For health plans with mature payment integrity programs, the question is usually not whether existing strategies are delivering value. Experienced teams, established workflows, proven rules and analytics, and specialized technology partners may already be identifying savings and improving payment accuracy.
A more revealing question is: What are you not finding?
Even sophisticated strategies can develop blind spots. Claims patterns change. Reimbursement policies evolve. Provider billing behavior shifts, and new risks and opportunities do not always fit neatly within the rules and concepts an organization is already monitoring. That makes adaptive, continuous discovery even more important. Additionally, that approach should be a proactive one – able to surface new payment integrity concepts and resolve underlying issues. The goal is not to replace the strategies a health plan has built, but to add AI-powered intelligence to uncover hidden blindspots, enabling you to quickly fine-tune your program and improve financial outcomes over time.
Strong Programs Still Need New Ways to Look at Their Data
Traditional healthcare payment integrity controls remain essential. Established rules, audits, and analytics are often designed to identify known issues: a coding conflict, policy violation, billing pattern, or previously identified area of risk.
Predictive analytics enhanced with artificial intelligence add another perspective. Rather than testing claims only against predefined conditions, AI can analyze large volumes of data to identify unexpected patterns, relationships, and anomalies that may warrant further review. It can help health plans evaluate not only, “Did this known issue occur?” but also, “What else is happening in our claims data that we haven’t thought to look for?” That analysis paired with proactive changes can not only help programs realize greater overpayment recovery, but better claims accuracy, less provider abrasion, and overall more proactive cost containment.
This capability is gaining ground. A 2026 U.S. Government Accountability Office report highlighted AI’s ability to analyze large volumes of data for anomalous patterns that may signal fraud or improper payments, augmenting more established detection approaches. For health plans, that same analytical advantage can extend across payment integrity, helping uncover opportunities that existing controls were never specifically designed to find.
Codoxo has seen this complementary approach benefit health plans that already use multiple methods to identify risk. In one national health plan case study, the payer compared AI-generated findings with leads from its internal efforts and other sources. Codoxo’s artificial intelligence identified approximately 97% of the exposure represented by those existing leads while also surfacing additional high-potential opportunities.
Even in a mature program, a different analytical lens can uncover opportunities that were previously out of view.
Moving Beyond Error Detection to Proactive Intelligence
Finding an overlooked opportunity with AI-powered payment integrity is valuable, but understanding why it happened and how to keep it from happening again is even more powerful.
When advanced analytics uncover a new issue, that finding can become a starting point for improving the broader strategy — for fraud detection or ensuring more accurate payments. Root-cause analysis may reveal a reimbursement policy gap, an opportunity to refine an existing rule, a weakness in an operational process, or a payment error due to billing patterns that warrant continued monitoring. The insight can then inform what happens next. Instead of repeatedly identifying and recovering the same type of payment error, health plans can use what they learn to strengthen controls and, where possible, intervene earlier.
Turning AI-driven insights into measurable action that improves payment accuracy is a critical next step as healthcare organizations move from AI experimentation toward accountable adoption. Deloitte’s 2026 survey of healthcare finance leaders found that 44% of surveyed organizations were scaling AI more broadly, yet only 18% of those “AI scalers” reported mature capabilities for financially attributing AI’s impact. The broader message is clear: adoption now requires demonstrable value.
Payment integrity offers a tangible opportunity to deliver it. As AI and generative AI enables continuous monitoring and faster feedback loops across the payment lifecycle, each new finding goes beyond identifying savings existing controls did not catch to generating intelligence that improves program operations going forward. This creates a more proactive model in which detection informs prevention.
What Happened When One Mature Health Plan Looked Again?
One leading national health plan recently put that premise to the test. Its mature payment integrity program already consistently identified and recovered overpayments. But leadership still wondered: Were additional savings opportunities going undetected?
The health plan engaged Codoxo to apply AI-powered data mining to multiple years of historical claims. The analysis covered 37 million claims, 108 million claim lines and more than $20 billion in paid claims. It examined 23 payment integrity concepts using AI-driven analysis, cross-data analysis, AI-generated fraud leads and traditional rules-based validation.
The financial results were significant. Within 60 days, the analysis identified more than $60 million in exposure, with more than $30 million in opportunities validated for additional review and potential recovery.
But the analysis also revealed where the existing claims processing approach could become stronger. Codoxo identified foundational issues that existing processes had missed, previously unidentified AI-driven anomalies, reimbursement policy enhancement opportunities and additional concepts for future evaluation. Root-cause analysis helped uncover adjudication leakage, policy gaps, operational breakdowns and system configuration issues that could be addressed to help prevent future overpayments.
Can AI-Powered Data Mining Find You New Savings?
Tools like AI-powered Data Mining Services can uncover additional overpayments and flag other fraud, waste, and abuse errors. Your program may be working, the next opportunity could be discovering what else it can find.
Read the full case study for a deeper look at how the health plan approached the analysis, what it uncovered and what other payment integrity leaders can learn from the experience.