The Total Economic Impact™ Of The TransUnion Synthetic Fraud Solution

Reduced Synthetic Fraud Losses

Evidence and data. Interviewees said Synthetic Fraud Solution improved their organizations’ ability to identify synthetic identities during account origination and onboarding. By incorporating synthetic fraud risk signals into existing fraud and underwriting workflows, interviewees reported higher confidence in fraud decision-making and lower fraud-related losses.

• The head of credit functions at an automotive financial services organization estimated that synthetic fraud detection reduced fraud losses by approximately 25% and generated annual savings in the seven figures.

• The SVP of acquisition fraud strategy at a consumer financial services organization estimated that synthetic fraud losses declined by 80% to 90% after implementing synthetic fraud detection capabilities. The interviewee noted, “Today, our synthetic fraud losses are probably in the low six figures.”

• The head of credit functions at an automotive financial services organization emphasized the value of additional fraud intelligence, stating, “Without it, we would be exposed to significantly more risk.” The interviewee explained that synthetic fraud scoring provided an additional layer of information to help identify potentially fraudulent applicants before accounts were opened.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• It processes 525,000 applications annually.

• The composite experiences a 0.25% loss-generating synthetic fraud incidence rate.

• Each loss-generating synthetic fraud account produces an average realized loss of approximately $2,000.

• The modeled benefit reflects a conservative estimate of fraud losses prevented through improved identification of higher-risk synthetic identities during account origination.

Risks. The scale of this benefit may vary by organization based on:

• Application volume and customer base size.

• Existing fraud program maturity and effectiveness.

• Fraud investigation, monitoring, and response practices.

• An organization’s ability to incorporate synthetic fraud intelligence into existing underwriting, fraud review, and fraud decisioning processes.

Results. To account for these risks, Forrester adjusted this benefit downward by 25%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $245,000.

Reduced Fraud Operations Effort

Evidence and data. Interviewees said Synthetic Fraud Solution helped their organizations focus fraud investigations on higher-risk applications and reduce the number of applications requiring manual review. By providing additional synthetic fraud intelligence during account origination, interviewees reported improved efficiency across fraud and underwriting operations while allowing teams to spend more time investigating the applications most likely to represent fraud risk.

• The EVP of credit strategy at a consumer finance organization estimated that fraud strategy improvements, including the use of synthetic fraud detection capabilities, reduced manual review volumes by approximately 25%. Before these improvements, the organization manually reviewed roughly 1,000 to 2,000 applications per month.

• The same interviewee said manual reviews typically required 10 to 15 minutes per application and noted that better fraud intelligence helped the organization “separate the good from the bad” more effectively during the credit review process.

• The SVP of acquisition fraud strategy at a consumer financial services organization estimated that fraud operations efficiency improved by 15% to 20%. The interviewee explained that stronger fraud signals helped focus operational resources on higher-risk applicants rather than broadly reviewing larger volumes of applications.

• Interviewees consistently said that improved risk identification reduced unnecessary investigative effort and enabled fraud teams to make decisions more quickly and confidently. One interviewee explained that the solution helped the organization identify “where we need to spend time reviewing and where we don’t.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization processes 525,000 applications annually.

• Approximately 7% of applications require manual review, resulting in 36,750 manual reviews each year.

• Each manual review requires 15 minutes of fraud analyst time on average.

• Improved fraud intelligence reduces manual review activity by approximately 20% and helps analysts focus on higher-risk applications.

• The fully burdened hourly rate for a fraud analyst is $60.

• Forrester applies a 50% productivity recapture rate to reflect the portion of time savings that can be redirected toward higher-value fraud analysis and investigation activities.

• The modeled benefit reflects labor effort redirected from lower-value manual review activities to higher-value fraud analysis, investigation, and risk management work.

Risks. The scale of this benefit may vary by organization based on:

• Application volume and baseline manual review requirements.

• Existing fraud operations processes, decisioning workflows, and automation levels.

• Fraud analyst workflows, review procedures, and handling times.

• Fraud program maturity and an organization’s ability to incorporate synthetic fraud intelligence into operational decision-making.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $117,000.

Incremental Profit From Improved Legitimate-Application Conversion

Evidence and data. Interviewees said Synthetic Fraud Solution helped their organizations make more informed decisions during account origination, enabling them to better distinguish legitimate applicants from potentially fraudulent identities. As a result, interviewees reported fewer unnecessary reviews, more efficient approval processes, and greater confidence in approval decisions while balancing fraud prevention and customer acquisition objectives.

• The EVP of credit strategy at a consumer finance organization said synthetic fraud intelligence improved the organization’s ability to identify legitimate applicants while maintaining fraud controls. The interviewee explained that the solution helped reduce uncertainty during credit decisioning, allowing more qualified applicants to progress through the approval process.

• The SVP of acquisition fraud strategy at a consumer financial services organization said the value of synthetic fraud detection extended beyond fraud detection alone. The interviewee explained: “You want to stop the fraudsters, but you don’t want to impact the legitimate customers that are trying to do business with you. The better your intelligence is, the better you can do both.” Stronger fraud intelligence enabled the organization to reduce unnecessary friction for legitimate applicants while maintaining fraud controls.

• The EVP of credit strategy at a consumer finance organization also described how improved fraud decisioning supported customer acquisition outcomes. The interviewee noted that more accurate identification of fraudulent applicants increased confidence in approvals, helping the organization reduce unnecessary interventions and delays that could negatively affect qualified applicants.

• The SVP of acquisition fraud strategy at a consumer financial services organization said stronger fraud intelligence helped focus operational resources on higher-risk applicants rather than broadly reviewing larger portions of the applicant population. This allowed the organization to streamline onboarding activities for legitimate customers while supporting fraud detection and customer experience objectives.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization processes 525,000 applications annually.

• Approximately 0.3% of legitimate applications are unnecessarily declined due to fraud screening activities.

• Improved fraud intelligence enables the organization to approve a greater share of legitimate applicants while maintaining fraud controls.

• The modeled benefit reflects approximately 1,575 legitimate applicants annually who would otherwise be unnecessarily declined but are ultimately approved and funded.

• Each incremental funded account contributes approximately $1,500 in first-year revenue.

• Forrester applies a 10% profit margin, resulting in approximately $150 of incremental profit per funded account.

• The modeled benefit reflects incremental profit generated through improved legitimate-applicant conversion resulting from more accurate fraud decision-making during account origination.

Risks. The scale of this benefit may vary by organization based on:

• Application volume and customer acquisition scale.

• Existing approval rates and underwriting practices.

• The proportion of legitimate applicants unnecessarily declined due to fraud screening.

• Customer conversion and funded account rates.

• Revenue contribution and profit margins associated with newly funded accounts.

• An organization’s ability to incorporate fraud intelligence into approval and onboarding workflows.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $499,000.

Unquantified Benefits

Benefits that provide value for the composite organization but are not quantified for this study include:

• Improved compliance with identity verification requirements. Interviewees said synthetic fraud detection was important in supporting customer identification, KYC, CIP, and broader fraud risk management practices. The SVP of acquisition fraud strategy at a consumer financial services organization explained that synthetic fraud detection supported the organization’s ability to meet customer identification and regulatory requirements while maintaining effective fraud controls. Interviewees described the solution as an additional layer of intelligence that helped validate applicant identities and support regulatory expectations during account origination. For the composite organization, these capabilities can strengthen identity verification processes and increase confidence in customer onboarding decisions.

• Improved fraud governance and auditability. Interviewees said the solution provided a structured approach to identify, classify, and review potential synthetic fraud cases. The credit manager at an equipment finance organization explained that before implementation, the organization lacked a consistent way to identify transactions associated with potential synthetic fraud risk. The interviewee noted that synthetic fraud scoring created a framework for deeper review, investigation, documentation, and ongoing analysis of higher-risk applications. For the composite organization, improved auditability can support stronger governance, oversight, and continuous improvement of fraud management processes.

• Increased organizational awareness of synthetic fraud risks. Interviewees said the solution helped improve their organizations’ understanding of synthetic fraud techniques and evolving fraud patterns. The head of credit functions at an automotive financial services organization said that before implementing Synthetic Fraud Solution, the organization had limited awareness of synthetic fraud and the ways fraudsters established and cultivated synthetic identities over time. Interviewees also reported that the insights provided by the solution improved training, strengthened underwriting awareness, and contributed to a more informed approach to fraud risk management. For the composite organization, increased awareness can help teams identify emerging fraud threats and make more informed risk decisions.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Synthetic Fraud Solution and later realize additional uses and business opportunities, including:

• Expanding synthetic fraud capabilities to additional products, channels, and markets. Interviewees described synthetic fraud as an evolving threat that affects multiple stages of customer acquisition and lending operations. As organizations continue to expand digital account opening, lending, and customer onboarding activities, the composite organization may extend synthetic fraud detection capabilities to additional products, business units, channels, or geographies. This expansion may enable the organization to identify and mitigate synthetic fraud risks across a broader portion of the business while supporting growth initiatives and digital transformation efforts.

• Adopting additional fraud and identity solutions. Interviewees discussed synthetic fraud detection as one component of a broader fraud risk management strategy. As fraud tactics evolve, the composite organization may choose to incorporate additional TransUnion fraud, identity, and risk solutions alongside its existing investment. Building on the current deployment may accelerate future fraud modernization initiatives and help the organization address emerging fraud threats, new customer verification requirements, and changing business priorities.

• Leveraging fraud intelligence to support future growth strategies. Interviewees emphasized the importance of balancing fraud detection and customer experience objectives. As the composite organization pursues future growth initiatives, enters new markets, or expands customer acquisition programs, it may leverage synthetic fraud intelligence to support more aggressive growth objectives while maintaining appropriate fraud controls. This could enable the organization to evaluate new opportunities that might otherwise be viewed as carrying unacceptable risk.

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).