November 2022
With a substantial increase in fraud claims, limited processes for risk management, and overextended resources, unemployment insurance divisions within government agencies need a solution to address risk associated with potential fraudulent claims. The Thomson Reuters ID Risk Analytics solution delivers a risk assessment of claims utilizing identity verification data sets and analytics. The solution decreases the number of dollars lost to fraudulent claims, saves time for fraud investigators, and improves trust in data quality.
According to Forrester research, identity verification technologies allow for formulating and using a risk or confidence score to determine the likelihood that an online subject is who they claim to be; most importantly, these tools and processes reduce the likelihood of identity theft and, consequently, fraud losses.1 For government agencies, specifically within unemployment insurance departments, the number of submitted claims skyrocketed during the COVID-19 pandemic, as did the number of fraudulent claims. These groups required a solution to manage and categorize potential fraudulent claims.
Thomson Reuters ID Risk Analytics(IDRA) identifies and categorizes potential risk within agency claims using a variety of identity verification data sets (e.g., CLEAR ID Confirm) and analytics to provide risk analysis in a single dashboard.
Thomson Reuters commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying IDRA.2 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of IDRA on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed the representative of an organization who has experience using IDRA. For the purposes of this study, Forrester used this experience to project a three-year financial analysis.
Prior to using IDRA, the interviewee noted that their organization had an internal claims management system and team members used a spreadsheet to manage fraud claims. The influx of claims during the COVID-19 pandemic overwhelmed the team and made clear that their process could not handle the high number of claims.
After the investment in IDRA, the number of dollars lost to fraudulent claims decreased and fraud investigators saved time with data analysis. Additionally, a simpler process for analyzing claims led to an improved employee experience and greater trust in the data.
Consulting Team: Rishabh Dua, Claudia Heaney, Sarah Lervold
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits include:
Due to an immense number of claims and a constrained internal process, millions of dollars were lost to potential fraudulent claims. With IDRA and its analytics system to categorize risk, the representative organization has decreased the number of fraudulent claims paid out annually. For the interviewee’s organization, this adds up to a three-year benefit of $3.6 million.
Fraud investigators at the interviewee’s organization used to spend hours conducting data analysis. Upon implementing IDRA, which uses information from across databases and proprietary analytics to conduct risk analysis, fraud investigators save nine hours a week, equating to 480 hours annually on claims analysis. For the representative organization, IDRA enabled fraud investigator productivity savings of $466,000 over three years.
Unquantified benefits. Benefits that are not quantified in this study include:
With a singular solution that collects information from across databases and verifies the data, employees gain greater trust in the data available to verify claims information.
With a simpler and more efficient process to manage potentially fraudulent claims, resulting in less manual work, employees gain an improved working experience.
Costs. Three-year, risk-adjusted PV costs include:
The interviewee’s organization receives an initial, one-time report with a risk analysis on the backlog of claims and pays an annual subscription fee, adding up to $1.2 million.
Implementation tasks to set up the IDRA system and ongoing tasks for updating data and report creation add up to $224,000 over three years.
The interview and financial analysis found that the representative’s organization experiences benefits of $4.08 million over three years versus costs of $1.47 million, adding up to a net present value (NPV) of $2.61 million and an ROI of 178%.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that IDRA can have on an organization.
Interviewed Thomson Reuters stakeholders and Forrester analyst to gather data relative to IDRA.
Interviewed the representative of an organization using IDRA to obtain data with respect to costs, benefits, and risks.
Constructed a financial model representative of the interview using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewee.
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Readers should be aware of the following:
This study is commissioned by Thomson Reuters and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in IDRA.
Thomson Reuters reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Thomson Reuters provided the customer name for the interview but did not participate in the interview.
Forrester interviewed a criminal investigator in the benefit payment control (BPC) division of the unemployment insurance group within a state government agency. The organization has used the IDRA solution for a period between six months and a year. The organization has the following characteristics:
Prior to investment in IDRA, the interviewee’s organization used spreadsheets, had limited processes in place to find fraud cases, and had no process for risk categorization. The in-house solution to manage potentially fraudulent claims could not scale to meet demands.
The interviewee noted how the organization struggled with common challenges, including:
Prior to the COVID-19 pandemic, the unemployment insurance division had a limited number of fraud cases in a year (approximately 100 per year). The internal case management system had limited capabilities to quickly process cases. Team members used a manual process to conduct identity verification. Periodically (once a month or so), the team ran a report to assess fraudulent activity and then updated a spreadsheet with the results. There was no risk management program in place. With the onset of the pandemic, the number of fraud cases increased exponentially, leading to a backlog of nearly 800,000 claims; the legacy spreadsheet system was not equipped to process so many cases.
On the administration side, the BPC unit would conduct audits on claims. If a claimant had fraudulently collected more than a set amount, the case would be flagged and referred to the BPC team to conduct a separate and complete investigation of the case. If required, the case would be referred to a local district attorney. The interviewee noted that the teams could not manage the increase in claims, saying, “We just knew with as small as our team was and stretched as we were then, we needed something more automated.”
The interviewee’s organization searched for a solution that could meet the following requirements and objectives:
The primary use case, the interviewee explained, required an automated risk management tool that could assist in processing the immense number of claims. She said, “We [wanted to] utilize those [federal] funds in seeking out a tool that would assist us in identifying, categorizing these fraudulent claims and giving us kind of a point to work from ... that was our driving factor.”
For this use case, Forrester has modeled benefits and costs over three years.
Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
---|---|---|---|---|---|---|
Atr | Fraud loss cost avoidance | $2,520,000 | $840,000 | $840,000 | $4,200,000 | $3,616,228 |
Btr | Improved productivity for fraud investigators | $187,488 | $187,488 | $187,488 | $562,464 | $466,255 |
Total benefits (risk-adjusted) | $2,707,488 | $1,027,488 | $1,027,488 | $4,762,464 | $4,082,483 |
Before implementing IDRA and prior to the COVID-19 pandemic, given the few fraud cases (fewer than 100 annually), the unemployment insurance division had a minimal process in place for fraud detection. The division ran a report for fraud maybe once a month and the benefits control unit updated a spreadsheet with the cases flagged for fraud. During the COVID-19 pandemic, there was an exponential rise in unemployment insurance claims. The division, with limited staff and a push to pay out claims quickly, conducted limited fraud checks so as to move through the backlog of claims totaling millions of dollars.
After implementing IDRA, the team enjoyed a new and more robust system for risk analysis and fraud management. Every week, new claims data is sent to Thomson Reuters to update the IDRA system. Although the average number of new claims has decreased from the peaks of the pandemic, the number of claims remain higher than pre-pandemic numbers. The IDRA system analyzes and prioritizes claims based on criteria set by the division as well as a flag amount for potentially fraudulent claims. It cross-checks information from CLEAR ID Confirm and other data sets to automatically categorize claims into low, medium, and high categories. The categorized data allowed the agency to pause and stop claims that required further investigation, which led to fewer fraudulent claims paid out by the unemployment insurance division.
For the composite organization, Forrester assumes the following to quantify this benefit:
The magnitude of this benefit may vary based on:
To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.6 million.
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | ||
---|---|---|---|---|---|---|---|
A1 | Number of unemployment insurance claims flagged for fraud investigation by the benefit payment control unit | Interview | 6,000 | 2,000 | 2,000 | ||
A2 | Flag for fraud amount on an unemployment insurance claim | Interview | $7,500 | $7,500 | $7,500 | ||
A3 | Fraud loss cost avoidance attributable to IDRA | Interview | 7% | 7% | 7% | ||
At | Fraud loss cost avoidance | A1*A2*A3 | $3,150,000 | $1,050,000 | $1,050,000 | ||
Risk Adjustment | ↓20% | ||||||
Atr | Fraud loss cost avoidance (risk-adjusted) | $2,520,000 | $840,000 | $840,000 | |||
Three-year total: $4,200,000 | Three-year present value: $3,616,228 | ||||||
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Before implementing IDRA, once claims were referred from the benefits control unit, criminal investigators would investigate potentially fraudulent claims, conducting time- consuming, labor-intensive analysis. As part of the analysis, investigators would review information across different public-sector databases such as incarceration databases and social security and death master files. For example, team members working on incarceration cases had to reach out to prisons to verify the incarceration data from the Equifax Workforce Solutions database via email, fax, or phone calls. The COVID-19 pandemic brought a multitude of claims, and criminal investigators had to spend more time conducting the initial investigation into each potential fraud claim
The IDRA system gave fraud investigators in the investigations and criminal enforcement unit a more efficient process for claims investigations and data analysis. The IDRA system is updated weekly with new claims, and IDRA’s system integrates and cross- checks data across a variety of databases using CLEAR ID Confirm, access to which is included with IDRA. According to the interviewed criminal investigator, this data is presented in a single solution with an intuitive user interface on the system dashboard. Potentially fraudulent claims are automatically flagged via the system, so much of the initial data analysis performed has already been conducted. Thus, teams within the investigations and criminal enforcement unit save time and can work on additional priority tasks for flagged claims. According to the interviewee, that team employs 14 fraud investigators.
For the composite organization, Forrester assumes:
The magnitude of this benefit may vary based on:
To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV of $466,000.
Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | ||
---|---|---|---|---|---|---|---|
B1 | Number of fraud investigators | Interview | 14 | 14 | 14 | ||
B2 | Average hourly fully loaded salary per fraud investigator | Forrester standard | $31 | $31 | $31 | ||
B3 | Hours saved annually per fraud investigator | Interview | 480 | 480 | 480 | ||
Bt | Improved productivity for fraud investigators | B1*B2*B3 | $208,320 | $208,320 | $208,320 | ||
Risk adjustment | ↓10% | ||||||
Btr | Improved productivity for fraud investigators (risk-adjusted) | $187,488 | $187,488 | $187,488 | |||
Three-year total: $562,464 | Three-year present value: $466,255 | ||||||
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The interviewee mentioned the following additional benefits that their organization experienced but was not able to quantify:
The interviewee described how the teams working on fraud investigations had greater confidence in the data. IDRA uses data from across a variety of databases such as CLEAR ID Confirm to validate claims information and verify identity. The interviewee explained that the team saw fewer false positives because the IDRA system categorizes the data into high, medium, and low, resulting in the team having cleaner data at its disposal, compared to the data produced via manual processes with the legacy system. The interviewee elaborated on improved confidence in the data because of this. “There is greater trust [in the data] because it's coming from trusted sources,” she said.
The interviewee described how the teams working with fraud cases had a more efficient and simpler experience working through cases because of IDRA. The interviewee said: “Because our process was just 100% manual, you [would] do a ot of [work] trying to connect the dots. It was a really cognitively heavy load ... [now we have an] absolutely [better experience].
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement IDRA and later realize additional uses and business opportunities, including:
The interviewee described the option of working with the data available through IDRA and applying the findings to different types of cases: “[IDRA has provided] more flexibility. I think IDRA really gives us that ability to data mine .... I’m excited to see that down the road.”
The interviewee said that the data available in IDRA could be used to support cases that go to prosecution: “What I would like to see for our team — and I know other folks [would] like to see that as well — we have investigators who specialize in incarceration cases that need to go to prosecution [who] specialize in working on while collecting [cases] to folks who specialize in ID theft [cases]. What I’m most excited about [is to] get these cases moving that maybe we wouldn’t have been able to do before.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A ).
Ref. | Costs | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
---|---|---|---|---|---|---|---|
Ctr | IDRA batch analysis and subscription cost | $1,052,700 | $77,000 | $77,000 | $77,000 | $1,283,700 | $1,244,188 |
Dtr | Agency implementation costs | $203,632 | $0 | $0 | $0 | $203,632 | $203,632 |
Etr | Agency labor costs | $0 | $8,184 | $8,184 | $8,184 | $24,552 | $20,352 |
Total costs (risk-adjusted) | $1,256,332 | $85,184 | $85,184 | $85,184 | $1,511,884 | $1,468,172 |
Prior to implementing the IDRA solution, the organization received a one-time batch analysis on the backlog of prior claims, presented as a risk analysis report. The large backlog of claims accrued during the COVID-19 pandemic. Once the IDRA system was implemented, the organization also paid an annual subscription fee.
For the composite organization, Forrester assumes the following:
These costs may vary based on:
To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three- year, risk-adjusted total PV (discounted at 10%) of $1.2 million.
Ref | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|---|
C1 | One-time batch analysis cost | Interview | $957,000 | ||||
C2 | Annual IDRA subscription cost | Interview | $70,000 | $70,000 | $70,000 | ||
Ct | IDRA batch analysis and subscription cost | C1+C2 | $957,000 | $70,000 | $70,000 | $70,000 | |
Risk adjustment | ↑10% | ||||||
Ctr | IDRA batch analysis and subscription cost (risk-adjusted) | $1,052,700 | $77,000 | $77,000 | $77,000 | ||
Three-year total: $1,283,700 | Three-year present value: $1,244,188 | ||||||
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The interviewee described a six- month implementation period with up to five employees on the project team, which included team members from the BPC team as well as IT resources.
For the composite organization, Forrester assumes the following:
This cost may vary based on:
To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three- year, risk-adjusted total PV of $204,000.
Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|---|
D1 | Number of IT resources | Interview | 2 | ||||
D2 | Average hourly fully loaded salary for IT resource | Forrester standard | $58 | ||||
D3 | Number of fraud investigators | Interview | 2 | ||||
D4 | Average hourly fully loaded salary per fraud investigator | Forrester standard | $31 | ||||
D5 | Time required for implementation (hours) | Interview | 1,040 | ||||
Dt | Agency implementation costs | ((D1*D2) +(D3*D4))*D5 | $185,120 | $0 | $0 | $0 | |
Risk adjustment | ↑10% | ||||||
Dtr | Agency implementation costs (risk- adjusted) | $203,632 | $0 | $0 | $0 | ||
Three-year total: $203,632 | Three-year present value: $203,632 | ||||||
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The interviewee described the process of updating the data within the IDRA system on a weekly basis. As the product owner, the interviewee would collect new claims data each week and send the new weekly claims data to Thomson Reuters to be entered into the system, which would be updated within several days. The interviewee would also review the data and create reports to be sent out to other groups within the division.
For the composite organization, Forrester assumes the following:
This cost may vary based on:
To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three- year, risk-adjusted total PV of $20,000.
Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
---|---|---|---|---|---|---|---|
E1 | Fraud investigator hours for ongoing work | Interview | 240 | 240 | 240 | ||
E2 | Average hourly fully loaded salary for fraud investigator | Forrester standard | $31 | $31 | $31 | ||
Et | Agency labor costs | E1*E2 | $0 | $7,440 | $7,440 | $7,440 | |
Risk adjustment | ↑10% | ||||||
Etr | Agency labor costs (risk-adjusted) | $0 | $8,184 | $8,184 | $8,184 | ||
Three-year total: $24,552 | Three-year present value: $20,352 | ||||||
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These risk-adjusted ROI and NPV values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
---|---|---|---|---|---|---|
Total costs | ($1,256,332) | ($85,184) | ($85,184) | ($85,184) | ($1,511,884) | ($1,468,172) |
Total benefits | $0 | $2,707,488 | $1,027,488 | $1,027,488 | $4,762,464 | $4,082,483 |
Net benefits | ($1,256,332) | $2,622,304 | $942,304 | $942,304 | $3,250,580 | $2,614,311 |
ROI | 178% | |||||
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Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
1 Source: “The Identity Verification (IDV) Landscape, Q3 2022,” Forrester Research. Inc., September 6, 2022.
2 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
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