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In some instances, the ways in which lenders make decisions is not unlike looking through a keyhole. From this vantage point, there is some information, but it isn’t the whole picture. Lenders are often making decisions on a subset of information about a consumer. And for millions of people in America, including those who are thin-file or credit-invisible, the amount of information available is limited.   Our goal is to help our clients gain a more representative understanding of consumers to better inform their decisions and ultimately better serve consumers.   Credit reports and traditional credit scores will continue to be an extraordinarily important part of the process, but we’re continually asking ourselves: how we can leverage our unique vantage point to help our clients obtain a more complete picture to create new opportunities for consumers?   One proven way we can achieve this is by helping financial services companies more easily leverage consumers’ banking and transaction information through open banking. This information can advance financial inclusion by providing a more comprehensive and accurate view of a consumer while giving consumers greater control of their data. And our research shows most consumers are onboard, with 71% stating they’re willing to provide this information if it increases their likelihood of qualifying for credit.1 Introducing Cashflow Score  Today, we’ve unlocked an exciting milestone in making the use of this information more accessible with the launch of Cashflow Score. This is the latest in a short list of products that can be used to make lending decisions, leveraging consumer-permissioned transaction information to more accurately assess risk, particularly for credit invisible or un-scorable consumers who have a bank account.  The score provides lenders with a clearer view of an applicant’s financial behavior, including income, expenses, cash reserves, and more, to enhance risk assessments with up to 25% lift in predictive performance.2 Our solution can easily integrate into lenders’ existing workflows in conjunction with traditional credit scores for credit decisions.   This means lenders can now leverage Cashflow Score in first and second chance credit decisions to assess applicants with limited or nonexistent credit histories, using only bank account data.    The future of financial inclusion  Open banking and transaction data can help create a future where we can help bring financial power to all.    As the only financial services company offering both traditional scores and cashflow-based scoring solutions developed in-house, we are uniquely positioned to connect credit outcomes with transaction data. And we’re just scratching the surface with solutions like Cashflow Score.   As we continue to innovate and embrace the possibilities of open banking, I am optimistic about what lies ahead and how we can expand the keyhole even further.   Together, we can create a more inclusive financial system where everyone has the opportunity to thrive.  [1]  Experian commissioned Atomik Research to conduct an online survey of 2,005 adults throughout the United States. The makeup of the sample is representative of the U.S. population based on national census data regarding demographic variables such as gender, age and geographical regions. The margin of error for the overall sample is +/- 2 percentage points with a confidence level of 95 percent. Fieldwork took place between March 17 and March 21, 2024. [2]  Based on Experian analysis when Cashflow Score is compared to conventional credit scores, tailored to targeted risk tiers. Predictability based on KS. Related Posts Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Manual employment and income verification remain a persistent challenge in today’s digital-first financial ecosystem. Despite advances in technology, many organizations still rely on processes that are slow, fragmented, and vulnerable to fraud. These inefficiencies not only strain operational resources but also create friction for consumers seeking timely financial decisions.  Why Manual Income and Employment Verification Falls Short  Traditional income and employment verification methods often involve back-and-forth communication with employer HR departments, unclear documentation requirements, and delays that can stretch from hours to days. Beyond inconvenience, these processes introduce risks such as:  Inaccurate or incomplete data  Exposure to fraud through forged documents  Coverage gaps for gig workers and the self-employed  Operational inefficiency that diverts attention from higher-value tasks  As the workforce evolves—particularly with the rise of the gig economy—these shortcomings become even more pronounced.  Emerging Solutions: From Consumer Permission Data (CPD) to AI  The industry is responding with innovations that prioritize speed, security, and inclusivity:  Consumer-Permissioned Data (CPD): This approach allows individuals to securely share payroll data directly from their provider, reducing manual follow-ups and improving trust through consent-driven access.  Secure Document Upload: For workers without digital payroll systems, document upload offers a practical alternative. Pay stubs, W-2s, and 1099s can be submitted through secure portals, enabling verification for freelancers and small business owners.  AI-Enhanced Verification: Artificial intelligence adds a critical layer of protection and efficiency. Automated scanning detects anomalies, while fraud indicators such as tampered entries are flagged in real time—accelerating review and strengthening accuracy.  Why This Matters  The gig economy is projected to reach $2.145 trillion by 2033, underscoring the need for verification models that accommodate diverse income streams. By integrating CPD, document upload, and AI document verification, organizations can move beyond the limitations of manual employment verification toward systems that are:  Faster and more scalable  Resilient against fraud  Inclusive of non-traditional employment types  Looking Ahead  Manual income and employment verification may still have a role for businesses using niche payroll platforms, but the trajectory is clear: the future of employment and income verification is intelligent, consumer-driven, and built to scale. For lenders and verification providers, embracing these tools isn’t just about efficiency—it’s about setting a new standard for transparency and trust.

Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

Experian Health is very pleased to announce that we’ve ranked #1 in the 2025 Best in KLAS: Software & Services report, for our Contract Manager and Contract Analysis product, for the third consecutive year. Contract Manager, when paired with Contract Analysis, empowers healthcare providers by ensuring payers comply with contract terms, identifying and recovering underpayments, and arming them with real claims data to negotiate contracts. This enables providers to negotiate more favorable terms and maintain financial stability.  Clarissa Riggins, Chief Product Officer at Experian Health, says, “In the ever-evolving healthcare landscape, our Contract Manager solution has once again been recognized as the #1 Revenue Cycle Management tool by KLAS for the third consecutive year. This prestigious ranking underscores the significant value our solution delivers to our clients by identifying underpayments and facilitating revenue recovery. We are honored to continue supporting our clients with innovative solutions that drive financial success and operational efficiency.”  Learn more about how Contract Manager and Contract Analysis can help your healthcare organization validate reimbursement accuracy, recover underpayments and boost revenue.   Learn more Contact us

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