Leveraging artificial intelligence for claims management

by Experian Health 6 min read April 18, 2023

Leveraging artificial intelligence for claims management

Healthcare claims management is getting a much-needed infusion of technology. Artificial intelligence (AI) is the key player, utilizing vast amounts of data related to human behavior and health to forecast patterns in disease outcomes with greater precision than ever before. The same analytical power can be applied to claims data to predict and prevent denials. Using artificial intelligence for claims management is now more crucial than ever.

By rooting out errors, evaluating trends and predicting payer behavior, AI helps reduce the likelihood of denied claims and maximize revenue opportunities. Staff can spend less time “treating” the effects of denied claims. But even when denials occur, AI still plays a role, quickly triaging high-value denials so staff uses their time efficiently. This two-pronged, proactive and reactive approach is captured in Experian Health’s AI Advantage solution™. Using AI-powered analytics and automation, this technology helps providers predict, prevent and process denials to improve claims management and increase revenue.

It’s time to update claims management systems

In Experian Health’s State of Claims survey, nearly 3 out of 4 healthcare executives said reducing denials was their top priority. Denials are increasing in number, taking longer to process and taking a bigger bite out of provider profits. Traditional claims management strategies are no longer fit for purpose. The volume and complexity are too much for manual processes to handle, resulting in errors, time-consuming rework and lost revenue.

Many providers are using automated claims management platforms to code and edit claims before they are submitted. Automation is ideal for these highly repetitive processes. Faster and more efficient claims processing increases clean claim rates and speeds up reimbursement. Experian Health’s automated claims management solutions are designed with these outcomes in mind, with ClaimSource® and Contract Manager named among the best-performing claims management products in 2023, according to a KLAS report.

Artificial intelligence builds on the benefits of automation, providing insights and recommendations to drive better decision-making. While automation frees staff from time-consuming, process-driven tasks, artificial intelligence allows them to perform remaining tasks at a higher level. For example, when it comes to processing denials, staff will often “guesstimate” each claim’s potential for payment. They’ll usually focus on reworking the highest-value denials first. AI removes the guesswork so staff can prioritize denials based on monetary value and likelihood of reimbursement, so time isn’t wasted chasing higher payments that may never materialize.

Using artificial intelligence for claims management can predict and prevent denials

A successful denial reduction strategy starts upstream, to proactively prevent denials before they occur. AI Advantage ­– Predictive Denials uses AI to review claims before they’re submitted and flag any that are likely to be denied, based on historical payment data and payer adjudication rules. The tool detects changes to the way payers handle denials, even if those aren’t explicitly documented. If a claim exceeds the (customizable) threshold for probability of denial, Predictive Denials alerts the appropriate biller, who can then intervene and make corrections prior to claim submission.

The benefits of this “early detection” approach include:

  • Reducing the number of denials to be processed (and staff time spent processing them)
  • Reducing AR days by flagging high-risk claims
  • Improving patient satisfaction by avoiding lengthy appeals processes.

After using AI Advantage – Predictive Denials for six months, Schneck Medical Center reduced average monthly denials by 4.6%. Reworking claims flagged with a predictive alert took 3–5 minutes, which was significantly quicker than before. By frontloading staff time to get claims right the first time, less effort was spent on denials. Implementation was straightforward, with no disruption to the existing claims workflow.

Triaging denials for faster, more effective rework

The second piece of the AI Advantage solution addresses denials that haven’t been prevented. AI Advantage – Denial Triage uses advanced algorithms to identify and segment denials so staff can focus on the most profitable resubmissions. Denials are automatically triaged into five customizable categories based on likelihood of approval. Staff can rework the claims in their work queue without wondering if they’re putting their effort in the right place.

By automating decisions about which claims to prioritize for rework in real time, Denials Triage eliminates time spent on low-value denials and increases revenue by prioritizing high-value claims. As with Predictive Denials, this reduces the administrative burden on staff, expedites AR days, and increases patient satisfaction by reducing time to decision.

Extending the automation advantage

To maximize reimbursements, providers need to look at opportunities to leverage automation and artificial intelligence across the entire claims ecosystem. AI Advantage integrates with existing systems and workflows to leverage the impact of tools such as ClaimSource®. ClaimSource manages the whole claims cycle from a single online application. AI Advantage uses real-time insights generated by ClaimSource to detect patterns and predict future payer behavior.

Other ways to use automation to improve claims management include:

  • Automated claim scrubbing – Claim Scrubber uses machine learning to assess which claims have been denied in the past and why. Claims can be tagged for extra checks before being prepared for processing, to ensure likely errors have been avoided. This helps eliminate undercharges, reduce errors and minimize rework.
  • Enhanced claim status monitoring – This helps providers keep track of existing claims. Automated status requests based on each payer’s adjudication timeframe reduce manual follow-up work and allow staff to respond promptly to issues. Gathering insights into potential problems before the electronic remittance advice and explanation of benefits are processed creates time to make corrections.
  • Using a denials workflow manager – This system automates and optimizes the denial management portion of the claims cycle, so staff can improve productivity and speed up reimbursement.

With a single vendor, these tools and systems are designed to work cohesively, so there are no issues with interoperability. Data is reliable, accessible and integrated, so automation can pull from the most up-to-date and complete sources. This data can feed into proprietary machine-learning algorithms to predict and shape future performance. Experian Health’s suite of automated claims management software solutions also comes with support from experienced claims-specific experts, who can help staff optimize their set-up and workflows.

With the rise of AI, the healthcare industry is turning towards a more proactive approach to claim denials. Leveraging artificial intelligence for claims management can improve the overall efficiency and accuracy of healthcare claims processing, leading to fewer denials and a more seamless patient experience. Instead of waiting for denials to occur before taking remedial action, providers can use AI and automation to proactively detect errors and diagnose weaknesses in the claims process for a healthier revenue cycle.

Discover how AI Advantage can help healthcare organizations predict and prevent claim denials.

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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.

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