At A Glance<\/h2>\n
Artificial intelligence (AI) can support healthcare claims processing by predicting claims at risk of denial, prioritizing follow-up and reducing repetitive manual work. The strongest denial-prevention strategy starts before a claim is created, with accurate patient and insurance data at registration.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n<\/div>\n Key takeaways:<\/em><\/strong><\/p>\n Could the era of manual claims processing be coming to an end? According to Experian Health\u2019s State of Claims 2025 survey<\/a><\/strong>, 82% of respondents say reducing denials is a priority for their organization. Automation can take on repeatable claims tasks, while artificial intelligence (AI) can add predictive capabilities that help teams identify denial risk and prioritize follow-up.<\/p>\n This article explains how AI can complement automated claims processing and how Experian Health solutions can help providers identify denial risk, prioritize work and address errors earlier in the revenue cycle.<\/p>\n Automation can help healthcare organizations reduce manual work, improve consistency and keep claims moving through the revenue cycle more efficiently. By taking on repetitive, rules-based tasks, automated tools can support staff with activities such as reviewing claim data, identifying errors, routing work and surfacing information that may need attention before or after submission. <\/p>\n Experian Health\u2019s ClaimSource\u00ae<\/a><\/strong> is a scalable claims management system that automates claims workflows across the claim life cycle. It helps organizations prioritize claims, payments and denials so staff can focus on higher-impact accounts, while customizable payer and provider edits, work queues and direct payer connections support cleaner claim submission and more efficient follow-up<\/p>\n The potential impact extends beyond individual claims. A recent CAQH index report<\/a><\/strong> states that replacing manual processes with intelligent technologies, like AI and automation, could save the healthcare industry at least $20 billion.<\/p>\n Claim denials remain a persistent challenge for revenue cycle leaders. AI can add predictive analysis to automated workflows by using historical claims and payer data to identify patterns and risk. Experian Health\u2019s State of Claims 2025 survey<\/a><\/strong> found that 59% of respondents plan to invest in denial reduction technology within six months. Combining automation and AI can give teams another way to strengthen claims processing.<\/p>\n AI can analyze claims and denial patterns to flag potential issues before submission and suggest where staff may need to intervene. When used with automation, these insights can help healthcare providers focus on claims that need review and reduce repetitive work.<\/p>\n Claims processing does not start at submission. Inaccurate or incomplete information captured during patient registration can create problems downstream. Experian Health\u2019s State of Claims 2025 survey<\/a><\/strong> found that 32% of respondents say inaccurate or incomplete patient data at intake drives denials.<\/p>\n At the front end, tools like Patient Access Curator<\/a><\/strong> use AI to validate demographics, eligibility, coordination of benefits (COB), Medicare Beneficiary Identifier (MBI) information and insurance discovery \u2014 all a single workflow. It can correct data in real time and sequence payers before the claim is created. This complements claims-management solutions, like ClaimSource<\/a><\/strong> and AI Advantage\u2122<\/a><\/strong>, which automate key steps in the claims lifecycle to help ensure that claims are clean before they ever reach the payer.<\/p>\n Automation can relieve staff of manual data handling activities, increasing the speed and accuracy of claim processing, from patient intake through scrubbing, submission and adjudication. AI enables staff to perform remaining tasks with greater confidence and accuracy. They no longer need to wonder, \u201cwhich claim should I rework first?\u201d \u2013 AI has the answer.<\/p>\n Teams may be tempted to rework the highest-value denials first, but claim value alone does not indicate whether rework will lead to reimbursement. AI can help staff prioritize by analyzing historical payment data and payer behavior to identify denials with a higher likelihood of reimbursement if reworked.<\/p>\n This is exactly how AI Advantage<\/a><\/strong> \u2013 Predictive Denials works. Experian Health\u2019s AI-based solution uses a provider\u2019s historical claims data and Experian\u2019s knowledge of payer rules to identify claims with a high likelihood of denial. If the solution determines that a claim exceeds the provider\u2019s configured risk threshold, it alerts staff so they can act before the claim is submitted.<\/p>\n Rules-based automation performs predefined, repeatable tasks. AI can analyze historical patterns and update predictions as new data becomes available, allowing claims or denials to be assessed and routed based on predicted risk or recovery potential.<\/p>\n AI Advantage<\/a><\/strong> \u2013 Denial Triage uses advanced algorithms to identify and intelligently segment denials so that providers can prioritize accordingly. Just as Predictive Denials uses historical payment data to predict the claims that may be at risk of rejection, Denial Triage learns from appeal success trends and segments denials by potential value, so teams can focus on remits with the most impact.<\/p>\n AI in claims management<\/a><\/strong> is designed to complement healthcare staff, not replace them. For organizations facing ongoing staffing shortages, that distinction matters. Revenue cycle teams still need people to review complex cases, interpret payer responses, resolve exceptions and make decisions that require human judgment and context.<\/p>\n AI and automation can help by taking on more of the repetitive, time-consuming work that can limit staff capacity. These tools can support tasks such as identifying potential issues, prioritizing claims for follow-up and surfacing patterns that may contribute to denials. That gives existing teams more time to focus on work where human expertise has the greatest value.<\/p>\n The State of Claims 2025 survey<\/a><\/strong> found that 90% of claim denials are still reworked with at least some human review before resubmission. AI in claims processing<\/a><\/strong> can help staff work more efficiently, reduce avoidable administrative burden and devote more attention to complex work that benefits from experience, judgment and human oversight.<\/p>\n <\/p>\n AI in claims processing uses data and machine learning to help healthcare organizations identify patterns, predict potential claim issues and prioritize work.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n Automation is well-suited to repeatable, rules-based tasks, while AI can use historical data and patterns to support predictions and prioritization. Together, they can reduce manual effort and complement staff expertise by helping teams identify claims that may require additional review or action.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n Patient Access Curator<\/a><\/strong> works at the front end of the revenue cycle, before a claim is created. It validates and corrects information across demographics, eligibility, coordination of benefits, Medicare Beneficiary Identifier and insurance discovery, then sequences payers so organizations can start the claims process with cleaner, more accurate data.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n <\/p>\n Learn more about how Experian Health can help organizations use AI and automation across the revenue cycle with Patient Access Curator<\/a><\/strong><\/em>, ClaimSource<\/a><\/strong> and AI Advantage<\/a><\/strong>.<\/em><\/p>\n Complete the form below to be connected with a member of our Sales team. For Customer Support, including password resets, please visit our Support page<\/a>.<\/p>\n![\"using-ai-in-claims-processing-for-healthcare\"](\"https:\/\/www.experian.com\/blogs\/healthcare\/wp-content\/uploads\/2023\/07\/using-ai-in-claims-processing-for-healthcare.png\")
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How automation helps with claims processing<\/h2>\n
The untapped potential of AI in claims processing and denials<\/h2>\n
How can AI help prevent denials before a claim is created?<\/h2>\n
More efficient and accurate claims predictions<\/h2>\n
\u201cLearning\u201d from denials data to drive financial performance<\/h2>\n
How does using AI benefit healthcare staff?<\/h2>\n
FAQs<\/h2>\n
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