Data Quality Scanner Frequently Asked Questions

Data Quality Scanner (DQS) is the end-to-end accuracy solution for credit reporting and disputes from Bridgeforce Data Solutions. Its product family connects Metro 2® furnishing data-quality review, credit bureau dispute-response oversight, and investigation support. Credit reporting, servicing, disputes, and quality assurance teams use DQS to identify discrepancies, prioritize review, and investigate recurring issues across the supplied account data. The family includes the Furnishing Module, Disputes Module, and AI Resolution Engine, which is in pilot. The AI Research Assistant is an Optional Premium Add-on to the Furnishing Module. Teams retain responsibility for validating findings, directing corrections, and approving dispute responses.

• DQS walkthrough

The Furnishing Module reviews Metro 2® furnishing data for discrepancies. The Disputes Module reviews disputed-account data and analyst responses for issues that remain unresolved or are introduced during response preparation. Used together, they help teams connect furnishing history with dispute activity and investigate possible upstream causes. The modules can also be used independently; the information available for review depends on the data supplied and the selected configuration.

Credit reporting, servicing, disputes, quality assurance, compliance, risk, and vendor-oversight teams use DQS to investigate reporting discrepancies and response-quality gaps. It helps answer practical questions: which accounts are affected, which patterns recur, what needs review first, and where corrections or coaching may be needed. Managers can use that visibility to support oversight and documented action across teams and portfolios.

• DQS by industry
• Disputes Module

The Furnishing Module reviews the Metro 2® account data supplied by an organization for credit reporting quality and consistency. Its rules help identify discrepancies in reported fields and relationships between fields, including patterns across reporting periods where history is available. Metro 2 is the industry reporting format; the Credit Reporting Resource Guide® provides reporting guidance. The scope of a DQS review depends on the files and history supplied.

DQS supports review of 100% of the furnished account records supplied for analysis, rather than limiting its rules-based review to a sample. Full-population coverage helps teams see concentrations of discrepancies across the submitted data. Coverage describes what is reviewed: it does not mean every record is accurate, every possible issue will be detected, or a file contains every account in the organization.

• Furnishing Module

The current DQS furnishing baseline includes 400+ risk-ranked rules and alerts. These checks help teams identify potential data-quality issues and prioritize investigation. The ruleset is maintained as reporting guidance, client feedback, and product research evolve. Older release announcements may contain counts that were accurate when published; the current Furnishing Module page is the reference for present product positioning.

• 2026 CRRG updates for data furnishers

A DQS alert identifies a condition that needs review. Teams use the applicable rule, reported fields, account history, and supporting records to determine whether a correction is needed. Some findings may require additional context or investigation. Risk ranking helps prioritize that work, while the organization remains responsible for validating findings and deciding what action is appropriate.

• Furnishing Module
• Metro 2 reporting guide

DQS supports discrepancy detection, investigation, and correction prioritization. The organization’s teams remain responsible for validating the findings and directing changes through their own systems and reporting processes. DQS results can help identify affected populations and support root-cause review, but the Furnishing Module should not be understood as automatically rewriting source records or deciding which corrections to submit.

• Large U.S. bank furnishing case study

The Disputes Module evaluates data quality in indirect credit bureau disputes and analyst responses. It helps identify discrepancies present when a dispute arrives, issues left unresolved in the response, and new discrepancies introduced by a response. Review can cover the full supplied disputed-account and response population, giving quality assurance teams broader visibility than manual sampling alone.

• ACDV and AUD data explained

DQS uses supplied Metro 2® furnishing records alongside Automated Credit Dispute Verification (ACDV) data and Automated Universal Dataform (AUD) data to review reporting and dispute-response quality. These records provide different views of account activity and corrections. Bringing the available information together helps teams understand what was reported, what was disputed, and how account information changed. Available comparisons depend on the completeness and history of the supplied records.

• Disputes Module

e-OSCAR supports the exchange of credit bureau dispute and account-update information. DQS adds data-quality analysis and oversight of the supplied furnishing and dispute records. Teams use DQS findings to investigate discrepancies and assess response quality while managing dispute communications through their established channels. This distinction helps organizations evaluate DQS as a quality-control capability within their existing dispute process.

• Disputes Module

DQS helps teams examine recurring data discrepancies, unresolved corrections, and response patterns across the records supplied for review. When furnishing history is also available, teams can investigate whether recurring disputes relate to upstream reporting issues or to how earlier responses addressed them. Those findings can inform correction priorities, training, and further investigation. A recurring dispute still needs review on its own facts.

• Disputes Module
• Complaint handling and furnisher readiness

Yes. The Disputes Module helps organizations review response-quality patterns at agent, team, and vendor levels. Teams can examine where discrepancies were missed, where corrections remained incomplete, and where responses introduced new issues. Managers can use these findings to target coaching and quality reviews across internal and outsourced operations while retaining responsibility for performance assessment and corrective action.

• Agent training and vendor oversight: 2024 announcement

The AI Research Assistant is an Optional Premium Add-on to the Furnishing Module. It adds account lookup and AI-assisted research for organizations that select and enable that capability. It is separate from the core Furnishing Module upgrade. Bridgeforce Data Solutions can confirm the packaging, access requirements, and availability for your environment; enabling the add-on requires the applicable client approvals.

The AI Research Assistant is designed to help furnishing teams explore available DQS results and furnishing records in plain English. It supports account-level research, field-by-field history review, and investigation of discrepancy patterns. For example, a team can use it to examine what changed over time or which reported fields need closer review. Users validate the findings and decide how to proceed.

• AI in credit reporting and dispute handling

The AI Research Assistant supports research using available DQS results, furnishing records, and related reference data within its approved scope. Coverage depends on the data available in the configured environment. Access to a lender’s system of record, unrelated documents, or other external datasets should be confirmed for the specific use case. Research results need to be checked against the relevant records before teams act on them.

• Information security considerations for AI

The AI Resolution Engine is a separate DQS product for AI-assisted credit bureau dispute review. It is in pilot and is designed to help analysts assemble case context, examine supporting evidence, and prepare more consistent documented responses under human control. It is distinct from both the Disputes Module and the AI Research Assistant. Pilot participation and supported scope should be confirmed with Bridgeforce Data Solutions.

• DQS walkthrough

Within its approved pilot scope, the AI Resolution Engine is designed to bring together dispute history, furnishing events, supporting documents and images, client procedures, and DQS findings. It helps analysts identify missing or conflicting information and review cases against approved procedures. The purpose is to give analysts clearer evidence and context for their review, with documented reasoning and human decision authority.

• AI in credit reporting and dispute handling

The organization’s authorized reviewers retain final decision authority. The AI Resolution Engine is in pilot and provides human-controlled decision support: analysts review the evidence, validate findings, and approve the response under their organization’s procedures. AI assistance does not remove the need for case-specific review, appropriate escalation, or accountability for the final response.

• Evaluating trust in AI-assisted dispute review

The AI Research Assistant is an Optional Premium Add-on to the Furnishing Module for account lookup and research into furnishing data and DQS findings. The AI Resolution Engine is a separate product, in pilot, for analyst-facing dispute decision support using case information and approved procedures. The distinction is the task: furnishing research for the Assistant, and supported dispute-case review for the Engine.

DQS is cloud-based and is designed to begin with limited direct integration. Setup focuses on the selected modules, required data files, secure delivery, user access, and the organization’s information-security review. The team confirms what history and account populations are available and how findings will be reviewed. Timing depends on data readiness, approvals, and configuration; the implementation scope should be agreed for your environment.

• Furnishing Module
• Trust, security, and product evaluation

Established delivery options include SFTP and, where applicable and authorized, Equifax routing for Metro 2® furnishing files. Direct delivery can also support organizations using DQS independently of the Equifax routing option. The selected modules determine which files and history are needed. Bridgeforce Data Solutions confirms the delivery arrangement and setup requirements with the organization before analysis begins.

• Furnishing Module

Security teams can use the Bridgeforce Data Solutions Trust Center to review public information and request access to additional documentation. The security page identifies SOC 2 Type 2 and PCI DSS materials and provides a route to supporting information on data protection and operational controls. Your review should use the available reports to confirm their scope and reporting periods for the services being evaluated.

• Trust, security, and product evaluation

DQS pricing depends on the selected modules, data volume, usage, scope, and configuration. The AI Research Assistant is an Optional Premium Add-on to the Furnishing Module, while AI Resolution Engine pilot arrangements are discussed separately. A product-fit conversation helps establish the relevant capabilities and data requirements before Bridgeforce Data Solutions confirms pricing and availability.

• DQS walkthrough
• Discuss DQS product fit with the team

Bridgeforce Data Solutions publishes case studies documenting results from the established DQS Furnishing and Disputes Modules. Two examples are:

• Furnishing accuracy: a large, super-regional U.S. bank reported a 70% reduction in DQS-identified discrepancies during its first year of using DQS. Read the furnishing case study (PDF).
• Dispute-review coverage: a published two-year analysis reported review rates increasing from approximately 10% to 100% through automated DQS review. This describes coverage of the supplied dispute data, not manual investigation of every case or a guarantee of accuracy. Read the disputes analysis (PDF).

These are reported outcomes in the published customer and analysis contexts, not guaranteed results for every organization. They are not measured results for the AI Research Assistant or AI Resolution Engine pilot. Review each source's scope and measurement period when evaluating the findings.

DQS helps teams identify relevant data discrepancies, review available history, and organize findings that can support investigation records and oversight reporting. Teams remain responsible for documenting the evidence they considered, the review they performed, and the action they approved. DQS analysis can contribute to that record; a rules-based check alone does not establish that an investigation was adequate or that all applicable obligations were met.

• Disputes Module
• FCRA investigation standards: further reading

Start with the reporting or dispute problem you need to address, the records available, and the teams responsible for acting on findings. Review the relevant DQS module, explore a walkthrough, and discuss data scope, implementation, security, and success measures with Bridgeforce Data Solutions. For AI-assisted capabilities, also evaluate evidence quality, human review, and performance against the specific task being considered.

• DQS walkthrough
• Evaluating AI for business outcomes

Bridgeforce Data Solutions serves credit unions, banks, fintechs, mortgage lenders and servicers, auto lenders, student lenders and servicers, and collection agencies and debt buyers. The same DQS product family supports these organizations, with the emphasis shaped by their portfolios, data, and operating responsibilities. The industry pages explain the reporting pressures and use cases relevant to each group.

DQS helps credit union teams focus on member-account reporting discrepancies and the quality of dispute responses. Furnishing review can highlight affected member populations, while disputes review helps teams investigate repeated issues and coaching needs. This is particularly useful when lean teams oversee core-system dependencies or assess reporting changes during a merger. The credit union directs investigation and corrective action using its own records and controls.

• DQS for credit unions
• Furnishing Module

DQS helps banks examine reporting and dispute-quality issues across portfolios, business lines, systems, internal teams, and vendors. Furnishing and disputes findings can help managers identify concentrations of risk and decide where further review is needed. That visibility supports consistent quality control and evidence for internal oversight. For a concrete example of customer use, the published large-bank case study describes furnishing results in its own measurement context.

• DQS for banks

DQS helps fintech reporting teams investigate discrepancies as products, platforms, and account populations change. Furnishing review can surface patterns that warrant checking data mappings, reporting logic, or integration changes. Disputes review adds visibility into response quality as dispute volumes develop. Together, these capabilities can support product-launch and partner-bank discussions with clearer evidence about the supplied reporting data and the issues requiring action.

• DQS for fintechs
• Metro 2 reporting guide

DQS supports review of furnishing and dispute data in servicing-heavy mortgage environments. Teams can investigate discrepancies around borrower account history, servicing transfers, loss mitigation, and default-servicing activity using the records available. Disputes review can also inform subservicer oversight and documentation checks. The emphasis is on identifying reporting and response-quality issues so the responsible servicing teams can investigate and direct the appropriate corrections.

• DQS for mortgage lenders and servicers
• Disputes Module

DQS helps auto lenders review portfolio-level reporting patterns associated with servicing events such as extensions, deferrals, repossessions, charge-offs, and recoveries. Teams can examine flagged payment-history and account-status discrepancies, then use dispute findings to assess whether related issues remain unresolved. This supports more focused investigation and correction priorities across the affected account population, with the lender validating findings against its servicing records.

• DQS for auto lenders
• Furnishing Module

DQS supports student lending teams reviewing reporting and dispute quality across accounts with long histories and changing repayment conditions. Relevant review areas include repayment-status changes, deferment, forbearance, and servicing transfers. Furnishing findings help focus investigation on affected accounts, while disputes review helps examine response quality and repeated issues. Available historical data and supporting servicing records determine the context teams can use in their review.

• DQS for student lenders and servicers
• ACDV and AUD data explained

DQS helps collection agencies and debt buyers assess reported account data and dispute-response quality. Teams can use findings to prioritize review of balance and status inconsistencies, account-transfer context, and supporting ownership records. The Disputes Module helps highlight unresolved discrepancies and response-quality patterns, while furnishing review supports accurate reporting. The organization remains responsible for reviewing the underlying documentation and approving any correction or response.

• DQS for collection agencies and debt buyers

The Equifax partnership gives furnishers a route to DQS analysis and supporting credit reporting expertise. With authorization, Equifax can route Metro 2® furnishing data to DQS for analysis. Organizations can also arrange direct file delivery. The partnership supports access and data delivery; DQS use is not limited to the Equifax routing option.

Bridgeforce Data Solutions uses Amazon Web Services (AWS) as part of the cloud infrastructure supporting DQS. The architecture and services relevant to a particular module can be discussed during product and security evaluation. Organizations seeking more detail can use the security review process to understand the environment applicable to their proposed DQS use.

• Trust, security, and product evaluation

Bridgeforce Data Solutions uses Rapid7 tools to support security activities. Organizations evaluating DQS can request more detail about the applicable security practices and supporting documentation through the Bridgeforce Data Solutions Trust Center. That review is the appropriate place to establish the scope of the controls relevant to the services being considered.

• information security and AI

Bridgeforce Data Solutions is a Platinum Sponsor of the Consumer Data Industry Association (CDIA). Its involvement includes participation in industry discussions about credit reporting, disputes, data quality, and AI. The CDIA Connect 2026 recap shares practitioner observations from the event and describes the company’s sponsorship participation.

The Bridgeforce Data Solutions Resource Center brings together educational guides, practitioner analysis, product information, and case studies. Start with the topic closest to your question: Metro 2® for furnishing, the CFPB complaint-process explainer for complaint basics, ACDV/AUD for dispute data, or the AI guide for research and dispute-review support. These guides provide more detail than the short answers on this page.

The Scanner is the Bridgeforce Data Solutions weekly news digest covering consumer lending, compliance, credit risk, credit reporting, and disputes. It gives readers a regular way to follow relevant industry developments and supporting analysis. You can browse the current issue and subscribe through The Scanner page. Data Quality Scanner (DQS) is the product family; The Scanner is the publication.