A1A1... 2.1.1.3 3.9 2234 Bank A jerry@help.org 22 Pine

2A1A1... 2.1.1.5 3.9 2234 Bank A jerry@help.org 22 Pine

• Returns are clustered by Device and, in this case, seem to be using a dynamic IP 

address limiting clustering capability for IP address 

• Small IP to Zip distance 

• Returns are going to the same account 

• Same address used across the cluster 

• Same e-mail used across the cluster, but the domain is a .org
Presentation Title | Office/BOD

Manual Investigation and Models

7 Date

At this point, systematic selection of EIP returns is not quite possible

➢ The previous examples show we can use clustering to find suspect activity, but 

differentiating between good and bad can be difficult

➢ There are bad clusters that look just like the good cluster example and bad clusters 

that are different from both examples

➢ Manual investigation allows for a better distinction between good and bad among 

suspicious returns 

Learning from selections may lead to a systematic approach

➢ Over the first six weeks of EIP, teams met daily to discuss findings, emerging patterns 

and questionable clusters

➢ These discussions helped identify feature generation possibilities and combinations 

of features highly associated with ID Theft

➢ Using early selections as targets, a modeling approach has been implemented which 

overlaps with manual selections by about 80% 
Research, Applied Analytics & Statistics

Data Delivery and Support

August 17, 2020

Sensitive But Unclassified
WORKING DRAFT – For Research Purposes Only

| Research, Applied Analytics and Statistics (RAAS) – Informing Strategic Priorities, Accelerating Results

OFFICIAL USE ONLY – SENSITIVE BUT UNCLASSIFIED

9

Lessons Learned 

▪ E-Fax solution used as submission vehicle resulted in poor image quality making OCR process 

difficult 

▪ Form 7200 fillable PDF form did not enforce field formats (text, currency, date, phone number, 

TIN). Field formatting would have improved OCR results 

▪ Existing technologies such as use of bar codes or option to attach completed forms to an e-mail 

routed to designated e-mail address would have improved data quality and produced significantly 

shorter processing times 

▪ The vast majority of F7200 submissions required extensive editing to perfect the data set

RAAS was asked to provide data for the Form 7200 – Advanced Payment of Employer Credits Due to COVID-19. This information was needed to validate the accuracy of these 

claims and to support a “true-up” process when the corresponding Form 94X is filed. This was a challenge due to image quality issues arising from E-FAX submission process.

RAAS pulls F7200 

PDF files from SBSE 

Shared Drive

Process PDFs to 

identify unique F700, 

convert to TIFF and 

extract e-Fax 

transmission data

OCR F7200 TIFF 

images to create 

F7200 raw data file 

in DIN database

Send raw data files 

and TIFF images to 

SOI for review and 

edit

Receive edited 

records from SOI 

and load to CDW

RAAS DMD 

Imaging 

Workflow For 

Form 7200
WORKING DRAFT – For Research Purposes Only

| Research, Applied Analytics and Statistics (RAAS) – Informing Strategic Priorities, Accelerating Results

OFFICIAL USE ONLY – SENSITIVE BUT UNCLASSIFIED

10

RAAS ANALYTICS 

RAAS has developed reusable natural language processing, computer vision, and 

information extraction capabilities that address common IRS data challenges

Natural Language Processing Computer Vision Information Extraction

04: Entity Resolution

IRS data refers to taxpayers in a 

variety of formats across data 

sources; resolving references is key 

to connect disparate data sources

01: Unstructured Text

Unstructured text exists in 

documents of varying types, often 

organized by case or collection of 

documents relating to a taxpayer

06: Embedded Tables

Tables are a common data structure 

within IRS text documents, and are 

frequently non-standard in layout 

and location within a document

05: Excessive Form Text

IRS documents regularly contains 

boilerplate or standard language that 

is not otherwise meaningful toward 

understanding its content or outcome

08: Form Version & Year Variability

IRS forms are frequently revised 

across and within years, which leads 

to inconsistent field presence, 

spacing, and other related challenges

03: Optimization for Volume

IRS has vast amounts of unstructured 

data that require optimized computing 

to systematically process and 

standardize data for analysis

02: Scanned Documents & Images

Taxpayers can submit paper 

versions of forms, which are then 

scanned and stored as images 

along side with traditional PDFs

09: Form Content Extraction

IRS forms are inconsistently 

transcribed, with specific fields often 

needed to be identified, extracted, 

and joined with structured data 

07: Addendums

IRS forms often contain addendums 

with spillover information, which is still 

pertinent but frequently much less 

structured than the form itself

11 User Interfaces

A user interface facilitates wider 

adoption of developed CLI tools by 

lowering barriers to use from 

system commands to be point and 

click

10 Command Line Tools

A command line tool allows end 

users to interact with different 

capabilities from a consolidated 

utility

$ python

RAAS Capability Key:

Considering broad needs across the IRS to 

identify, extract, manipulate, and analyze text, 

Command Line Tools and User Interfaces will 

allow the IRS to leverage advances from RAAS 

text analysis efforts to maximize the ability to 

transfer code pipelines and workflows between 

projects
WORKING DRAFT – For Research Purposes Only

| Research, Applied Analytics and Statistics (RAAS) – Informing Strategic Priorities, Accelerating Results

OFFICIAL USE ONLY – SENSITIVE BUT UNCLASSIFIED

11

22,316 pages processed, 

across over 13,938 forms filed 

to date

Produced data extract for eFAX transmission

Estimated $349M in relief 

payments issued to 4,842 

taxpayers

Computer-Vision: Optical character 

recognition, form box location

Processing Pipelines: N/A

Highlights at a glance

Business Problem

Technical Capabilities

Text: Page level text extraction, text 

classification, text characteristics 

Due to Campus shutdowns due to COVID19 and the implementation 

time-frame for the relief provision, taxpayer were instructed to file 

Form(s) 7200 by accessing and completing a fillable PDF form and 

transmitting to the IRS via e-Fax. There is a need for the data from 

these forms to complete the “true-up” reconciliation process, track 

relief payments made.

Capabilities: 01 02 03 04 05 06 07 08 09 10 11

Technical Problem

Although the Forms were to be filed via PDF fillable forms, the forms 

produced did not enforce any data entry standards nor did they 

leverage computational functionality that would have improved data 

quality and reduced taxpayer errors in completing the forms. 

Additionally, instructions for filing the forms were to e-FAX the form 

to a number provided. E-FAX transmission does not produce quality 

images for OCR.

To overcome these issue the it was necessary to:

• Employ Kofax solution for data extraction

• Leverage SOI/WI resources to identify and correct OCR issues

Form 7200 – Advance Payment of Employer Credits Due to COVID19

SBSE, CI, OTA and RAAS delivery partners were looking to obtain data elements from Form(s) 7200 filed via e-FAX to 

identify relief payments made, identify potential fraudulent activity and to reconcile with Q2 Form(s) 941 and 941 Schedule R
Research, Applied Analytics & Statistics

Statistical Tables update for EIP

August 17, 2020

Sensitive But Unclassified
WORKING DRAFT – for research purposes only

| Research, Applied Analytics and Statistics – Informing Strategic Priorities, Accelerating Results

SENSITIVE BUT UNCLASSIFIED

13

Update on Statistical Tables for Economic Impact Payments (EIP)

▪ Tables will be released on specific landing page in 

the Individual area of TaxStats on irs.gov 

▪ Tables will be released monthly 

▪ Currently working with OTA on language and 

definitions in tables for clarity of what presented 

▪ Three tables planned

▪ Table 3 classified by marital status

▪ Information on number of payments and amounts 

for: 

– Total EIP

The Statistics of Income Division is currently engaged with the Office of Tax Analysis on preparing tables to 

be released presenting data on the Economic Impact Payments [add link to landing page for tax stats]

▪ Table 1 classified by size of Adjusted Gross 

income (11 different size classes)

▪ Information on number of payments and amounts 

for: 

– Total EIP 

– EIP paid electronically 

– EIP paid by paper checks 

– EIP paid by debit card

▪ Table 2 classified by State

▪ Information on number of payments and amounts 

for: 

– Individuals with and without qualifying children 

– Total EIP 

– EIP paid electronically 

– EIP paid by paper checks 

– EIP paid by debit card