Department of the Treasury 

2016 Annual Privacy Act and Data Mining Report 
2

Message from the Deputy Assistant Secretary for Privacy, Transparency, and 

Records 

On behalf of the Department of the Treasury Senior Agency Official 

for Privacy and Chief Privacy and Civil Liberties Officer, I am 

pleased to present Treasury’s Annual Privacy and Data Mining 

Reports for Fiscal Year 2016, as required by Section 522 of the 

Consolidated Appropriations Act of 2005 and the Federal Agency 

Data Mining Reporting Act of 2007. For the fourth year in a row, 

Treasury is combining these two separate reporting requirements into 

a single report. 

Inquiries about this report may be directed to privacy@treasury.gov. 

This report, as well as previous annual reports, can be found on the 

Department’s Privacy Act website. 

Ryan Law 

Deputy Assistant Secretary 

Privacy, Transparency, and Records 

U.S. Department of the Treasury
3

2016 Annual Privacy and Data Mining Reports

Table of Contents

Message from the Deputy Assistant Secretary for Privacy, Transparency, and Records .................................. 2

Statutory Requirements................................................................................................................................. 4

The Reporting Period.................................................................................................................................... 4

The Annual Privacy Report .......................................................................................................................... 4

The Data Mining Report............................................................................................................................... 4

SECTION ONE: DEPARTMENT OF THE TREASURY 2016 ANNUAL PRIVACY REPORT ........ 6

Oversight and Compliance............................................................................................................................ 6

System of Records Notices (SORN)............................................................................................................. 6

Privacy and Civil Liberties Impact Assessments.......................................................................................... 7

Federal Information Security Management Act of 2002 .............................................................................. 7

Section 803 of the Implementing Recommendations of the 9/11 Commission Act of 2007........................ 7

Treasury’s Compliance with Privacy-Related Requirements in OMB M-16-04.......................................... 8

Elimination of the Unnecessary Use of Social Security Numbers................................................................ 8

Privacy Awareness and Training ................................................................................................................... 9

Leadership and Coordination within Treasury ........................................................................................... 11

Treasury Computer Matching Programs..................................................................................................... 11

SECTION TWO: DEPARTMENT OF THE TREASURY 2016 DATA MINING REPORT ............. 13

The Role of the Treasury Chief Privacy and Civil Liberties Officer (CPCLO) ......................................... 13

Treasury Data Mining Activities ................................................................................................................ 14

Conclusion ...................................................................................................................................................... 35
4

Statutory Requirements 

In this report, Treasury consolidates the following two reporting requirements to reduce 

duplication and to provide Congress and the public with a more comprehensive overview of 

Treasury’s privacy compliance and oversight activities: 

(1) The Annual Privacy Report required by Section 522(a) of the Consolidated 

Appropriations Act of 2005; and, 

(2) The Data Mining Reporting Act requirement contained in Section 803 of the 

Implementing Recommendations of the 9/11 Commission Act of 2007, 42 U.S.C. § 

2000ee–3. 

The Reporting Period 

This report covers Treasury activities within the 2016 fiscal year (the reporting period). 

The Annual Privacy Report 

The Annual Privacy Report has been prepared in accordance with Section 522(a) 

of the Consolidated Appropriations Act of 2005, which includes the following requirement: 

Privacy Officer— 

Each agency shall have a Chief Privacy Officer to assume primary responsibility 

for privacy and data protection policy, including— 

* * * 

(6) preparing a report to Congress on an annual basis on activities of the 

Department that affect privacy, including complaints of privacy violations, 

implementation of section 552a of title 5, 11 United States Code, internal 

controls, and other relevant matters; 

* * * 

The Data Mining Report 

The Federal Agency Data Mining Reporting Act of 2007, 42 U.S.C. § 2000ee-3, includes 

the following requirement: 

(c) Reports on data mining activities by Federal agencies 

(1) Requirement for report - The head of each department or agency of the Federal 

Government that is engaged in any activity to use or develop data mining shall 

submit a report to Congress on all such activities of the department or agency under 

the jurisdiction of that official. The report shall be produced in coordination with the 

privacy officer of that department or agency, if applicable, and shall be made 

available to the public, except for an annex described in subparagraph (C). 
5

(2) Content of report - Each report submitted under subparagraph (A) shall include, for 

each activity to use or develop data mining, the following information: 

(A) A thorough description of the data mining activity, its goals, and, where 

appropriate, the target dates for the deployment of the data mining activity. 

(B) A thorough description of the data mining technology that is being used or 

will be used, including the basis for determining whether a particular pattern 

or anomaly is indicative of terrorist or criminal activity. 

(C) A thorough description of the data sources that are being or will be used. 

(D) An assessment of the efficacy or likely efficacy of the data mining activity in 

providing accurate information consistent with and valuable to the stated 

goals and plans for the use or development of the data mining activity. 

(E) An assessment of the impact or likely impact of the implementation of the 

data mining activity on the privacy and civil liberties of individuals, 

including a thorough description of the actions that are being taken or will be 

taken with regard to the property, privacy, or other rights or privileges of any 

individual or individuals as a result of the implementation of the data mining 

activity. 

(F) A list and analysis of the laws and regulations that govern the information 

being or to be collected, reviewed, gathered, analyzed, or used in conjunction 

with the data mining activity, to the extent applicable in the context of the 

data mining activity. 

(G) A thorough discussion of the policies, procedures, and guidelines that are in 

place or that are to be developed and applied in the use of such data mining 

activity in order to— 

(i) protect the privacy and due process rights of individuals, such as redress 

procedures; and 

(ii) ensure that only accurate and complete information is collected, 

reviewed, gathered, analyzed, or used, and guard against any harmful 

consequences of potential inaccuracies. 
6

SECTION ONE: DEPARTMENT OF THE TREASURY 2016 ANNUAL 

PRIVACY REPORT 

Oversight and Compliance 

For Treasury to accomplish its mission, it must collect PII from its employees and the 

public, as well as from various organizations and other government agencies. The Department is 

responsible for managing and protecting the information it collects, maintains, and discloses. 

Federal law, regulations, and policies govern these activities and are designed to maintain the 

public’s trust. 

System of Records Notices (SORN) 

A system of records is a grouping of paper or electronic records maintained by a federal 

agency from which information about an individual is retrieved by the name of the individual or 

another unique identifier assigned to the individual (e.g., Social Security number). Pursuant to 5 

U.S.C. § 552a(e)(4), agencies are required to publish a SORN in the Federal Register for each 

system of records. Treasury has published regulations describing how it collects, maintains, and 

discloses records about individuals that are maintained in a system of records. These regulations 

provide procedures by which individuals may request access to their information maintained by 

Treasury.1

During FY 2016, the Department published three new SORNs in the Federal Register: 

 Treasury/DO .016, Multiemployer Pension Reform Act of 2014 System of Records, March 

16, 2016 (81 FR 14223); 

 Treasury/IRS 10.008, Certified Professional Employer Organizations System of Records, 

July 11, 2016 (81 FR 44924); and 

 Treasury/United States Mint .014, Denver Public Tour and Outreach Reservation System 

of Records, October 27, 2015 (80 FR 65870). 

In FY 2016, Treasury also published 16 reissued and renewed SORNs in the Federal Register: 

 Department of the Treasury .004 Freedom of Information Act/Privacy Act Request 

Records System of Records, September 16, 2016 (81 FR 63856). 

 The Office of the Comptroller of the Currency (OCC) republished its systems of records 

inventory, including 15 SORNs, in the Federal Register on January 19, 2016 (81 FR 2945). 

These republished SORNs are available on Treasury’s Privacy Act website. 

Treasury maintains approximately 211 systems of records, nearly 42 percent of which are 

maintained by the Internal Revenue Service (IRS). The entire Treasury SORN collection was 

                                                            

1 See 31 C.F.R. §§ 1.20-1.36.  
7

updated in 2016 as part of the biennial review process required by the Privacy Act of 1974. A 

complete list of the Department’s SORNs is available on Treasury’s Privacy Act website. 

Privacy and Civil Liberties Impact Assessments 

A Privacy and Civil Liberties Impact Assessment (PCLIA) is an analysis of how 

information is handled in compliance with legal, regulatory, and policy privacy requirements. It 

assesses the risks and effects of collecting, maintaining, and disseminating information and 

discusses the mitigation strategies used to address those risks. Section 208 of the E-Government 

Act of 2002 (E-Gov Act) requires agencies to conduct PCLIAs for electronic information 

systems and collections that involve the collection, maintenance, or dissemination of information 

in identifiable form from or about members of the public. 

 

In FY 2016, Treasury reviewed 234 PCLIAs. Treasury currently has 259 information 

technology systems that require a PIA. Pursuant to the E-Gov Act, agencies are required to make 

PCLIAs publicly available through the agency website, the Federal Register, or other means. 

The Department’s PCLIAs are available on Treasury’s Privacy website. 

Federal Information Security Management Act of 2002 

The Federal Information Security Management Act of 2002 (FISMA) requires each agency 

to develop, document, and implement an agency-wide program to provide security for the 

information and information systems that support its operations. In addition, FISMA requires 

Chief Information Officers, Inspectors General, and SAOPs to report to OMB on information 

security questions that address areas of risk. Federal agencies must report performance metrics 

related to the management of their privacy programs. This entails tracking and reporting the 

number of Treasury systems that contain PII, and the number of systems that require and/or have 

completed a PCLIA and/or SORN. 

For FY 2016, the Department reported a total inventory of 281 FISMA systems that are 

used to create, collect, use, process, store, maintain, disseminate, disclose, or dispose of PII. 

Section 803 of the Implementing Recommendations of the 9/11 Commission Act of 

2007 

Pursuant to Section 803 of the Implementing Recommendations of the 9/11 Commission 

Act of 

2007, agencies must ensure that adequate processes exist to receive, investigate, respond to, and 

redress complaints from individuals who allege privacy or civil liberties violations. To meet the 

requirement, Treasury issued Treasury Directive (TD) 25-09, which directs heads of bureaus and 

relevant offices to establish internal procedures to ensure accurate and complete reporting to 

OPTR. 

The ASM, with the support of OPTR, continues to provide timely Section 803 metrics to 

Congress on behalf of the Department. For FY 2016, Treasury bureaus and its offices performed 

348 reviews, provided advice 15 times, and responded to 27 privacy and civil liberties 
8

complaints. The Department and its bureaus reviewed: 

 114 Privacy Threshold Assessments (to determine whether a Privacy and Civil Liberties 

Impact Assessment was required). 

 234 Privacy and Civil Liberties Impact Assessments. 

 The elimination/redaction or masking of Social Security numbers from 83 forms or 

programs.

 Four Computer Matching Agreements for approval or extension.

 Five privacy complaints, all of which were resolved.

 22 civil liberties complaints, of which 12 were resolved. 

Treasury’s Compliance with Privacy-Related Requirements in OMB M-16-04 

In FY 2016, Treasury initiated processes to identify its High Value Assets (HVAs) to 

comply with OMB Memorandum 16-04, Cybersecurity Strategy and Implementation Plan 

(CSIP) for the Federal Civilian Government (OMB M-16-04). OMB M-16-04 defines HVAs as 

“assets, systems, facilities, data and datasets that are of particular interest to potential adversaries. 

These assets, systems, and datasets may contain sensitive controls, instructions or data used in 

critical Federal operations, or house unique collections of data (by size or content) making them 

of particular interest to criminal, politically-motivated, or state-sponsored actors for either direct 

exploitation of the data or to cause a loss of confidence in the U.S. Government.”2

 Treasury 

identified as HVAs 176 Treasury systems containing PII. All HVA systems that require PCLIAs 

or updated PCLIAs either had them or were in the review process. Updated SORNs for all HVA 

systems were current and up-to-date. 

Elimination of the Unnecessary Use of Social Security Numbers 

Internal Revenue Service (IRS) 

In FY 2016, the IRS continued its efforts to develop and implement written policy and is 

developing updated guidance and an Internal Revenue Manual section related to the collection or 

use of Social Security numbers (SSN). The IRS continues to address the use of SSNs and reduce 

the use of unnecessary SSNs through its SSN Elimination and Reduction program. 

The IRS’s SSN Elimination and Reduction Program made significant strides in eliminating 

or reducing the use of SSNs within systems, forms, notices, and letters where the collection or 

use of the SSN was not necessary. The IRS is systematically reviewing 100% of all existing and 

new notices, letters, and forms for unnecessary SSN use. As of FY 2016, the IRS eliminated or 

reduced the use of the SSNs on 138 payment and non-payment notices, with an estimated annual 

volume of 54 million taxpayer mailings. Wherever possible, the IRS is using a Truncated 

Taxpayer Identification Number (TTIN). A TTIN can be an alternative to using an SSN, IRS 

individual taxpayer identification number (ITIN), or IRS adoption taxpayer identification number 

                                                            

2 OMB M-16-04, Cybersecurity Strategy and Implementation Plan (CSIP) for the Federal Civilian Government, 

October 30, 2015.
9

(ATIN). The filer of certain information returns may use a TTIN on the corresponding payee 

statements to identify the individual being furnished a statement. To further protect identities, 

the TTIN displays only the last four digits of an individual’s identifying number and is shown in 

the format XXX-XX-1234 or ***-**-1234. 

Financial Crimes Enforcement Network (FinCEN) 

The Financial Crimes Enforcement Network (FinCEN) has statutory obligations under the 

Bank Secrecy Act (BSA) and the USA PATRIOT Act to deter and detect criminal activity and 

safeguard financial systems from abuse. This is achieved in large part by the collection, 

maintenance, and sharing of financial information with law enforcement and regulatory agencies. 

This information typically includes SSNs for the individuals who are the subjects of reporting 

mandated by FinCEN regulations. Given the unique role that SSNs currently play in identifying 

individuals in the United States, it is not currently practicable for FinCEN to eliminate the 

collection and use of SSNs in its data collection. If and when alternative mechanisms for 

identifying individuals become widely accepted, FinCEN will provide implementation plans with 

the associated timelines to eliminate the use of SSNs in the related processes. FinCEN will 

monitor its business processes such that a new or modified business process may necessitate a reevaluation of the use of SSNs. 

Privacy Awareness and Training

A Culture of Privacy Awareness 

In M-07-16, Safeguarding Against and Responding to the Breach of Personally 

Identifiable Information, OMB required agencies to train employees on their privacy and security 

responsibilities before granting them access to agency information and information systems. 

Additionally, agencies must provide at least annual refresher training to ensure employees 

continue to understand their responsibilities. Ninety-six percent of all Treasury employees 

completed annual privacy awareness training during the reporting period. 

Internal Revenue Service (IRS) Privacy Training 

Annual Privacy and Unauthorized Access to Taxpayer Accounts (UNAX) training is 

mandatory for all IRS employees. Treasury continuous updates these training modules to address 

emerging issues, support new directives, and meet business needs. For example, the UNAX 

training includes three video vignettes depicting current, real-life workplace dilemmas and the 

resulting violations applicable when policies are not followed. 

In FY 2016, the IRS developed a privacy training series to meet new OMB requirements 

and expectations. The courses are virtual, interactive and available on demand through the IRS 

Enterprise Learning Management (ELMS) system. The series includes: 

1. IRS Privacy Foundation Training; 

2. Privacy Training for Information Technology Specialists; 

3. Privacy Training for Information Technology Designers; 

4. Enterprise Architecture and Data Strategy Officers Privacy Training; and 

5. Cybersecurity Privacy Training 
10

The IRS Privacy Foundation Training is supplemented by four role-based, tactical privacy 

courses for technical employees including Project Managers, Privacy & Civil Liberties Impact 

Assessment Preparers, Business Subject Matter Experts, and Adaptive Privacy Impact 

Assessments Preparers. 

Appropriate, on-demand privacy training is equally important for IRS contractors. The 

IRS overhauled its Privacy Act training to highlight emerging privacy policy issues and make the 

training more applicable to a wider audience including contractors. Additionally, 534 

Contracting Officer Representatives were trained on how to ensure proper privacy, security, and 

disclosure clauses are included in contracts and enforced. Because these clauses are a critical step 

toward protecting privacy during contract execution and closeout, the IRS simultaneously 

conducted Contract Review training for key privacy staff members.

Fiscal Service Privacy Training 

In FY 2016, Treasury’s Bureau of the Fiscal Service focused on role-based privacy 

training. The Fiscal Service defined privacy roles and identified personnel who met the minimum 

requirements for role-based privacy training. The Fiscal Service’s continued efforts include 

working with the Department to ensure the Treasury Learning Management System includes 

adequate privacy training modules. 

IRS Advancements in Privacy Policy and Protection 

The IRS takes a proactive approach to privacy policy development by monitoring 

emerging issues, identifying gaps, issuing policy, and establishing accountability. In 2016, the 

IRS updated and reinforced employee policy and guidance on three privacy issues: personal 

email, electronic messaging, and shared drives. Interim guidance on “Using IRS and Personal 

Email Accounts” reinforced existing policy by providing specific procedures employees must 

follow when sending emails to stakeholders, taxpayers, other IRS employees, and themselves. 

Guidance on “Electronic Message Usage and Preservation” reminded employees that the content 

of an electronic message may be a federal record and, if so, they must manage and protect the 

records accordingly. Interim Guidance on shared drives provided specific policy on methods to 

protect privacy when shared drives contain Sensitive But Unclassified (SBU) data, including tax 

information and PII. 

Fiscal Service Significant Advancements in Privacy-Related Policy 

The Bureau of the Fiscal Service drafted a general privacy policy. In fall 2016, the 

bureau’s policy governance process was reviewing the draft policy, and the Fiscal Service 

anticipates publishing it prior to the end of the year. 

In 2016, Fiscal Service began updating its “Privacy Program Handbook,” which will 

include significant updates regarding the bureau’s Privacy Program framework and additional 

guidance for the Fiscal Service’s business area programs and bureau personnel who access 

Privacy Act-protected data. 
11

Leadership and Coordination within Treasury

Executive Order (E.O.) 13636: Improving Critical Infrastructure Cybersecurity 

In 2013, the President signed E.O. 13636, Improving Critical Infrastructure Cybersecurity, 

stating: “[i]t is the policy of the United States to enhance the security and resilience of the 

Nation’s critical infrastructure and to maintain a cyber environment that encourages efficiency, 

innovation, and economic prosperity while promoting safety, security, business confidentiality, 

privacy, and civil liberties.” 

To ensure the inclusion of privacy and civil liberties protections in activities under the 

Order, section 5(a) of the E.O. required federal agencies to coordinate E.O. 13636-related 

cybersecurity activities with their SAOP. Section 5(b) further required the SAOP to conduct an 

assessment of their agency’s activities under the Order. As required, OPTR conducted a privacy 

and civil liberties assessment of the Department’s cybersecurity activities under the E.O. and 

submitted its assessment to the Department of Homeland Security for inclusion in a consolidated 

public report. The consolidated report is available on DHS’ website. 

Treasury Computer Matching Programs 

Pursuant to the Computer Matching and Privacy Protection Act of 1988,3 Treasury 

maintains a Data Integrity Board (DIB) to oversee its computer matching programs. Computer 

matching programs provide a direct benefit to the public by assisting in the elimination of errors 

and in monitoring waste, fraud, and abuse. 

In FY 2016, the Treasury DIB reviewed and approved three extensions of computer 

matching programs and renewed seven of the Department’s ongoing computer matching 

programs. Matching agreements expires in 18 months after execution unless extended for an 

additional 12-month period. After an extension expires, an agreement may be renewed for an 

additional 18 months. 

Published notices for Treasury’s ongoing computer matching programs are available on 

Treasury’s Privacy Act website. 

 

                                                            

3 Pub. L. No. 100-503.
12

The DIB’s actions included:

Agreement Title Agencies Involved Action Date of Action

Data Loss Prevention Program Internal Revenue Service Renewal January 5, 2016 

Taxpayer Address Request

Program

Internal Revenue Service – 

Department of Justice 

Extension June 6, 2016 

Disclosure of Information to 

Federal, State, and Local 

Agencies 

Internal Revenue Service – 

Social Security Administration 

Renewal October 1, 2015 

Disclosure of Information to 

Federal, State, and Local 

Agencies 

Internal Revenue Service – 

Department of Veteran Affairs 

Extension October 1, 2015 

Disclosure of Information to 

Federal, State, and Local 

Agencies 

IRS – (50) States 

*This includes 50 separate 

agreements.

Renewal October 1, 2015 

CMA 1038-Supplemental 

Security Income 

Bureau of the Fiscal Service – 

Social Security Administration 

Renewal May 2, 2016 

CMA 1304-Medicare Part D 

Prescription Benefit Program 

Bureau of the Fiscal Service – 

Social Security Administration 

Renewal February 26, 2016 

Verification of Household Income 

and Family Size for Insurances 

Affordability Programs and 

Exemptions 

Internal Revenue Service – 

Department of Health and 

Human Services Exemptions 

Renewal September 20, 2016

Prescription Drug Subsidy 

Program IRS Project 692 SSA 

Match #1305 

Internal Revenue Service – 

Social Security Administration 

Extension September 27, 2016

Title XVI Benefits Internal Revenue Service – 

Social Security Administration 

Renewal November 20, 2015
13

SECTION TWO: DEPARTMENT OF THE TREASURY 2016 DATA 

MINING REPORT 

The Role of the Treasury Chief Privacy and Civil Liberties Officer (CPCLO) 

The Department of the Treasury (Treasury or the Department) is providing this 

report to Congress pursuant to Section 803 of the Implementing Recommendations of the 

9/11 Commission Act of 2007 (9/11 Commission Act), entitled the Federal Agency Data Mining 

Reporting Act of 2007 (Data Mining Reporting Act or the Act). This report discusses activities 

currently deployed or under development in the Department that meet the Data Mining 

Reporting Act’s definition of data mining. The report also provides the information the Act 

requires with respect to each data mining activity. 

Definitions 

(1) DATA MINING. The term “data mining” means a program involving pattern-based 

queries, searches, or other analyses of one or more electronic databases, where: 

a. a department or agency of the Federal Government, or a non-Federal entity acting 

on behalf of the Federal Government, is conducting the queries, searches, or other 

analyses to discover or locate a predictive pattern or anomaly indicative of 

terrorist or criminal activity on the part of any individual or individuals; 

b. the queries, searches, or other analyses are not subject-based and do not use 

personal identifiers of a specific individual, or inputs associated with a specific 

individual or group of individuals, to retrieve information from the database or 

databases; and 

c. the purpose of the queries, searches, or other analyses is not solely— 

i. the detection of fraud, waste, or abuse in a Government agency or 

program; or 

ii. the security of a Government computer system. 

(2) DATABASE. The term “database” does not include telephone directories, news 

reporting, information publicly available to any member of the public without payment 

of a fee, or databases of judicial and administrative opinions or other legal 

research sources.4

Three Treasury bureaus maintain systems using applications that meet the definition of 

data mining: the Financial Crimes Enforcement Network (FinCEN), the Internal Revenue Service 

(IRS), and the Alcohol and Tobacco Tax and Trade Bureau (TTB). These IRS, FinCEN, and 

TTB systems were discussed in previous Treasury data mining reports. 

                                                            

4 42 U.S.C § 2000ee-3(b)(1). “[T]elephone directories, news reporting, information publicly available to any 

member of the public without payment of a fee, or databases of judicial and administrative opinions or other legal 

research sources” are not “databases” under the Act. § 2000ee-3(b)(2). 
14

Treasury Data Mining Activities 

FinCEN Data Mining Activities 

(A) A thorough description of the data mining activity, its goals, and, where appropriate, the 

target dates for the deployment of the data mining activity. 

FinCEN’s mission is to safeguard the financial system from illicit use, combat money 

laundering, and promote national security through the collection, analysis, and dissemination of 

financial intelligence and strategic use of financial authorities. To accomplish its mission, 

FinCEN provides financial intelligence, data stewardship, and support for law enforcement. 

FinCEN also engages in the detection of trends and typologies of money laundering and terror 

finance. FinCEN strives to respect privacy and civil rights and the exercise of civil liberties 

while overseeing the data it maintains and uses in fulfillment of its mission as set forth under the 

USA PATRIOT Act, Public Law 107-56, October 26, 2001. 

In furtherance of this goal, FinCEN is required to maintain a government wide data 

access service with a range of financial transaction information; to conduct analysis and 

dissemination of information in support of law enforcement at the federal, state, local, and 

international levels; to identify emerging trends and methods in money laundering and other 

financial crimes; to serve as the Financial Intelligence Unit of the United States; and to carry out 

other delegated regulatory responsibilities. FinCEN’s legal authorities are codified at 31 U.S.C. 

§ 310. FinCEN works to achieve its mission while avoiding the collection and indexing of 

information on persons exercising their constitutional rights and civil liberties. 

FinCEN’s analysts use various data analysis techniques for generating leads on subjects 

or institutions whose activities warrant outreach, investigation, or other statutorily mandated 

activities. 

FinCEN has successfully developed algorithms to identify activity associated with 

specific types of financial crimes, such as check cashing activity associated with health 

insurance fraud. FinCEN also uses algorithms to examine filing patterns across financial 

sectors. This analysis supports a broad range of objectives from the identification of trends and 

patterns of illicit financial activity to the detection of institutions that may require additional 

regulatory oversight. 

FinCEN continues to develop and expand the use of automated business rules to rapidly 

surface high value reports of illicit financial activity on a daily basis. The term “business rule” 

refers to automated queries or algorithms designed to screen incoming Bank Secrecy Act (BSA) 

findings against known criteria to identify high priority filings likely to require further review or 

analysis. Rule findings are reviewed internally by FinCEN and distributed to external 

stakeholders, such as law enforcement and our foreign financial intelligence unit (FIU) partners. 

FinCEN’s business rules play a vital role in the identification and dissemination of timely 

financial intelligence to combat threats such as terrorist financing, money laundering, cyber 

threats, and other illicit financial activity. 
15

(B) A thorough description of the data mining technology that is being used or will be used, 

including the basis for determining whether a particular pattern or anomaly is indicative of 

terrorist or criminal activity. 

FinCEN leverages two principal methods for deriving information relevant to illicit 

financial activity from the BSA data. The first is content driven, that is, searching for specific 

entity names, or term combinations used in reporting that are associated with various types of 

illicit financial activity. The second method is pattern driven and can take various forms. 

Patterns may be derived from searches for a particular type of subject in the data. FinCEN then 

identifies subjects that fit that same pattern and have certain filing profiles. Matching filing 

patterns across different types of BSA reports highlights anomalous behavior that leads to the 

identification of subjects for potential investigation. 

For content driven data analysis, FinCEN staff leverage a web-based application called 

FinCEN Query. The application provides analysts with the capability to search for specific 

entity names and term combinations across all of FinCEN’s records. For pattern driven 

analysis, staff leverage FinCEN’s “Advanced Analytics” system. This system is comprised of 

commercial off-the-shelf (COTS) and custom developed tools with capabilities including 

statistical, social network, and geospatial analysis, data modelling and visualization, and text 

analytics that aid in the analysis of BSA data. 

(C) A thorough description of the data sources that are being or will be used. 

BSA reports administered by FinCEN, e.g., a report by a financial institution of a 

suspicious transaction relevant to a possible violation of law or regulation,5

 form the underlying 

data for FinCEN’s manual and automated search methods and trend analysis activities. 

To accomplish its mission and give context to the data FinCEN extracts from its BSA 

database, FinCEN must consider other information available to it through a variety of sources, 

including open source material, law enforcement information, other government information, 

and subscription services. This information is used to support or amplify conclusions or 

hypotheses derived from the analysis of BSA data. For example, commercially available 

databases are used to support or further identify information and to aid in the identification of 

potential illicit activity based on suspicious trends, patterns, or methods. FinCEN’s trend 

analysis uses any records available to it in fulfilling its mission, including subpoenaed financial 

records, public source information, commercial database information, and third party data 

sources, such as Census Bureau, Social Security Administration,6

 and Office of Foreign Assets 

Control data. 

                                                            

5 31 U.S.C. § 5318(g).

6 The Death Master File is Social Security Administration (SSA) information used by medical researchers, 

hospitals, medical programs, and law enforcement agencies and other government agencies to verify a 

person’s death and to prevent fraud. Although it is SSA information, the National Technical Information 

Service in the Department of Commerce maintains the database. For more information, please visit the NTIS 

website. 
16

(D) An assessment of the efficacy or likely efficacy of the data mining activity in providing 

accurate information consistent with and valuable to the stated goals and plans for the use or 

development of the data mining activity. 

FinCEN provides strategic and tactical products for several audiences: law enforcement, 

foreign Financial Intelligence Unit (FIU) partners, financial regulators, the financial industry, 

and the general public. Each of these sets of consumers has different restrictions or guidelines 

under which FinCEN can provide BSA data or BSA data derived analysis. 

In FY 2016, FinCEN produced a total of 1,110 intelligence products for law enforcement 

partners and responded to 996 requests for BSA information from foreign FIU partners. For 

domestic and foreign law enforcement partners, FinCEN provides high value data analytics. 

FinCEN annually receives the results of surveys of its foreign Egmont member counterparts and 

domestic law enforcement agencies regarding the utility of its analytical products. These survey 

results consistently reflect positive feedback from our foreign and domestic stakeholders. 

FinCEN also receives feedback on individual reports from law enforcement and regulatory 

agencies on our efforts to combat terrorism financing, healthcare, mortgage, and government 

programs fraud, southwest border narcotics, and bulk cash smuggling. Examples of several 

analytical projects that received significant positive feedback are outlined below: 

 To combat threats related to ISIL and Foreign Terrorist Fighters (FTFs), FinCEN has 

conducted extensive network analysis designed to identify potential FTFs and 

facilitators, developed targeted business rules to identify individuals who may seek to 

travel abroad to fight with ISIL, and partnered with domestic law enforcement agencies 

such as Customs and Border Protection to help interdict individuals returning from 

conflict areas that may pose a threat to the United States. These efforts have received 

significant positive feedback from both domestic law enforcement agencies and the 

intelligence community. 

 To combat threats related to transnational organized crime (TOC), FinCEN, working in 

conjunction with a number of federal law enforcement agencies, developed an algorithm 

designed to identify high volume funnel account activity. FinCEN’s law enforcement 

partners have indicated that TOC organizations often use funnel accounts to move their 

illicit proceeds. To combat this threat, FinCEN designed an algorithm that law 

enforcement can use to target the highest volume funnel accounts. Initial feedback on 

the algorithm from law enforcement agencies, such as IRS-CI, has been very positive. 

 To combat terrorist financing threats, FinCEN has developed more than 31 business 

rules designed to identify and disrupt these organizations’ revenue streams and target 

their financial support networks. These rules generate more than 1,250 leads per month 

that FinCEN disseminates to the law enforcement, intelligence, and FIU communities 

via expedited “Flash Reports.” Flash Reports are designed to provide critical 

intelligence to FinCEN’s stakeholders on a timely basis. Since the inception of the 

program in late 2014, FinCEN has disseminated more than 1,500 counter terrorist 
17

financing Flash Reports. Feedback on these reports has been extremely positive, with 

stakeholders noting that the reports helped corroborate information related to 

investigations and provided new leads, assisted investigators in identifying targets, 

cultivated sources, resulted in the identification of more than 30 previously unknown 

foreign terrorist fighters, and assisted in the real-time interdiction of an outbound 

shipment of concern. 

FinCEN narrowly tailors its business rules to achieve its mission, and each rule is 

developed, tested, implemented, and re-tested for efficacy throughout its deployment. The 

Office of Chief Counsel and the Technology Division are engaged during the development of all 

business rules. FinCEN continues to receive strong positive feedback both from our domestic 

and international partners on the value of the intelligence derived from our business rules 

program. 

Finally, FinCEN provides annual aggregated statistics on SAR data by sector to the 

public in a publication titled “SAR Stats” and provides an interactive SAR Stats module for 

SAR Statistical data searches. The most recent version of SAR Stats was published on 

FinCEN’s website in October 2015. Data indicate that in FY15, readers accessed the 

publication 271,276 times, and that the interactive SAR Stats module was accessed 295,810 

times. This is a total of 567,086 accesses for SAR statistical information, an indication of the 

data’s high utility. 

(E) An assessment of the impact or likely impact of the implementation of the data mining 

activity on the privacy and civil liberties of individuals, including a thorough description of the 

actions that are being taken or will be taken with regard to the property, privacy, or other 

rights or privileges of any individual or individuals as a result of the implementation of the 

data mining activity. 

The impact of FinCEN’s congressionally mandated mission on the privacy and civil 

liberties of individuals has been and will continue to be minimal. As a threshold matter, the 

Supreme Court has ruled that the financial information that banks and other financial 

institutions hold, and that FinCEN collects and analyzes pursuant to its authority in 31 U.S.C. § 

310 and the BSA (discussed in more detail in item (F) below), carries no constitutionally 

protected “expectation of privacy.” Moreover, the Right to Financial Privacy Act of 1978 

expressly provides that it gives no protection for financial records or information required to be 

reported in accordance with any federal statute or regulation, which includes information 

contained in BSA reports. 

Significantly, FinCEN takes no adverse actions against individuals based on the existence 

of, or information contained in, BSA data. Since a BSA report itself is not necessarily 

indicative of criminal activity, it is only useful when viewed in conjunction with other evidence. 

Therefore, FinCEN provides the data, or analytical products analyzing the data, to outside 

agencies where the information may be relevant to current or potential investigations or 

proceedings under the jurisdiction of those agencies. 

During the development of all business rules, analytical models, and algorithms, FinCEN 
18

documents a description of why the analytics will not adversely affect an individual or entity’s 

privacy or civil liberty rights. This documentation is shared with both FinCEN’s Technology 

Division and its Office of Chief Counsel. 

The BSA provides standards for proper use of the financial data collected by FinCEN. 

The collected information is also generally subject to the Privacy Act of 1974, discussed in 

more detail under item (F) below. FinCEN has developed extensive policies and procedures to 

ensure, to the extent reasonably possible, that: (1) the analyzed information is used for purposes 

authorized by applicable law; and (2) the security of the information is adequately maintained. 

Analytical products produced by FinCEN are subject to clearly specified restrictions regarding 

use and further dissemination of the products to ensure that the products will only be used by 

appropriate agencies for statutorily authorized purposes. To the extent such products reference 

information collected pursuant to the BSA, FinCEN has issued guidelines requiring user 

agencies to attach warning language to such products and to follow specific procedures for 

further dissemination of the BSA information. These procedures aim to ensure that: (1) only 

appropriate agencies will have access to the information; (2) the information will be used for 

statutorily authorized purposes; (3) agencies with access are aware of the sensitivity of the 

material; and (4) FinCEN will be able to track which agencies have such materials in their 

possession. 

FinCEN posts Privacy Impact Assessments (PIA) on their public website, which informs 

the public of FinCEN activities and practices related to the collection, processing, retention, and 

distribution of personally identifiable information (PII).7

 The PII these systems handle is 

necessary to assist regulators and law enforcement in identifying and monitoring the financial 

activities of individuals who are potentially committing financial crimes. 

(F) A list and analysis of the laws and regulations that govern the information being 

or to be collected, reviewed, gathered, analyzed, or used in conjunction with the data mining 

activity, to the extent applicable in the context of the data mining activity. 

1) The Bank Secrecy Act, 31 U.S.C. § 5311, et seq. (BSA) and Implementing 

Regulations, 31 C.F.R. Chapter X, et seq: 

31 U.S.C. § 5311— Declaration of Purpose 

This section specifies that the purpose of the recordkeeping and reporting requirements in the 

BSA is to, “require certain reports where they have a high degree of usefulness in criminal, tax, 

or regulatory investigations or proceedings, or in the conduct of intelligence or 

counterintelligence activities, including analysis, to protect against international terrorism.” 

FinCEN strives to ensure that all uses of information are consistent with this purpose. 

31 C.F.R. § 1010.301 — Determination by the Secretary 

                                                            

7 For more information about FinCEN PIAs, please visit FinCEN’s website.
19

This regulation provides the determination that the reports collected pursuant to the BSA have a, 

“high degree of usefulness,” in the areas covered by 31 U.S.C. § 5311. 

31 U.S.C. § 5319 — Availability of Reports 

This section makes it clear that, upon request, the Secretary (as delegated to FinCEN) is required 

to provide BSA information for the purposes described in this section, to agencies including state 

financial institutions supervisory agencies, United States intelligence agencies, or self-regulatory 

organizations registered with the Securities and Exchange Commission or the Commodity 

Futures Trading Commission. This list of types of agencies is not exhaustive, but those listed are 

clearly covered. This section also provides that reports collected pursuant to the BSA are exempt 

from disclosure under the Freedom of Information Act, 5 U.S.C. § 552. 

31 C.F.R. § 1010.950 — Availability of Information 

This section authorizes the Secretary to make BSA information available to appropriate agencies 

for purposes specified in the BSA, and specifies that the requesting agency is to receive the 

information, “in confidence.” 

31 U.S.C. § 5313 — Reports on domestic coins and currency transactions 

This section provides for the reporting by financial institutions of reports of certain currency 

transactions involving more than an amount specified by the Secretary (as delegated to FinCEN). 

31 C.F.R. §§ 1010.311; 1021.311 — Reports of transactions in currency 

These regulations implement the reporting requirement of 31 U.S.C. § 5313 and specify the 

amount of reportable transactions in currency at more than $10,000. 

31 U.S.C. § 5316 — Reports on exporting and importing monetary instruments 

This section requires reports by those that transport currency or other monetary instruments of 

more than $10,000 at one time from outside the United States into the United States, or from the 

United States outside the United States. 

31 C.F.R. § 1010.340 — Reports of transportation of currency or monetary instruments 

This regulation implements the reporting requirement of 31 U.S.C. § 5316 with respect to 

currency or other monetary instruments of more than $10,000 imported into the United States or 

exported outside the United States. 

31 U.S.C. § 5314 — Records and reports on foreign financial agency transactions 

This section authorizes the Secretary (as delegated to FinCEN) to prescribe regulations requiring 

the reporting of certain types of foreign transactions and relationships with foreign institutions. 
20

31 C.F.R. § 1010.350 — Reports of foreign financial accounts 

This regulation, implementing 31 U.S.C. § 5314, requires that U.S. persons file reports of foreign 

bank accounts. 

31 U.S.C. § 5318(g) — Reporting of suspicious transactions 

This section authorizes the Secretary (as delegated to FinCEN), to require the reporting of 

suspicious transactions relevant to a possible violation of law. The section also provides for the 

confidentiality of such reports, barring financial institutions from notifying anyone involved in 

the transaction that the transaction has been reported. Government employees are subject to the 

same confidentiality restrictions, except as “necessary to fulfill the official duties” of such 

employees. The policies and procedures detailed above in response to item (E) are aimed, in 

large part, at maintaining the confidentiality of these reports. 

31 C.F.R. §§1010.320; 1020.320; 1021.320; 1022.320; 1023.320; 1024.320; 1025.320; 1026.320 

— Reports of Suspicious Transactions 

These regulations implement 31 U.S.C. § 5318(g), requiring covered financial institutions to file 

suspicious activity reports and requiring the maintaining of strict confidentiality of the reports. 

31 U.S.C. § 5331 — Reports relating to coins and currency received in nonfinancial trade or 

business 

This section provides for the reporting of currency transactions of more than $10,000 by 

businesses other than financial institutions. 

31 C.F.R. § 1010.330 — Reports related to currency in excess of $10,000 received in a trade or 

business 

This regulation implements 31 U.S.C. § 5331. 

2) The Privacy Act of 1974 (Privacy Act), 5 U.S.C. § 552a 

Generally, the Privacy Act protects reports that FinCEN collects pursuant to the BSA as 

these reports are “records” contained in a “system of records.”8

 The Privacy Act provides that 

covered records may be disclosed without the permission of the individual to whom the record 

                                                            

8

 5 U.S.C. § 552a(a)(3) (defining a “record” to mean any item, collection, or grouping of information about an 

individual that is maintained by an agency, including, but not limited to, his education, financial transactions, 

medical history, and criminal or employment history and that contains his name, or the identifying number, symbol, 

or other identifying particular assigned to the individual, such as a finger or voice print or a photograph and a 

“system of records” to mean a group of any records under the control of any agency from which information is 

retrieved by the name of the individual or by some identifying number, symbol, or other identifying particular 

assigned to the individual);
21

pertains if they are disclosed pursuant to a “routine use.”9 FinCEN includes sets of routine uses 

in its published Systems of Records Notices (SORNs) as the Privacy Act requires. These routine 

uses identify the individuals and organizations external to Treasury with which FinCEN routinely 

shares BSA information. Sharing with these specified recipients is consistent with the purposes 

for which the information is collected, as specified in the BSA. 

FinCEN has three SORNs that cover the information it collects under the BSA: 

(1) Treasury/FinCEN .001, FinCEN Investigations and Examinations System10; 

(2) Treasury/FinCEN .002, Suspicious Activity Report (SAR) System11; and, 

(3) Treasury/FinCEN .003, Bank Secrecy Act (BSA) Reports System.

12

FinCEN followed Privacy Act procedures (including appropriate public notice and 

comment periods) to exempt certain records maintained in the SARs and BSA systems of records 

from specific provisions of the Privacy Act, including those allowing for subject’s access to the 

reports, notification to the subject when reports are shared, requests for correction of the contents 

of such reports by the subject, and the civil remedies covering these areas. These exemptions 

prevent individuals who are planning crimes from avoiding detection or apprehension or 

structuring their operations to avoid detection or apprehension. 

3) Other Relevant Provisions 

31 U.S.C. § 310— Financial Crimes Enforcement Network 

This section establishes FinCEN as a bureau in the Department of the Treasury, sets out 

the duties and powers of the Director, and empowers the Director to administer the BSA to the 

extent delegated by the Secretary of the Treasury.13 This section also requires FinCEN to 

maintain a “government-wide data access service” for the information collected under the BSA, 

as well as records and data maintained by other government agencies and other publicly and 

privately available information.14 FinCEN is required to “analyze and disseminate” the data for 

a broad range of purposes consistent with the law.15 These purposes include identifying possible 

criminal activity; supporting domestic and international criminal investigations (and related civil 

proceedings); determining emerging trends and methods in money laundering and other financial 

crimes; supporting the conduct of intelligence and counterintelligence activities, including 

analysis, to protect against international terrorism; and supporting government initiatives against 

money laundering. 

The section further requires that FinCEN furnish research, analytical, and informational 

                                                            

9 5 U.S.C. § 552a(b)(3).

10 79 Fed. Reg. 20969 (April 14, 2014). 11 Id. at 20972.

12 Id. at 20974.

13 Treasury Order 180-01, Financial Crimes Enforcement Network (July 1, 2014) (delegating to the Director of 

FinCEN various duties and responsibilities, including the authority to administer, implement, and enforce the BSA).

14 31 U.S.C.§ 310(b)(2)(B). 15 Id. at § 310(b)(2)(C)(i)-(vii).
22

services to financial institutions and domestic and foreign law enforcement agencies for the, 

“detection, prevention, and prosecution of terrorism, organized crime, money laundering and 

other financial crimes,” and provide, “computer and data support and data analysis to the 

Secretary of the Treasury for tracking and controlling foreign assets.”16 The section also 

provides for the establishment of standards for making the information available through 

efficient means, and to screen appropriate users and appropriate uses.17 The activities and 

procedures described in this report adhere to the requirements of this statute. 

(G) A thorough discussion of the policies, procedures, and guidelines that are in place or that 

are to be developed and applied in the use of such data mining activity in order to: 

(i) protect the privacy and due process rights of individuals, such as redress procedures; and 

A description of the policies, procedures, and guidance in place to ensure the privacy and 

due process rights of individuals that are the subject of FinCEN data mining activities is provided 

in subsection (E) above. 

(ii) ensure that only accurate and complete information is collected, reviewed, gathered, 

analyzed, or used, and guard against any harmful consequences of potential inaccuracies. 

FinCEN, through its data perfection procedures, ensures that information contained in the 

database of BSA reports is accurate and complete. In addition, as discussed in item (E) above, 

FinCEN does not take adverse actions against individuals (outside the context of enforcing the 

requirements of the BSA itself) based on the information contained in BSA reports. In addition, 

because user agencies only use BSA information in conjunction with other evidence, a BSA 

report in itself is not used as the sole basis for adverse actions by user agencies. Accordingly, 

there is an inherent system of “checks and balances” in the use of BSA information that greatly 

reduces the risk of harmful consequences from inaccuracies that may be contained in BSA 

reports. 

As noted earlier in this report, FinCEN’s BSA data contains no constitutionally protected, 

“expectation of privacy” and FinCEN takes no adverse actions against individuals based on the 

BSA data collected. Therefore, FinCEN’s BSA analyst training does not focus on civil liberties. 

However, FinCEN has mandatory training for its data users that includes the privacy component 

of secure handling and safeguarding of the information. FinCEN provides on-line training for all 

external users as a requirement for system access. Biennially, at a minimum, users must 

complete training as a requirement of continued system access. In addition to this online 

training, FinCEN hosts webinars as requested. All FinCEN staff are required to complete 

Privacy Awareness training annually that includes explanation of the staff’s civil liberties 

responsibilities. Accountability for the security and confidentiality of the BSA data and its 

handling are prominently articulated in all course materials. 

                                                            

16 Id. at § 310(b)(2)(E), (G). 17 Id. at § 310(c)(1) and (c)(2). 
23

IRS Data Mining Activities 

(A) A thorough description of the data mining activity, its goals, and, where appropriate, the 

target dates for the deployment of the data mining activity. 

Three divisions of the IRS are engaged in data mining activities covered by the Act: IRS 

Criminal Investigation organization (IRS-CI); the IRS Small Business/Self-Employed Division 

(SB/SE); and the IRS Wage and Investment Division (W&I). In FY 2016, each of these IRS 

divisions used one or more of six data mining applications to search for specific characteristics 

that are indicators of potential criminal activity: 

 Reveal - retired in March 2016; 

 Investigative Data Examination Application (IDEA) - formerly known as Investigative 

Data Analytics; 

 Lead and Case Analytics (LCA); 

 Electronic Fraud Detection System (EFDS); 

 Return Review Program (RRP); and 

 FinCEN Query 

IRS-CI is tasked with protecting IRS revenue streams by detecting fraudulent activity and 

preventing recurrences. In FY 2016, IRS-CI used the Reveal, IDEA, LCA, EFDS, and RRP 

systems to support this work. Data uncovered using these systems may be reflected in 

indictments and criminal prosecutions. 

Retired in March 2016, Reveal was a data query and visualization tool allowing CI 

analysts and agents to query and analyze large and potentially disparate sets of data through a 

single access point. CI streamlined the data analytics program to shut down Reveal and ensure 

data sets and features were consolidated with IDEA and LCA. 

IDEA is a data query tool currently in use at the CI Lead Development Centers (LDC), 

Scheme Development Centers (SDC), and field offices, and it provides CI analysts and special 

agents with the ability to quickly search electronic data through a single access point. By using 

the IDEA application, special agents and investigative analysts can proactively identify patterns 

indicative of illegal activities. This tool enhances investigation selection and supports 

investigative priorities in tax law enforcement, counterterrorism, and other high-priority criminal 

investigations. The IDEA application uses data for both reactive and proactive queries. Reactive 

queries are a result of specific, targeted investigations; proactive queries are the result of pattern 

matching to generate leads. Data available in the IDEA application enable users to detect 

suspicious financial transactions indicative of money laundering, terrorism, and other financial 

crimes. IDEA query results are used exclusively for the purpose of generating leads. Any 

investigative process that results from these leads uses the corresponding data from the 

originating systems. 

LCA is a data query and visualization application that allows CI investigative analysts 

and agents to query and analyze large and disparate sets of data through a single access point. 

This enhances the analyst’s ability to develop a comprehensive picture of suspicious or criminal 

activity. The LCA application uses data for both reactive and proactive queries. Reactive 
24

queries are a result of specific, targeted investigations; proactive queries are the result of pattern 

matching to generate leads. Data available in the LCA application enable users to detect 

suspicious financial transactions indicative of money laundering, terrorism, and other financial 

crimes. The application presents information to the user visually, exposing associations between 

entities in the data that might otherwise remain undiscovered. The software used to create LCA 

allows users from the LDCs, SDCs, and field offices to create visualization diagrams, graphs, 

spreadsheets, reports, timelines, and maps to enhance investigation selection and supports 

investigative priorities to proactively identify and develop leads for refund fraud, identity theft, 

counterterrorism, money laundering, offshore abusive trust schemes, and other financial crime, 

as well as Bank Secrecy Act (BSA) Suspicious Activity Report (SAR) reviews and Financial 

Crimes Task Force activity. 

IRS-CI and W&I use RRP and EFDS to maximize detection of tax return fraud, tax 

noncompliance, and identity theft. EFDS compiles, cross-references, and verifies information 

indicative of potentially fraudulent tax returns. As EFDS receives returns, it loads and assigns a 

score to each tax return. Scores range from 0.0 to 1.0, with a higher score indicating a greater 

potential for fraud. RRP expands on EFDS’ capabilities by providing multiple model scores, rule 

breaks, and linking characteristics. In both RRP and EFDS, IRS-CI does not directly examine 

the scores, but does use returns that W&I determines to be potentially fraudulent as a basis for its 

criminal investigations. 

IRS-CI and SB/SE users access the FinCEN Query system (see FinCEN report) as the 

system of record for BSA data. 

(B) A thorough description of the data mining technology that is being used or will be used, 

including the basis for determining whether a particular pattern or anomaly is indicative of 

terrorist or criminal activity. 

IDEA and LCA do not provide IRS with the ability to determine indicators of terrorist or 

criminal activity. Special agents and investigative analysts can query based on experience. 

Agents and analysts determine indicators of fraudulent activity based on previous successful 

investigations of money laundering, counterterrorism, and BSA violations. 

W&I employees use RRP and EFDS to identity potentially fraudulent, noncompliant, and 

identity theft activity. IRS-CI uses the fraudulent tax returns identified by W&I as a basis for its 

criminal investigations. Paper refund returns come to EFDS from the Generalized Mainline 

Framework (GMF)18 and Questionable Refund Program19. Paper returns come into RRP via 

multiple feeds from GMF. This allows W&I and SDC employees to review those returns for 

                                                            

18 The Generalized Mainline Framework is a service center pipeline processing system that validates and perfects 

data from a variety of input sources. Tax returns, remittances, information returns, and adjustment and update 

transactions in the system are controlled, validated, corrected, and passed on for master file posting. 

19 The Questionable Refund Program (QRP) is a subsystem of EFDS. QRP is a nationwide multifunctional program 

designed to identify fraudulent returns, to stop the payment of fraudulent refunds, and to refer identified fraudulent 

refund schemes to CI field offices.
25

suspicious activities. 

EFDS employs a data mining technology called IBM SPSS Modeler. Using this tool, 

EFDS creates rule sets using a standard built-in algorithm called C5.0. Using examples of 

current and prior year verified fraud and non-fraud data, the machine-learning model discerns 

patterns or rules indicative of fraud. The output of the model is a score where a higher score (in 

the range of 0.0 to 1.0) represents a higher risk or a higher likelihood of a return being 

fraudulent. 

If a return meets designated score tolerances and other criteria, W&I and IRS-CI 

personnel examine the return for fraudulent activity. Once a return is verified to be false via 

screening, Taxpayer Protection Program authentication and/or the wage verification process, the 

fraudulent returns are added via EFDS systemically or by W&I and CI-IRS users to the Scheme 

Tracking and Retrieval System (STARS) component. IRS-CI investigative analysts review the 

returns in Discoverer and STARS to find possible schemes, or fraudulent patterns, which may 

result in a referral to a CI field office for investigation. 

RRP employs multiple technologies for data mining activities. Each of these 

technologies uses current and prior year examples of identity theft (IDT), non-IDT tax fraud, and 

non-fraud to develop supervised models, unsupervised models, rules, and network analytics: 

 SAS – RRP uses SAS as the workbench for developing and evaluating supervised and 

unsupervised models as well as for data exploration activities. RRP uses multiple SAS 

machine learning algorithms (e.g., decision trees, neural networks, logistic regression) to 

uncover patterns in the data associated with fraud. RRP also includes components of 

SAS’ High Performing Analytics (e.g., SAS Grid, SAS in-database analytics) to develop 

and deploy models with greater complexity than what could be built on a traditional 

infrastructure. Greater complexity allows RRP models to display greater accuracy and 

robustness. Supervised models produce a score from 0.000 to 1.000 where a higher score 

represents a higher likelihood of a return being fraud. 

 Greenplum Data Computing Appliance (DCA) – All RRP models are deployed and run 

directly in the database. Deploying models directly to the database removes the network 

latency required to move data to a separate application tier server containing the models. 

Moreover, the Greenplum DCA provides massively parallel processing capabilities across 

multiple segment servers. In addition to models developed using SAS, RRP also develops 

models in the form of custom user-defined functions in the Greenplum DCA. 

 RRP’s network analytics tool – Linked Return Analysis (LRA) – uses multiple custom 

built Greenplum functions to link returns that display common, suspicious characteristics. 

 RRP builds “identity theft filters” using Greenplum functions. These functions combine 

the outputs of RRP models, rules and LRA to flag suspicious cases of identity theft 

treatment. 

 FICO Blaze Advisor (FICO BA) – RRP builds and maintains business rules using FICO 

Blaze Advisor. FICO BA provides transparency into the logic that drives business 

decisions. FICO BA houses the logic that drives RRP’s Systemic Verification process – 

the rule logic that matches taxpayer submitted Income Documents (IDOCs) to the 

document submitted by withholding party(ies) (e.g., employer submitted W-2s containing 

income and withholding information). 
26

(C) A thorough description of the data sources that are being or will be used. 

The IRS-CI applications IDEA and LCA leverage the following data sources: 

 Taxpayer: The source is the electronically filed return, as transmitted through the 

Modernized E-File Program (MeF), or a paper filed tax return. 

 Employers/Payers: Information from employers/payers captured on various forms as 

stored in the Information Returns Master File (IRMF). 

 Other Treasury sources: BSA data provided by FinCEN, Specially Designated 

Nationals’ data provided by the Office of Foreign Assets Control. 

 Other IRS sources: Tax Exempt Organizations data, Voluntary Disclosures, Criminal 

Investigations data. 

The EFDS and RRP application leverages the following data sources. 

 Taxpayer: The source is the electronically filed return (as transmitted through the MeF) 

or a paper filed tax return. EFDS and RRP also load taxpayer data contained on the IRS 

Master File. 

 Employers/Payers: Information from employers/payers captured on Form W-2 and/or 

Form 1099 as stored in the IRMF. 

 Other federal agencies: Federal Bureau of Prisons for prisoner information; Social 

Security Administration for National Accounts Profile data for dates of births and deaths. 

 State and local agencies: Prison systems in all states and the District of Columbia deliver 

prisoner-listing information annually to IRS-W&I in electronic format. 

(D) An assessment of the efficacy or likely efficacy of the data mining activity in providing 

accurate information consistent with and valuable to the stated goals and plans for the use or 

development of the data mining activity. 

The data uncovered during the query searches are only leads and require additional 

investigative steps for quality verification. There is no empirical data on the efficacy of searches 

by the IDEA and LCA applications. 

The efficacy of the data mining on EFDS can be measured in terms of fraud detection. A 

key overall measure of efficacy is “hit: scan,” which represents the number of returns selected for 

verification that, upon inspection by IRS employees, are found to be fraudulent. The overall “hit: 

scan” for the EFDS system is 1:1.75 for FY 2016. This means that the data mining program 

accurately predicts fraudulent returns in 10 of 17 cases. 

The efficacy of RRP can be measured in terms of identity theft detection. Two key 

metrics are used to assess RRP’s efficacy: lead generation and True Positive Rate. In 2016, RRP 

generated over 693,000 identity theft leads at a true positive rate of 62 percent. This means that 

over 6 out of every 10 returns flagged as IDT by RRP never receive a legitimate taxpayer 

identity authentication via the IRS’s web, phone, or in-person authentication processes. In 

addition to identity theft detections, RRP deployed a new set of models in 2016 to detect fraud 
27

that does not involve identity theft. During the year, RRP generated over 103,000 non-identity 

theft fraud leads (above all identity theft leads) at a true positive rate of 49 percent. 

The efficacy of the FinCEN Query system is discussed in Section (D) of that report.

(E) An assessment of the impact or likely impact of the implementation of the data mining 

activity on the privacy and civil liberties of individuals, including a thorough description of the 

actions that are being taken or will be taken with regard to the property, privacy, or other 

rights or privileges of any individual or individuals as a result of the implementation of the 

data mining activity. 

Once evidence of fraud is discovered, laws and administrative procedures, policies, and 

controls govern the ensuing actions. IDEA and LCA applications use PII for pattern matching, 

but the results of a query are used for further investigation. IRS-CI follows the IRS security and 

privacy IRM standards and regulations for the use and protection of PII. 

The impact or likely impact of the EFDS and RRP data mining activities on privacy and 

civil liberties of individuals is governed by 26 U.S.C. § 6103, which provides general rules of 

maintaining confidentiality and permissible disclosures. Under this statute, all taxpayer data are 

private and confidential and protected from disclosure except under specific conditions. 

Additional laws provide for civil and criminal penalties for any unauthorized disclosure of 

taxpayer data. The penalties include (1) felony for the willful unauthorized disclosure of tax 

information, (2) misdemeanor for the unauthorized inspection of tax information, and (3) civil 

cause of action for the taxpayer whose information has been inspected or disclosed in a manner 

not authorized by Section 6103. The CI special agents receive periodic training on maximum 

sentencing and penalties for each criminal violation. Access to the system requires a background 

check. IRS has a system, Online 5081, governing program access authorization. 

Further, EFDS and RRP data mining activities, including its machine learning and 

scoring process, do not directly use any PII in determining whether a return is likely to be 

fraudulent. Scoring occurs on the characteristics of the return in question, not on the PII. When 

performing investigative techniques, PII associated with the return is pulled to assist in validating 

the return was filed using the taxpayer account in question and to determine venue of the 

investigation. 

The tax returns that IRS-CI reviews are the subjects of criminal investigations and actions 

based on tax laws, policies, and criminal procedures. Other tax returns are subjected to IRS civil 

treatments and examination procedures that provide for due process and redress procedures 

through taxpayer notification, appeals, and tax court options. 

(F) A list and analysis of the laws and regulations that govern the information being or to be 

collected, reviewed, gathered, analyzed, or used in conjunction with the data mining activity, to 

the extent applicable in the context of the data mining activity. 

The use of all tax data is governed by 26 U.S.C. § 6103. Subsection (a) sets out the general rule 

of confidentiality. Subsection (b) sets forth definitions of terms commonly used throughout 

Section 6103. Subsections (c) through (o) of Section 6103 contain exceptions to the general rule 
28

of confidentiality. These subsections permit disclosures as described generally below: 

 Section 6103(c) – Disclosures to taxpayer’s designees (consent); 

 Section 6103(d) – Disclosures to state tax officials and certain state and local law 

enforcement agencies; 

 Section 6103(e) – Disclosures to the taxpayer and persons having a material interest; 

 Section 6103(f) – Disclosures to certain committees of Congress; 

 Section 6103(g) – Disclosures to the President and certain other persons; 

 Section 6103(h) – Disclosures to Federal employees and the courts for tax administration 

purposes; 

 Section 6103(i) – Disclosures to Federal employees for non-tax criminal law enforcement 

purposes and to combat terrorism, as well as the Government Accountability Office; 

 Section 6103(j) – Disclosures for statistical purposes; 

 Section 6103(k) – Disclosures for certain miscellaneous tax administration purposes; 

 Section 6103(l) – Disclosures for purposes other than tax administration; 

 Section 6103(m) – Disclosures of taxpayer identity information (generally for Federal 

debt collection purposes); 

 Section 6103(n) – Disclosures to contractors for tax administration purposes; and 

 Section 6103(o) – Disclosures with respect to certain taxes. 

In addition to disclosures permitted under the provisions of Section 6103, other 

provisions of the Internal Revenue Code also authorize disclosure of tax information. For 

example, Section 6104 authorizes disclosure of certain tax information regarding tax-exempt 

organizations, trusts claiming charitable deductions, and qualified pension plans. Section 6110 

authorizes disclosure of certain written determinations and their background files. 

(G) A thorough discussion of the policies, procedures, and guidelines that are in place or that 

are to be developed and applied in the use of such data mining activity in order to: 

(i) protect the privacy and due process rights of individuals, such as redress procedures; and

All tax information is protected as required in 26 U.S.C. § 6103 (see E and F above). All 

employees who interact with tax return and other protected information are required to undergo 

yearly refresher training that details their responsibilities with respect to information protection 

and disclosure. In addition to covering 26 U.S.C. § 6103 disclosure provisions, this training 

module also includes information on the Privacy Act, E-Government Act, Freedom of 

Information Act, and policies related to protecting PII and other sensitive information. The use 

of BSA information is strictly controlled under the statute that directs its collection. 

The data uncovered during query in IDEA and LCA applications are used as a lead and 

requires additional investigative steps to verify the quality of the information, as discussed 

above. IRS maintains an audit trail on all users’ access to case data. In addition, a full system 

log is maintained for any system level activities, including new data loads to the IDEA and LCA 

application. 

Neither EFDS nor RRP determines whether a return is fraudulent or whether a person is 

going to be subject to criminal prosecution. Once fraud is suspected, laws and administrative 
29

procedures, policies, and controls govern criminal investigations or any other ensuing actions. 

Due process is provided during any ensuing criminal investigation or civil action. 

(ii) ensure that only accurate and complete information is collected, reviewed, gathered, 

analyzed, or used, and guard against any harmful consequences of potential inaccuracies. 

An individual/entity self-reports tax data when submitting the information to the 

government. FinCEN’s data are gathered from information compiled by the reporter based on 

information provided by their customer or based on the reporter’s personal experience. 

Investigators scrutinize the Suspicious Activity Reports filed by the subject companies and 

request grand jury subpoenas for the underlying documentation. The supporting records are 

examined and individuals of interest are identified. 

The IDEA and LCA applications are not the authoritative owners of data. However, the 

data are used for investigative analysis purposes under the IRS Internal Revenue Manual (IRM) 

standards and guidelines. The data uncovered during query searches are only used as a lead and 

require additional investigative steps to verify the quality of the information. Therefore, IRS-CI 

uses these data for generating leads and the special agents verify it through further investigative 

work. 

The tax return information and other information stored in EFDS and RRP used for data 

mining are based on outside data sources. The only data generated directly in EFDS are the 

processing steps and the results of examinations of possibly fraudulent returns. The only data 

generated in RRP are for system monitoring and diagnostics. Through a series of test case 

procedures executed through Application Qualification Testing (AQT), Systems Acceptability 

Testing (SAT), and Final Integration Test (FIT), the IRS verifies that the data loaded into EFDS 

and RRP match the data from the input source and that the system accurately displays the data in 

the EFDS and RRP end user applications. AQT, SAT, and FIT perform verification with each 

release of the system. IRS applications are required to have internal auditing capabilities. The 

internal audits track user access and queries performed with checks against misuse. 

TTB Data Mining Activities 

(A) A thorough description of the data mining activity, its goals, and, where appropriate, the 

target dates for the deployment of the data mining activity. 

TTB’s analytics program performs three types of activities that, together, qualify as data 

mining as defined by the Federal Agency Data Mining Reporting Act of 2007: 

 Queries of commercial transactions recorded by tax and trade databases maintained by 

 TTB and other federal agencies; 

 Searches of public records and law enforcement databases for indications of illicit 

dealings; and, 

 Link analysis of connections between businesses and individuals.

TTB conducts these activities primarily for the purpose of discovering or locating 

patterns or anomalies indicative of activity by individuals or businesses that violate federal 

statutes and regulations administered by TTB. Many of the statutory provisions have criminal 
30

sanctions for their violation. The data used in these activities are, for the most part, gathered 

with queries of registered individuals or businesses. However, subsequent analysis of the data is 

primarily pattern-based, seeking anomalies in compiled records. The data mining activities also 

include some queries and searches that are solely pattern-based, e.g., queries of all tobacco 

product imports over a given time period. 

The goals of TTB’s data mining activities are to automate certain routine oversight 

processes and improve detection of common violations. These activities support predictive 

models and business intelligence that identify compliance risks and potential fraudulent or 

criminal activity that may be subject to further field review and action. TTB has predictive 

models in place that score the risk of tax diversion in the tobacco industry and evaluate 

businesses seeking a TTB permit. TTB compiles business intelligence that highlights patterns in 

tax and trade data. These models and business intelligence are in regular use today with 

improvements and expansions planned for fiscal year 2017. 

(B) A thorough description of the data mining technology that is being used or will be used, 

including the basis for determining whether a particular pattern or anomaly is indicative of 

terrorist or criminal activity. 

TTB uses commercially available data mining technologies to access and analyze 

information. The experience of intelligence analysts and investigators provides the basis for 

determining whether a particular pattern or anomaly is indicative of violations. The ability to 

identify patterns and anomalies is supplemented with statistical analysis and machine learning 

techniques. 

Most data mining is conducted with a combination of SAS statistical analysis software 

and Oracle relational database systems. Data are retrieved with SAS data step programming 

and/or Structured Query Language (SQL) queries. Data fields are transformed with procedures 

that aggregate, correlate, cluster, and otherwise simplify available variables. 

 

Once data are collected and transformed, predictive models use the data to estimate the 

expected violation risk of a particular individual, business, or incident. The estimates today are 

based primarily on business rules and templates defined by experienced analysts. These 

estimates are then implemented in the SAS programming language. Patterns identified through 

these methods are vetted with experienced analysts and evaluated against randomized test cases. 

(C) A thorough description of the data sources that are being or will be used. 

TTB uses data from its own databases, the databases of other federal agencies, and 

commercial data providers. The data sources include: 

Internal Data: 

 Integrated Revenue Information System (IRIS) – tax data submitted by TTB 

industry members; 

 Permits Online (PONL) –application data from businesses requesting a TTB permit; 
31

 AutoAudit – data from TTB’s audits and investigations; 

 Formulas Online (FONL) – data from businesses submitting formula approval requests; 

 COLAS Online – data from business submitting label approval requests; 

External Data: 

 Automated Commercial Environment (ACE) / Automated Commercial System (ACS) / 

Automated Export System (AES) – data regarding imports and exports of products 

regulated by TTB; 

 Census Export Data – data regarding exports of products regulated by TTB; 

 Financial Crimes Enforcement Network Query (FinCEN Query) – data submitted in 

compliance with the Bank Secrecy Act transcripts such as Suspicious Activity Reports 

(SARs), Currency Transaction Reports (CTRs), etc.; and, 

 LexisNexis Accurint – public records data of court proceedings (including some criminal 

cases), property holdings, licenses, and registrations. This is a for fee service. 

These databases are either in use today or being evaluated for inclusion in predictive 

models. Further integration of the sources is ongoing, as is identification of potential new data 

sources. 

(D) An assessment of the efficacy or likely efficacy of the data mining activity in providing 

accurate information consistent with and valuable to the stated goals and plans for the use or 

development of the data mining activity. 

TTB’s data mining activity is valuable for automating certain routine oversight processes, 

and improving detection of compliance violations. Initial evaluations indicate that data mining 

enables more regular oversight and produces indicators for further field review, including 

investigation and audit. This evaluation is continuing and generating new improvements as the 

data mining activity matures. 

The data mining activity, models, and business intelligence supported by the activity have 

been effective at helping to automate oversight processes. Predictive models automatically 

screen approximately 4,000 original permit applications, 2,000 active tobacco businesses, and 

2,100 active distilled spirits manufacturers. The models verify information and monitor patterns 

in operations, tax payments, and international trade activity. The models also automatically 

monitor financial and trade databases for indications of activity by unregistered businesses. 

Automating basic screens enables TTB to provide oversight to a wider section of its regulated 

industries. 

The ability of predictive models to detect compliance violations depends greatly on the 

accuracy of the source data available for the models. Data quality and mining techniques 

continue to improve with increased use and scrutiny over data. Predictive models that rely on 

data mining activity are showing promise in detecting violations, though the evaluation is still 

ongoing. The Tobacco Importer Risk Model and Small Wine Producers Model has 

demonstrated accuracy of approximately 0.90 (i.e., 9 out of 10 cases recommended by these 

models result in the detection of previously undisclosed tax liability) and provided a positive 
32

return on investment. The accuracy rate (based on precision and recall) for the Tobacco Risk 

Model is approximately 0.87; the majority of leads have found compliance issues, and possible 

undisclosed tax liability. Evaluation of these and other models will continue as part of TTB’s 

ongoing effort to improve model accuracy. 

(E) An assessment of the impact or likely impact of the implementation of the data mining 

activity on the privacy and civil liberties of individuals, including a thorough description of the 

actions that are being taken or will be taken with regard to the property, privacy, or other 

rights or privileges of any individual or individuals as a result of the implementation of the 

data mining activity. 

TTB’s data mining activity has little impact on the privacy and civil liberties of 

individuals. Insights gained from the activity primarily result in actions against property, or the 

privilege to operate in regulated industries, after thorough review by experienced specialists with 

oversight authorities mandated by federal laws and regulations. The data sources mined are also 

limited to include only tax records, regulatory records, commercial records, and law enforcement 

records authorized for use in oversight and enforcement. 

Any data concerning individuals or businesses are vigorously protected against 

unauthorized use and disclosure. Policies and procedures prohibit the search of any database for 

reasons other than providing authorized oversight or enforcement. In cases when patterns in data 

are thought to be indicative of compliance issues, the data and circumstances are carefully 

reviewed by experienced staff before any adverse action is taken. TTB also continues to protect 

data against any unauthorized disclosure through all investigation and enforcement actions. 

Data gathered in data mining activities is considered private and confidential and 26 

U.S.C. § 6103 protects it from disclosure. TTB handles these data consistent with that statute. 

Privacy protections are further assured by additional laws that provide for civil and criminal 

penalties for any unauthorized disclosure of taxpayer data. There are criminal penalties 

including: (1) felony for the willful unauthorized disclosure of tax information; (2) misdemeanor 

for the unauthorized inspection of tax information; and (3) civil cause of action for the taxpayer 

whose information has been inspected or disclosed in a manner not authorized by Section 6103. 

(F) A list and analysis of the laws and regulations that govern the information being or to be 

collected, reviewed, gathered, analyzed, or used in conjunction with the data mining activity, to 

the extent applicable in the context of the data mining activity. 

TTB administers the provisions of the Internal Revenue Code (IRC) relating to distilled 

spirits, wine, and beer (26 U.S.C. Chapter 51), tobacco (26 U.S.C. Chapter 52), firearms and 

ammunition excise taxes (26 U.S.C. sections 4181, 4182, and related portions of chapter 32), 

and the general rules of tax procedure with respect to these commodities (including related 

criminal provisions at 26 U.S.C. Chapters 68 and 75). In addition, TTB administers the Federal 

Alcohol Administration Act (27 U.S.C. chapter 8, subchapter I), which covers basic permits, 

unfair trade practices, and labeling and advertising of alcohol beverages; the Alcoholic Beverage 

Labeling Act of 1988 (27 U.S.C. chapter 8, subchapter II), which requires a specific 

“Government Warning” statement on alcohol beverage labels; and the Webb-Kenyon Act (27 

U.S.C. sections 122-122b), which prohibits the shipment of liquor into a state in violation of state 
33

law. 

The IRC establishes qualification criteria to engage in the businesses relating to 

manufacturing and importing or exporting tobacco products, and manufacturing or importing 

processed tobacco, and require that persons obtain permits to engage in these activities. See 26 

U.S.C. § 5713. A permit qualification requirement also applies to the production of distilled 

spirits and wine, as well as to the wholesaling and importation of all beverage alcohol products. 

See 26 U.S.C. §§ 5171(c) and (d), 5271; see also 27 U.S.C. §§ 201 et seq. 

Through an agreement with FinCEN, dated May 3, 2005, TTB is granted direct electronic 

access to data collected pursuant to provisions of the Bank Secrecy Act, 31 U.S.C. § 5311 et seq. 

The direct access is granted for tax or regulatory purposes relevant to the mission of TTB. 

The authority to collect excise taxes on imported alcohol and tobacco products was 

originally retained by the Secretary of the Treasury through the Homeland Security Act of 2002. 

See 6 U.S.C. §§ 212 and 215. Through Treasury Order 100–16, the Secretary of the Treasury 

delegates authority over “Customs revenue functions” to the Secretary of the Department of 

Homeland Security. The Homeland Security Act of 2002 defines these functions as “assessing 

and collecting customs duties (including antidumping and countervailing duties and duties 

imposed under safeguard provisions), excise taxes, fees, and penalties due on imported 

merchandise, including classifying and valuing merchandise for purposes of such assessment.” 6 

U.S.C. § 215(a)(1). 

TTB is authorized pursuant to the Homeland Security Act of 2002, Pub. L. 107-296; 

Executive Order 13439, July 18, 2007; the Internal Revenue Code of 1986 (IRC); and the 

Federal Alcohol Administration Act, 27 U.S.C. chapter 8 (FAA Act) to access data within 

Customs and Border Protection (CBP) data systems necessary to fulfill its statutory mission. 

TTB is working in conjunction with CBP to fulfill its statutory mission as it relates to imported 

products subject to various taxes and to ensure taxpayers understand their tax responsibilities 

related to these products. Cooperative efforts across federal agency lines will accommodate the 

collection of data as it relates to imported commodities subject to federal taxes, including but not 

limited to retail, excise, manufacturers, and environmental taxes. 

When the data analyzed by the models consists of taxpayer information, 26 U.S.C. § 6103 

governs the use of all tax related data. Subsection (a) sets out the general rule of confidentiality. 

Subsection (b) sets forth definitions of terms commonly used throughout Section 6103. 

Subsections (c) through (o) of Section 6103 contain exceptions to the general rule of 

confidentiality. The use of confidential commercial, financial, or trade secrets information is 

governed by the Trade Secrets Act, 18 U.S.C. § 1905, which prohibits the unlawful disclosure of 

this information by any federal official, employee, or contractor. 
34

(G) A thorough discussion of the policies, procedures, and guidelines that are in place or that 

are to be developed and applied in the use of such data mining activity in order to: 

(i) protect the privacy and due process rights of individuals, such as redress 

procedures; and

All of TTB’s information collections are subject to the OMB review process and any 

forms that request personal information include a Privacy Act Statement. In addition, TTB’s 

privacy policy is posted on TTB’s website and is referenced on TTB’s Online Applications. 

TTB’s systems of record notice can be found in the Federal Register at 80 F.R. 4637 (January 28, 

2015). 

TTB data mining activities do not determine whether a person or entity will be subject to 

administrative enforcement action or criminal prosecution. Any audit or investigation that is 

initiated based, in part, upon data from the activities are governed by the laws, administrative 

procedures, policies, and controls that govern criminal investigations or any other ensuing 

actions. 

Information generated and accessed by the data mining activities is protected by internal 

controls that limit access to persons whose official duties require inspection of such information 

for tax administration purposes. The information is further protected by 26 U.S.C. § 6103, 

governing the confidentiality of returns and return information, and the Trade Secrets Act, 18 

U.S.C. § 1905, which protects confidential commercial, financial, or trade secrets information 

collected by the federal government. 

TTB notifies system operators of the requirements and legal consequences of 

accessing predictive models in production. The message states: 

26 U.S.C. 6103 Data Warning. Information contained in this report is tax return 

information protected from disclosure by 26 U.S.C. 6103. By accessing this 

report, you hereby certify that your official duties require you to inspect such 

information for tax administration purposes. 

Users of predictive models in production receive training in the proper handling of 

information. Users receive system demonstrations of the model and have access to a user guide. 

The same process will be followed for future models when successful testing and evaluation has 

been completed. Field Operations staff receive 26 U.S.C. § 6103 and disclosure training. In 

addition, all TTB employees complete the annual Privacy Awareness and Cyber Security 

Awareness training. Finally, system sponsors and IT staff supporting development, maintenance, 

and operations of IT systems are required to take additional specialized security training each 

year. 

(ii) ensure that only accurate and complete information is collected, reviewed, gathered, 

analyzed, or used, and guard against any harmful consequences of potential inaccuracies. 

The data mining activities rely on information collected through systems that have their 

own accuracy related checks and balances. TTB does not rely solely on information gathered 
35

through predictive models to take any adverse action against any individual or entity. Rather, the 

models are the first step in gathering data and this information is verified through subsequent 

research and audits of companies and importers before any adverse action is taken. 

TTB documents and manages all data sets associated with its systems using the 

TTB Systems Development Life Cycle (SDLC). Checks and balances are inherent to the data 

correction process ensuring different teams handle different steps of the effort and include 

oversight by the Office of the Chief Information Officer Quality Assurance (OCIO QA) Team. 

When the system owner identifies inconsistencies with data, TTB’s OCIO QA Team may initiate 

a data correction. All changes are documented via the Request for Change process managed by 

the Configuration Management Team and work orders track the correction through its lifecycle 

(from request to development and through implementation), which includes confirmation of 

successful completion by the system owner. The process includes specific identification of the 

data to be corrected along with rationale for the change. SDLC artifacts (e.g., database scripts) 

supporting data corrections conform to Data Management (DM) standards. The Software 

Maintenance Team verifies analysis, development, and testing through a quality review process 

conducted by the DM Team to ensure the data correction is thoroughly documented. Once the 

DM Team has approved the data correction, the Operations Team executes the correction and the 

system owner verifies the correction. 

The Memorandum of Understanding with CBP contains language that both parties will 

notify one another if either agency discovers data issues. Also, the ACS and ACE data import 

processes in support of the Tobacco Importer Risk Model were documented and tested using 

TTB’s SDLC. For all available governmental data sources, users must sign a non-disclosure 

agreement before receiving access.

Conclusion 

The Department of the Treasury is pleased to provide to Congress its Annual Privacy and 

Data Mining Reports for Fiscal Year 2016. OPTR has reviewed the activities and programs 

described in this combined report and will continue to work closely with all Treasury bureaus 

and offices to protect individual privacy and civil liberties in all Treasury activities.