Executive Summary
As fraudsters continually make use of new technology to evolve their tactics, fraud management has become a rapidly and constantly evolving journey. Traditional static, rules-based fraud models that rely on historical data quickly become outdated and ineffective against new types of fraud.
Lynx uses an AI-based daily adaptive model to protect against various forms of fraud, including identity theft, scams, account takeovers, and authorized push payment fraud (APPF) using adaptive models to analyze real-time anomalies.
Lynx commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Lynx Fraud Prevention.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Lynx on their organizations.
307%
Return on investment (ROI)
$9.4M
Net present value (NPV)
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed three decision-makers with experience using Lynx. For the purposes of this study, Forrester aggregated the experiences of the interviewees and combined the results into a single composite organization, which is a bank based in the United States that has $5 billion in annual revenue and handles more than 500 million monetary transactions annually.
Interviewees said that before using Lynx, their organizations had rules-based consortia fraud prevention models. However, these static solutions could not defend against evolving fraud trends, resulting in missed fraudulent transactions and excessive false positive alerts. Consequently, these organizations had to dedicate numerous resources to fraud operations to investigate alerts and follow up with customers on disputed transactions. These static models also required regular retraining (two to four times per year), which consumed resources and effort.
After adopting Lynx Fraud Prevention solutions, the interviewees observed a marked improvement in the accuracy of their fraud prevention, especially in catching fraud and decreasing false positives, which decreased the need for manual investigations and follow-up and ultimately led to fewer fraud losses.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
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Total savings on fraud investigations. One major challenge the composite organization faced with its legacy fraud solution was high false positive rates, which required manual investigation into each individual alert. With Lynx, the composite organization reduces false positives from 80:1 to 10:1, reducing the number of alerts that need to be investigated. Over three years, and with more than 1.5 million monetary transactions handled daily, the time savings on fraud investigations amount to $8.6 million for the composite organization.
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Reduction in fraud losses. With improved accuracy in fraud detection, Lynx helps reduce the number of fraud losses the composite organization incurs by 50%, from approximately $4 million per year to $2 million per year. Assuming that about 70% of disputed transactions eventually get paid out, the reduction in fraud losses amounts to $2.8 million over three years.
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Revenue recaptured through reducing customer friction. The high levels of false positive rates resulted in authentication checks or even automatically rejected transactions, creating friction for customers. With an estimated 20% of customers abandoning flagged/rejected transactions, this led to lost revenue for the composite organization through the commissions on transactions. Reducing flagged transactions, and thus improving the customer experience, enables the composite organization to recapture $870,000 in revenue over three years.
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Improved fraud management processes. Previously, managing and updating fraud risk rules with its legacy system was a slow and cumbersome process. Each change required coordination with the vendor, including testing and approval cycles, which could take up to two weeks to complete. With Lynx, however, the composite organization can implement rule changes within minutes, reducing the time and effort involved. What used to take 3 hours per change now takes just 30 minutes. Since it makes approximately 30 policy changes per week, this efficiency translates into an estimated $216,000 in time savings over three years.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
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Strengthened brand reputation and customer trust. The composite organization enhances its ability to deliver fast, secure customer experiences, supporting its brand promise and reinforcing customer confidence. This supports its ability to differentiate in the market and deliver fast transactions without compromising security, reinforcing its value proposition to customers.
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Improved employee job satisfaction by allowing fraud teams to focus on more value-adding work. The composite organization’s fraud teams transform the way they operate. Improved fraud detection reduces the number of false positive alerts requiring investigation, allowing teams to focus on analyzing trends and optimizing risk policies instead.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
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Lynx license fees. Based on an estimated 1.5 million monetary transactions handled daily, and assuming a cloud deployment, the composite organization incurs a total of $2.8 million in license, maintenance, support, and infrastructure costs over three years.
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Total implementation costs. The composite organization spends about 4 months implementing Lynx, which includes running through a pilot and then scaling up to full implementation. Approximately seven employees each spend about 10 hours per week on the implementation. These resource costs, in addition to the professional service fees paid to Lynx for help with the implementation, cost the composite organization about $220,000.
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Ongoing management and training costs. Ongoing costs include the time that fraud analysts spend learning to use the Lynx platform, as well as project and admin management efforts. Together, these costs add up to $44,000 for the composite organization.
The financial analysis that is based on the interviews found that the composite organization experiences benefits of $12.5 million over three years versus costs of $3.1 million, adding up to a net present value (NPV) of $9.4 million and an ROI of 307%.
Key Statistics
307%
Return on investment (ROI)
$12.5M
Benefits PV
$9.4M
Net present value (NPV)
<6 months
Payback
Benefits (Three-Year)
The Lynx Fraud Prevention Customer Journey
Drivers leading to the Lynx investment
Interviews
| Role | Industry | Daily Transactions | Assets/Revenue |
|---|---|---|---|
| Head of antifraud technology | Tier-one bank | 2 million | Revenue: $3.5 million |
| Head of corporate security | Tier-one bank | 25 million | Assets: $40 billion |
| Head of fraud strategy | Tier-one bank | 1.5 million | Assets: $165 billion |
Key Challenges
Before Lynx, interviewees shared that their organizations had rules-based consortia fraud prevention models. However, these static solutions could not defend against evolving fraud trends, resulting in missed fraudulent transactions and excessive false positive alerts. This resulted in some shared, common challenges, including:
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Rigid, rules-based fraud solutions that were ineffective in preventing fraud. Interviewees shared that the rules-based fraud solutions they were using lacked flexibility and scalability. These systems struggled to keep pace with the rapid evolution of fraud trends — such as authorized fraud or APP that are more difficult to detect — and required frequent policy changes that were also difficult to implement.
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Outdated models that gave excessively high false positive rates. All three interviewees experienced high false positive rates in the range of 80:1 to 100:1, which led to huge amounts of resources dedicated to unnecessary investigations and increased operational costs. They also negatively impacted customer experience due to unnecessary friction and disruptions. One interviewee mentioned high false positive rates becoming particularly problematic as digital banking adoption accelerates, which would make it difficult for their organization to keep up with investigations.
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Fragmented fraud solutions. The interviewees’ banks used separate fraud detection systems for different transaction types, complicating scalability and increasing the learning curve for employees across various departments, including fraud teams and contact center staff.
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Resource-intensive processes. The interviewees’ banks relied on fixed rule-based systems for fraud detection, where modifications required manual updates by developers and redeployment to production — meaning it often took up to two weeks to implement policy changes. This limited the banks’ ability to respond quickly to evolving fraud tactics and increased the risk of operational losses.
Solution Requirements
Interviewees highlighted a need for more modern, sophisticated fraud prevention solutions that use AI and deep learning to help improve detection accuracy, enhance scalability, and deliver a better customer experience. They searched for a solution that could provide:
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Improved fraud detection accuracy and performance. Interviewees needed a daily adaptative model that would consistently outperform their consortia models in identifying and blocking fraudulent activities.
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Flexibility and ease of integration. Interviewees sought a platform that could integrate smoothly with their banks’ core systems using multiple integration methods, including file transfers and APIs, to simplify the implementation process and reduce operational complexity.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the interviewees’ organizations, and it is used to present the aggregate financial analysis in the next section.
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Description of composite. The composite is a retail bank in the United States with 20,000 employees across different locations and approximately $5 billion in annual revenue. It handles 1.5 million monetary transactions daily (including debit and credit card transactions and account transfers) and has a fraud team with 70 full-time employees (FTEs), including policy managers and analysts investigating fraud alerts.
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Deployment characteristics. Before Lynx, the composite organization used several different fraud solutions for different channels. The bank first implemented Lynx with a proof-of-concept for six months, running it alongside legacy solutions before rolling it out to process all monetary and nonmonetary transactions.
KEY ASSUMPTIONS
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$5 billion annual revenue
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1.5 million monetary transactions processed daily
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70 FTEs on fraud team
Analysis Of Benefits
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Total savings on fraud investigations | $3,163,667 | $3,480,035 | $3,828,049 | $10,471,750 | $8,628,192 |
| Btr | Reduction in fraud losses | $1,120,000 | $1,120,000 | $1,120,000 | $3,360,000 | $2,785,274 |
| Ctr | Revenue recaptured through reducing customer friction | $318,919 | $350,811 | $385,892 | $1,055,621 | $869,778 |
| Dtr | Improved fraud management processes | $87,048 | $87,048 | $87,048 | $261,144 | $216,475 |
| Total benefits (risk-adjusted) | $4,689,633 | $5,037,894 | $5,420,988 | $15,148,515 | $12,499,719 |
Total Savings On Fraud Investigations
Evidence and data. One major challenge that all interviewees shared was that before Lynx, they all had excessively high false positive rates with their legacy fraud systems. Two of the interviewees reported a false positive rate of more than 100:1 (or 100 false alerts for every one true positive).
The head of corporate security at a tier-one bank said, “The system was inefficient, required many staff, and couldn’t keep up with our digital banking growth.” They also said that since switching to Lynx, they had reduced their operational workload by 70%.
Similarly, the head of fraud strategy at another tier-one bank shared that with Lynx, their false positive rates for card transactions decreased to “almost 1:1,” and that the reduction in false positive alerts had helped them reduce time and resources spent on investigations by 40%.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization processes 1.5 million monetary transactions daily.
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Before Lynx, about 1% of transactions are flagged, and 98.75% of these turn out to be false positives (i.e., a false positive rate of 80:1). With Lynx, 0.45% of transactions are flagged, and 90% of these turn out to be false positives (i.e., a false positive rate of 10:1). This improvement corresponds to a 55% reduction in false positive alerts.
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The fraud team manually investigates about 20% of these alerts and spends about 15 minutes on each investigation.
Risks. Several factors can influence the impact that organizations will see with regard to time savings on fraud investigations, including:
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The volume of transactions handled daily.
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The performance of a bank’s legacy fraud model.
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An organization’s rules and policies regarding fraud risk and investigations.
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $8.6 million.
55%
Total reduction in false positive alerts
Total Savings On Fraud Investigations
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Monetary transactions handled | Composite | 547,500,000 | 602,250,000 | 662,475,000 | |
| A2 | Percentage of transactions flagged as potential fraud before Lynx | Interviews | 1% | 1% | 1% | |
| A3 | Transactions flagged as potential fraud before Lynx | A1*A2 | 5,475,000 | 6,022,500 | 6,624,750 | |
| A4 | False positives before Lynx (false positive rate of 80:1) | (80-1)/80*A3 | 5,406,563 | 5,947,219 | 6,541,941 | |
| A5 | Percentage of alerts requiring manual investigation | Composite | 20% | 20% | 20% | |
| A6 | Average time spent investigating each alert (hours) | Interviews | 0.25 | 0.25 | 0.25 | |
| A7 | Total time spent investing false positive alerts before Lynx (hours) | A4*A5*A6 | 270,328 | 297,361 | 327,097 | |
| A8 | Percentage of transactions flagged as fraud with Lynx | Interviews | 0.45% | 0.45% | 0.45% | |
| A9 | Transactions flagged as fraud with Lynx | A1*A8 | 2,463,750 | 2,710,125 | 2,981,138 | |
| A10 | False positives with Lynx (false positive rate of 10:1) | (10-1)/10*A9 | 2,217,375 | 2,439,113 | 2,683,024 | |
| A11 | Total time spent investigating false positive alerts with Lynx (hours) | A10*A5*A6 | 110,869 | 121,956 | 134,151 | |
| A12 | Total time savings on fraud investigations (hours) | A7-A11 | 159,459 | 175,405 | 192,946 | |
| A13 | Fully burdened hourly rate for a fraud analyst | Composite | $31 | $31 | $31 | |
| A14 | Productivity recaptured | TEI methodology | 80% | 80% | 80% | |
| At | Total savings on fraud investigations | A12*A13*A14 | $3,954,583 | $4,350,044 | $4,785,061 | |
| Risk adjustment | ↓20% | |||||
| Atr | Total savings on fraud investigations (risk-adjusted) | $3,163,667 | $3,480,035 | $3,828,049 | ||
| Three-year total: $10,471,750 | Three-year present value: $8,628,192 | |||||
Reduction In Fraud Losses
Evidence and data. Alongside high false positives, interviewees also shared that over time, their legacy fraud models became increasingly outdated and inefficient at detecting fraud, especially new fraud patterns like scams, phishing, and other APPF.
The head of antifraud technology at a tier-one bank shared that Lynx helped their organization lower its average fraud disputes from $1.8 million to $450,000. A second interviewee shared that with Lynx, their organization can now detect 85% of fraud attacks, with substantial savings on its fraud losses.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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Lynx reduces transaction amounts in dispute by 50%, from $4 million per year to $2 million per year.
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Of disputed transactions, approximately 70% are eventually paid after an investigation.
Risks. Factors that can impact an organization’s reduction in fraud losses include:
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Laws and regulations for a bank’s liability and responsibility towards consumers when it comes to fraudulent transaction claims.
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A bank’s own policy on how it handles fraudulent transactions claims.
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.8 million.
50%
Reduction in fraudulent transactions in dispute
Reduction In Fraud Losses
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Transaction amount in dispute before Lynx | Interviews | $4,000,000 | $4,000,000 | $4,000,000 | |
| B2 | Transaction amount in dispute with Lynx | Interviews | $2,000,000 | $2,000,000 | $2,000,000 | |
| B3 | Percentage of disputed transactions paid out | Composite | 70% | 70% | 70% | |
| Bt | Reduction in fraud losses | B3*(B1-B2) | $1,400,000 | $1,400,000 | $1,400,000 | |
| Risk adjustment | ↓20% | |||||
| Btr | Reduction in fraud losses (risk-adjusted) | $1,120,000 | $1,120,000 | $1,120,000 | ||
| Three-year total: $3,360,000 | Three-year present value: $2,785,274 | |||||
Revenue Recaptured Through Reducing Customer Friction
Evidence and data. Another consequence of the high false positive rates was the disruption to customers through excessive authentication checks, or even denial of legitimate transactions. Interviewees worried that this put some customers off the transaction, and that these customers would then switch to using another card or bank for the transaction.
The head of antifraud technology at a tier-one bank shared that with their old solution, 20% of transactions triggered additional authentication, whereas with Lynx, only 3% now require authentication. Furthermore, whereas the previous solution only allowed for facial biometric authentication (which may be a barrier for customers without a smart phone), Lynx accommodates multiple authentication options like SMS, tokens, and WhatsApp.
Similarly, a second interviewee shared that customers now enjoyed more convenience since the switch to Lynx. Whereas before Lynx, customers had to inform the bank ahead of travelling to enable use of their cards overseas, this was no longer necessary with Lynx. Additionally, with the consolidation of several different fraud solutions into one, it was easier for call center representatives to learn how to work with a singular fraud prevention platform, allowing them to serve customers better.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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Twenty percent of customers who encounter a denial of transaction or an authentication check will abandon the transaction. For instance, they may not have the right authentication tool at hand, or they may switch to using a different bank’s payment method.
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The average transaction value is $25, and the bank earns a 2.5% commission on each transaction.
Risks. Factors that can impact an organization’s ability to recover lost revenue include its existing fraud risk management policies.
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $870,000.
Revenue Recaptured Through Reducing Customer Friction
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Reduction in falsely flagged transactions | A4-A10 | 3,189,188 | 3,508,106 | 3,858,917 | |
| C2 | Percentage of abandoned transactions | Composite | 20% | 20% | 20% | |
| C3 | Average value of transaction | Composite | $25 | $25 | $25 | |
| C4 | Bank commission per transaction | Composite | 2.5% | 2.5% | 2.5% | |
| Ct | Revenue recaptured through reducing customer friction | C1*C2*C3*C4 | $398,648 | $438,513 | $482,365 | |
| Risk adjustment | ↓20% | |||||
| Ctr | Revenue recaptured through reducing customer friction (risk-adjusted) | $318,919 | $350,811 | $385,892 | ||
| Three-year total: $1,055,621 | Three-year present value: $869,778 | |||||
Improved Fraud Management Processes
Evidence and data. Interviewees shared that with their legacy systems, managing and updating fraud risk rules was a slow and cumbersome process. Each change required significant development time and vendor coordination (including testing and approval cycles) and could take up to two weeks to complete. With Lynx, however, the composite organization can write and implement rule changes within minutes — dramatically reducing the time and effort involved.
One interviewee shared: “Before Lynx, it took at least 3 hours from drafting the rule to sending it to the development team and deploying it. Now, it takes about 15 minutes on average. Changes are faster too: Some can be done in just 1 minute if they’re simple. On average, changes take around 5 minutes.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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It makes about 30 policy changes per week.
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Before Lynx, each policy change took 3 hours to write, test, and deploy. With Lynx, this entire process takes 30 minutes.
Risks. Factors that can impact an organization’s ability to save time on improved fraud management processes include:
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The frequency of policy changes.
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How an organization’s legacy fraud systems are set up.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $216,000.
Improved Fraud Management Processes
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Average policy changes (30 per week) | Composite | 1,560 | 1,560 | 1,560 | |
| D2 | Time taken to execute a policy change before Lynx (hours) | Composite | 3 | 3 | 3 | |
| D3 | Time taken to execute a policy change with Lynx (hours) | Composite | 0.50 | 0.50 | 0.50 | |
| D4 | Time savings to execute each policy change with Lynx (hours) | D2-D3 | 2.50 | 2.50 | 2.50 | |
| D5 | Total time savings for managing fraud policy changes | D1*D4 | 3,900 | 3,900 | 3,900 | |
| D6 | Fully burdened hourly rate for a fraud analyst | Composite | $31 | $31 | $31 | |
| D7 | Productivity recaptured | TEI methodology | 80% | 80% | 80% | |
| Dt | Improved fraud management processes | D5*D6*D7 | $96,720 | $96,720 | $96,720 | |
| Risk adjustment | ↓10% | |||||
| Dtr | Improved fraud management processes (risk-adjusted) | $87,048 | $87,048 | $87,048 | ||
| Three-year total: $261,144 | Three-year present value: $216,475 | |||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
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Strengthened brand reputation and customer trust. Reputation and trust are imperative for a bank to attract and retain customers. One interviewee shared that being the first in their market to use an AI-powered fraud solution was good PR for them. The composite organization enhances its ability to deliver fast, secure customer experiences, supporting its brand promise and reinforcing customer confidence. This supports its ability to differentiate in the market and deliver fast transactions without compromising security, reinforcing its value proposition to customers.
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Improved employee job satisfaction by allowing fraud teams to focus on more value-adding work. The composite organization’s fraud teams transform the way they operate. Improved fraud detection reduces the number of false positive alerts requiring investigation, allowing teams to focus on analyzing trends and optimizing risk policies instead.
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Lynx and later realize additional uses and business opportunities, including:
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Avoided internal resource costs. Interviewees shared that as an alternative to purchasing a fraud solution like Lynx, some banks choose to build their own solution. However, this would require investment for additional infrastructure, as well as time and effort. By using Lynx, organizations gain ongoing access to a continuously updated solution with specialized AI knowledge and expertise.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
Analysis Of Costs
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Etr | Lynx license fees | $0 | $1,029,380 | $1,132,318 | $1,245,550 | $3,407,248 | $2,807,400 |
| Ftr | Total implementation costs | $219,692 | $0 | $0 | $0 | $219,692 | $219,692 |
| Gtr | Ongoing management and training costs | $16,368 | $11,185 | $11,185 | $11,185 | $49,922 | $44,183 |
| Total costs (risk-adjusted) | $236,060 | $1,040,565 | $1,143,503 | $1,256,735 | $3,676,862 | $3,071,275 |
Lynx License Fees
Evidence and data. Lynx offers pricing based on the volume of transactions handled and deployment options (i.e., on-premises versus cloud). This pricing scenario is an estimate only. Readers should reach out to Lynx to discuss specific requirements to get a more accurate cost estimate.
Modeling and assumptions. Cost estimates in this study are provided by Lynx and are based on these assumptions about the composite organization:
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It parses 1.5 million monetary transactions and 750,000 nonmonetary transactions through Lynx daily.
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Lynx is deployed as a SaaS solution with servers located in the United States.
Risks. Factors that will impact the fees incurred include:
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Volume of transactions handled, which could depend on the number of channels and/or payment methods integrated.
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Mode of Lynx deployment.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.8 million.
Lynx License Fees
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | License fees | Composite | $575,000 | $632,500 | $695,750 | |
| E2 | Maintenance, support, and infrastructure costs | Composite | $360,800 | $396,880 | $436,568 | |
| Et | Lynx license fees | E1+E2 | $0 | $935,800 | $1,029,380 | $1,132,318 |
| Risk adjustment | ↑10% | |||||
| Etr | Lynx license fees (risk-adjusted) | $0 | $1,029,380 | $1,132,318 | $1,245,550 | |
| Three-year total: $3,407,248 | Three-year present value: $2,807,400 | |||||
Total Implementation Costs
Evidence and data. On average, interviewees said that implementation took between two to six months, including a pilot phase where they ran Lynx alongside their legacy solution.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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It takes four months to implement Lynx. Over this period, seven FTEs each spend 10 hours working on the implementation.
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It incurs additional professional service fees, paid to Lynx, for help with integrating the platform alongside its existing banking systems.
Risks. Forrester recognizes that these results may not be representative of all experiences and that results will vary depending on the following factors:
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The complexity of an organization’s IT environment.
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The complexity of the local market’s laws and regulations for fraud and risk management.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $220,000.
Total Implementation Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | FTEs involved in pilot and implementation | Interviews | 7 | |||
| F2 | Time spent on project implementation (hours per week) | Interviews | 10 | |||
| F3 | Total FTE time spent on project implementation over a 16-week period (hours) | F1*F2*16 | 1,120 | |||
| F4 | Fully burdened hourly rate for a fraud analyst | Composite | $31 | |||
| F5 | Internal resource costs for Lynx implementation | F3*F4 | $34,720 | |||
| F6 | Professional services fees | Interviews | $165,000 | |||
| Ft | Total implementation costs | F5+F6 | $199,720 | $0 | $0 | $0 |
| Risk adjustment | ↑10% | |||||
| Ftr | Total implementation costs (risk-adjusted) | $219,692 | $0 | $0 | $0 | |
| Three-year total: $219,692 | Three-year present value: $219,692 | |||||
Ongoing Management And Training Costs
Evidence and data. Interviewees shared that once implemented, the Lynx platform required minimal maintenance.
Modeling and assumptions. Based on the interviews, Forrester assumes that the composite’s fraud analysts initially spend 8 hours learning how to use the platform and an additional 2 hours per year to keep up with changes.
Risks. Factors that can impact management and training costs include the experience of an organization’s fraud team.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $44,000.
Ongoing Management And Training Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| G1 | Fraud analysts | Composite | 60 | 60 | 60 | 60 |
| G2 | Time spent on training (hours) | Interviews | 8 | 2 | 2 | 2 |
| G3 | Fully burdened hourly rate for a fraud analyst | Composite | $31 | $31 | $31 | $31 |
| G4 | Training costs | G1*G2*G3 | $14,880 | $3,720 | $3,720 | $3,720 |
| G5 | Ongoing project management (4 FTE hours per week) | Interviews | 208 | 208 | 208 | |
| G6 | Ongoing project management costs | G3*G5 | $6,448 | $6,448 | $6,448 | |
| Gt | Ongoing management and training costs | G4+G6 | $14,880 | $10,168 | $10,168 | $10,168 |
| Risk adjustment | ↑10% | |||||
| Gtr | Ongoing management and training costs (risk-adjusted) | $16,368 | $11,185 | $11,185 | $11,185 | |
| Three-year total: $49,922 | Three-year present value: $44,183 | |||||
Financial Summary
Consolidated Three-Year, Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Cash Flow Analysis (Risk-Adjusted)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($236,060) | ($1,040,565) | ($1,143,503) | ($1,256,735) | ($3,676,862) | ($3,071,275) |
| Total benefits | $0 | $4,689,633 | $5,037,894 | $5,420,988 | $15,148,515 | $12,499,719 |
| Net benefits | ($236,060) | $3,649,069 | $3,894,391 | $4,164,254 | $11,471,653 | $9,428,444 |
| ROI | 307% | |||||
| Payback | <6 months |
Please Note
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
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.
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Fraud Prevention.
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 Fraud Prevention can have on an organization.
Due Diligence
Interviewed Lynx stakeholders and Forrester analysts to gather data relative to Fraud Prevention.
Interviews
Interviewed three decision-makers at organizations using Fraud Prevention to obtain data about costs, benefits, and risks.
Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
Financial Model Framework
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
Case Study
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.
Total Economic Impact Approach
Benefits
Benefits represent the value the solution delivers to the business. The TEI methodology places equal weight on the measure of benefits and costs, allowing for a full examination of the solution’s effect on the entire organization.
Costs
Costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
Flexibility
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
Risks
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.”
Financial Terminology
Present value (PV)
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PV of costs and benefits feed into the total NPV of cash flows.
Net present value (NPV)
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
Return on investment (ROI)
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
Discount rate
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
Payback
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
Appendix A
Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Appendix B
Endnotes
1 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Disclosures
Readers should be aware of the following:
This study is commissioned by Lynx 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 Fraud Prevention. For any interactive functionality, the intent is for the questions to solicit inputs specific to a prospect’s business. Forrester believes that this analysis is representative of what companies may achieve with Fraud Prevention based on the inputs provided and any assumptions made. Forrester does not endorse Lynx or its offerings. Although great care has been taken to ensure the accuracy and completeness of this model, Lynx and Forrester Research are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein. The interactive tool is provided ‘AS IS,’ and Forrester and Lynx make no warranties of any kind.
Lynx 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.
Lynx provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Josephine Phua
Published
September 2026
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