Attachment_A_-_Call__0001_Analytical_Enhancement.pdf

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VICTOR CHAVEZ

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TFSAFSA17CI0004

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BPA Call #0001. See attached Performance Work Statement (PWS).

COR: DeAyrus Smith, 215-912-6184.

Period of Performance: 11/21/2016 to 11/20/2017

0001 Call #0001 for Analytical Enhancement. See attached Performance Work Statement (PWS).

COR POC: DeAyrus Smith, 205-912-6184.

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13 2 of

Solicitation No. TFSAFSA17CI0004 Attachment A - Call #0001

BPA Call #0001 for DMS Analytical Enhancement and Support

Introduction

The Bureau of the Fiscal Service (Fiscal Service) is a bureau of the United States Department of the Treasury. The mission of the Fiscal Service is to promote the financial integrity and operational efficiency of the United States Government through exceptional accounting, financing, collections, payments, and shared services. The Fiscal Service plays a key role in supporting the Department of the Treasury’s strategic goal of managing the United States Government’s finances effectively by operating as the financial manager and principal fiscal agent for the Federal Government. This role includes managing the national finances by collecting money due to the United States (including delinquent debt), making its payments, preventing improper payments, performing central accounting functions, and issuing debt to finance the operations of the United States government.

Debt Management Services (DMS) is an organization within the Fiscal Service. DMS works with federal government agencies to provide a comprehensive debt management program, and also provides debt collection services to the states. DMS supports the following Fiscal Services’ Strategic Goals:

1. Serve as a catalyst for effective Government through initiative and innovation.

2. Cultivate an engaged, highly effective and skilled workforce.

3. Deliver exceptional programs and services.

4. Promote data transparency and usefulness as a public good.

Statement of Need

DMS is seeking assistance with the following objectives and needs:

1. Improve debt recover rates through the development of a more sophisticated suite of predictive models that will allow DMS to identify debts to prioritize for collection, as well as allocate the appropriate collection tools to those debts.

2. Improve insight into business processes through the evaluation of both the impact and costs of these activities to DMS.

3. Provide analytical modeling and analysis to empower policy and operational decisions made by DMS leaders.

4. Train DMS staff in analytical capabilities.

5. Ad-hoc support and analyses.

Scope Skill Requirements: Subject Matter Expert (SME), Project Director, Project Manager, Analyst

1.0 Improve Debt Recovery Rates

Currently, DMS has not allocated sufficient resources to fully take advantage of the rich datasets that it generates from its daily operations.

Developing a more sophisticated suite of predictive analytics models will allow DMS to identify debts to prioritize for collection, as well as apply the appropriate collection tools to debts. This will allow DMS to increase collections, allowing it to better serve its creditor agencies, and increase the fees collected by DMS while maintaining similar costs.

Activities that promote the goals of this initiative include:

1.1 Develop Optimal Collection Strategies

This activity includes learning and documenting the DMS operations and collection tool options available, as well as a review of the debts serviced and current collection practices. It would also include implementing predictive analytics, machine learning, and econometric techniques to develop groupings of similar debts, debts prioritization, and related tools, and identify optimal collection methods for different types of debt. This would allow DMS to use its historical collection information to determine what kind of debts and which debts are most collectible, apply or develop new tools that increases collectability and maximize payments received, as well as develop strategies to maximize the amount of debt collected.

1.2 Translate Analytical Findings into Implementable Recommendations

This activity requires a deep knowledge of DMS debt collection practices and business lines, which is critical to translating the optimal collection strategies into specific steps that can be understood and implemented by DMS collection agents or the current (or updated) DMS debt processing system. It also provides an opportunity to develop an analytical plan to evaluate the success of the program given the implementation parameters, and ensure proper implementation of the program by the UCC and other stakeholders.

1.3 Optimize Collection Tool Deployment

This activity includes an assessment of the current DMS policies for implementing certain collection tools. It applies econometric models to estimate risk predictors and identify optimal tool specifications to maximize collections for individual debts. This enables Cross- Servicing and the UCC to identify debtors that may respond more favorably to alternative types of collections not normally used by DMS, and will allow DMS to maximize the collections from individual debtors once initial collections are initiated.

1.4 Assessing Cross-Impact Effects of Debt Collection Programs This activity includes assessing the TOP and Cross-Servicing DMS collection programs and the cross-impact effects between the two programs. It uses econometric models to estimate risks and to determine if cross program collections are complimentary or not. This will allow both TOP and Cross-Servicing to better understand how different debts respond to the different collection programs, and provide outreach to its creditor agencies to encourage debt referral patterns that lead to maximum recoveries for both DMS and the relevant creditor agency.

2.0 Improve Insight into Business Processes

DMS participates in a large number of debt collection activities each day. Some of these are standard debt collection items that happen on a daily basis, while others are special projects or pilot programs that are being tested for integration into standard activities. Evaluating both the impact and the costs of these activities will allow DMS to better understand the net effects of all the activities that it currently conducts, and will provide data-driven evidence for continuing or reducing the activities performed. They will allow DMS to more efficiently allocate its resources to accomplish its mission.

2.1 Implementing Performance Reporting through Business Intelligence Tools This activity includes automating and customizing current performance reporting through visualization tools, as well as developing uniform definitions for common metrics within DMS. Establishing these performance metrics will allow DMS to better understand the impacts of its everyday activities, as well as understand the relationships or dependencies of the performance of different business lines.

This will allow for quicker identification and more appropriate reactive action to changes in DMS performance.

2.2 Evaluation of DMS Pilot Projects

This activity includes the assessment of the research questions and objectives of each project, as well as identifying and analyzing the data produced through each DMS pilot to use in building relevant metrics for success. Developing formal evaluations for these pilot programs will help DMS ensure that they are appropriately analyzing the data generated by each pilot, as well as identifying lessons learned during pilot implementation that can be applied in future pilots. This will help DMS by providing accurate information that can support findings by other DMS business lines, such as Business Planning, to help determine if a pilot recommendation should be formalized into standard debt collection procedures, as well as prevent pilots from providing data that cannot be usefully analyzed.

2.3 Supporting Survey Development

This activity includes developing appropriate methodologies and questionnaires to reach a population of individuals to answer specific research questions. Sending out surveys can help DMS better understand its customer base on a variety of metrics, including satisfaction with the collection process, ability to pay, or other items. This will allow DMS to implement collection policies that address debt unhappiness or identify clusters of debtors that may react differently to certain policies.

2.4 Projecting Future Collections and Workload

This activity includes developing projection models for different collection programs or workloads for the UCC. Implementing these models would allow DMS to understand how macroeconomic or current collection patterns will impact future collections. This would allow DMS to better strategize for changes in collections, potentially changing UCC work allocations or Cross Servicing or TOP collection policy to maximize the impact of DMS, as well as help estimate the impact of DMS activities versus impacts due to changes outside of DMS’ control.

3.0 Fully Empower DMS Leaders and Operations with Data through Analytics

DMS generates a large amount of data each day as it carries out debt collection. Information on debtor financial information, phone call results, financial transactions, and tool activations are just a small portion of the information collected. However, not all of the data are currently able to be linked to each other. Improving this integration will allow DMS to better understand the relationships between all parts of the debt collection process, as well as be in compliance with legal reporting requirements. This will allow DMS to properly allocate its resources to high impact projects that will significantly increase collections, customer satisfaction, or other metrics of import to DMS.

3.1 Integrate and Reconcile Databases

This activity involves improving linkages across DMS databases, as well as acquires and integrates information from external data that could enhance debt collection policies. Accomplishing this allows DMS to link debt collection outcomes to previously unanalyzed information, such as the number of calls a debtor makes to the UCC, or the credit score of the debtor before debt default. It would also help them understand the differences in databases and why reports for similar metrics are different. This would help DMS identify new priorities for debt collector training, or provide more accurate analysis of issues that could be addressed through multiple databases (such as creditor agency referral compliance).

3.2 Develop Approximate Matching Techniques

This activity involves the assessment of identification information with DMS databases, and developing matching algorithms to identify unique entities across different databases. Developing these algorithms would allow DMS to better detect patterns for debtors within their system, which requires manual entry of information. It also may allow for linkage of data that do not share identifiers. This would help DMS maximize TOP collections, while minimizing individuals being offset for debts that they do not have.

3.3 Support DMS Compliance with the DATA Act

With the implementation of the DATA Act, DMS needs to be prepared to be compliant with the act, as detailed in the previous section.

This will require multiple activities, such as:

• Prepare to receive new data.

• Process and standardize data for online publication.

• Identify data quality or variable definition issues.

• Define an automated reporting process to produce the required reports for compliance.

To prepare, DMS must strengthen their analytics capacity to analyze the new data, augmenting staff and experts with relevant expertise.

Additionally, analysts will need to analyze the new debt cycle given the new reporting requirements and optimize their debt collection methods to respond to characteristics of new debts.

In addition to new analytical considerations, resources must be allocated to ensure that the new data is provided with the proper security procedures dictated by the amount of PII that is present within the information. System limitations may require upgrades or repurposing of current technologies to comply with the requirements. These issues often hinder any organization that is presented with large volumes of new data. By detecting these limitations early, Treasury is well-positioned to ensure that their systems and personnel are capable of efficiently handling the new requirements of the DATA Act.

4.0 Train DMS Staff in Analytical Capabilities

In conjunction with the implementation of these tasks, it is necessary that DMS provide training for all relevant staff to ensure that employees understand the processes, are knowledgeable of the concepts involved in each task, and be able to explain the results in an intuitive way of leadership and other stakeholders. This is aligned with the Fiscal Service’s strategic goal of cultivating an engaged, highly effective, and skilled workforce, which is a key to enhancing DMS capabilities that optimize the use of data and information to reach DMS objectives.

Trainings will provide overviews of the topic, provide interactive case studies where trainees can execute small examples of at each important step of the process, and list situations where the analytical methodologies could be implemented.

Potential topics for these trainings include:

• Program evaluation,

• Predictive analytics,

• Experimental design,

• Projection models,

• Application of analytics to DMS Data,

• Sampling, or

• Using SAS, State, OxMetrics, or other analytical software.

5.0 Ad-hoc Support and Analyses

Provide ad-hoc analysis and subject matter expertise in support of DMS Analytics’ current and ongoing activities and priorities. As needed, the Contractor shall provide technical guidance, data analysis, training, and communications support to assist ongoing and new DMS initiatives.

Skill Requirements

Project Director, Project Supervisor, Expert Advisor, Economics Specialist, Database Administrator, Analysts

To complete the above set of complex and unique activities in an efficient and accurate way, DMS must have access to contractors that bring recent and relevant experience in:

• Debt Collection Analytics,

• Predictive Analytics Tools,

• Econometrics and Advanced Data Analysis,

• Experimental Design and Implementation,

• Forecasting,

• Program Evaluation, and

• Project Management.

Contractors must also bring a working, if not strong, understanding of DMS’ complex debt collection flow, databases and future data architecture changes, policies, tools, and important business lines (whether in Cross-Servicing or TOP). These tasks also require staff that specializes both in predictive analytics and statistical methodologies specifically applied to challenges encountered in the debt collection industry. Furthermore, it would be necessary for DMS Analytics to have access to experienced trainers in the area of statistical analysis and predictive analytics, including ready training materials, and ideally, a training facility local to DMS. Contractors who do not bring these competencies may not be able to perform the duties within the tight timelines required.

A specialized team with the following qualifications and abilities can support the described tasks:

• 1 Project Director o Qualifications: Master’s Degree. At least two years of experience working in federal debt collection, and ten years of experience managing large federal projects.

o Functions: Lead or attend key meetings, ensure sufficient team staffing, supervise quality control procedures, identify subject matter experts as needed,

• 1 Project Supervisor o Qualifications: Master’s Degree. At least two years of experience working in federal debt collection. At least five years of experience managing projects in the federal space.

o Functions: Lead or attend key meetings, develop project management plan and timelines, ensure sufficient team staffing, supervise quality control procedures and implementation of project activities, identify subject matter experts as needed.

• 1 Expert Advisor o Qualifications: Master’s Degree. Experience managing projects across multiple government agencies for at least 10 years. At least two years of experience working in debt collection.

o Functions: Provides expert opinions needed to implement economic, financial, market, or debt collection industry analysis for the project, and present main findings and results to DMS senior leaders.

• 2 Economics Specialists o Qualifications: Doctoral or Master Degree in Economics. At least 5 years of experience developing predictive analytics models, forecasting, and using econometric techniques.

o Functions: Leads economic, statistical, or other analysis. Performs technical work and directs and reviews work of other staff.

Develops econometric and statistical models, supervises literature reviews, data analysis, and database development.

• 1 Database Administrator o Qualifications: Bachelor’s Degree. At least one year of experience working in debt collection. Experience developing and analyzing results of experiments, and surveys using large datasets. SAS, Stata, and SQL experience required.

o Functions: Develops econometric and statistical designs to answer specific research questions using large datasets. Carries out specific econometric and statistical steps/implementations, including data collection and programming.

• 1-2 Analysts o Qualifications: Bachelor’s Degree. At least one year of experience working in debt collection. Experience using relational databases. SAS, Stata, and SQL experience recommended.

o Functions: Works under general supervision to perform research and analysis in assigned subject matter area.

Deliverables:

The consultant shall perform the tasks listed below as applicable:

No. Task Deliverable Start Date Delivery Date

1.0 Improve Debt Recovery

Rates September 2016 May 2017

1.1 Develop Optimal

Collection Strategies

Descriptive document outlining the composition of agency debts and debtors, and historically effective collection tools.

September 2016 December 2016

1.2 Translate Analytical

Findings into Implementable Recommendations

Document describing potential pilots to be conducted in legacy FedDebt using segmentation data derived from previous pilots.

January 2017 March 2017

1.3 Optimize Collection Tool

Deployment

Econometric models for three creditor agencies to be determined by DMS, with risk profiles and collectability forecasting.

January 2017 May 2017

2.0 Improve Insight into

Business Processes

September 2016 June 2017

2.1 Implementing

Performance Reporting through Business Intelligence Tools

Automation of current performance reporting through the Fiscal Service data visualization tool (the FIR) and other desktop solutions identified by DMS.

September 2016 June 2017

2.2 Projection model

integration into performance measure reporting

Document describing how to integrate projection models developed by Fiscal Service.

March 2017 June 2017

2.3 Lag-Structure and Timing

Analysis

Document conducting the historical referral and collection activities for additional creditor agencies or collection tools relative to public projections

September 2016 January 2017

2.4 Projection Models Documentation of projection

models, relevant programming code, and analysis of the models’ performance

January 2017 March 2017

3.0 Fully Empower DMS

Leaders and Operations with Data through Analytics

3.1 Integrate and Reconcile

Databases Document analyzing data integration shortfalls and reconciliation issues.

September 2016 January 2017

3.2 Recommended Solutions

to Database Reconciliation Deficiencies

Document providing database reconciliation solutions

January 2017 August 2017

3.3 Develop Approximate

Matching Techniques

Analysis of industry standard data matching algorithms and identification of potential process improvements within TOP.

April 2017 August 2017

4.0 Train DMS Staff in

Analytical Capabilities

As requested As needed As needed

5.0 Ad-hoc analyses As requested As needed As needed

Contractor Performance Requirement Summary

No. Performance Objective based on tasks in PWS

Standard for Successful Performance

Acceptable Quality Level/ Acceptable Deviation from the Standard

Monitoring Method Incentive/Disincentive

1.0 Improve Debt Recovery Rates

Develop Optimal Collection Strategies

Translate Analytical Findings into Implementable Recommendations

Optimize Collection Tool Deployment

Standard is met

Performance input provided by Consultant’s day to day contact and COR

Positive or negative feedback document in the Federal Government’s Contractor Performance Assessment Reporting System (CPARS)

No. Performance Objective based on tasks in PWS

Standard for Successful Performance

Acceptable Quality Level/ Acceptable Deviation from the Standard

Monitoring Method Incentive/Disincentive

2.0 Improve Insight into Business

Processes

Implementing Performance Reporting through Business Intelligence Tools

Projection model integration into performance measure reporting

Lag-Structure and Timing Analysis

Projection Models

Standard is met

Performance input provided by Consultant’s day to day contact and COR

Positive or negative feedback document in the Federal Government’s Contractor Performance Assessment Reporting System (CPARS)

3.0 Fully Empower DMS Leaders and Operations with Data through Analytics

Integrate and Reconcile Databases

Recommended Solutions to Database Reconciliation Deficiencies

Develop Approximate Matching Techniques

Standard is met

Performance input provided by Consultants’ day to day to contact and COR

Positive or negative document in the Federal Government’s Contractor Performance Assessment Reporting System (CPARS)

4.0 Train DMS Staff in Analytical

Capabilities

Appropriate training in identified subjects with successful knowledge transfer

Standard is met Performance input provided by Consultants’ day to day to contact and COR

Positive or negative feedback documented in the Federal Government’s Contractor Performance Assessment Reporting System (CPARS)

5.0 Ad-hoc Support and Analyses

Provided Ad-hoc analysis and subject matter expertise in support of DMS Analytics’ current and ongoing activities and priorities

Standard is met Performance input provided by Consultant’s day to day contact and COR

Positive or negative feedback documented in the Federal Government’s Contractor Performance Assessment Reporting System (CPARS)

Place of Performance

The work shall be performed at Liberty Center Building or any location mutually agreed upon by Contractor and COR with prior approval.

Security

The contractor shall meet the security requirements listed in Attachment A Security Requirements document (See Attachment A)

Period of Performance

• September 17, 2016 – September 16, 2017

• Level of effort for all tasks outlined above is estimated at 4,320 hours

• Level of effort shall be managed by the Director of DMRAD monthly.

• All tasks shall be approved by the Director of DMRAD and COR prior to commencement of work to ensure effort stays within budget

Travel

Any contractor travel related to this contract must be approved by the COR. Contractor travel expenses will be reimbursed in accordance with federal travel regulations.