Lanzko Insights

I’ve often been called in to audit the Third-Party Administrators (TPAs) for companies that outsource their claims handling in property & casualty (P&C) and specialty insurance lines. It’s a task that’s traditionally seen as costly and time-consuming. When you’re auditing, it’s like searching for a needle in a haystack; you’re looking for inconsistencies in the process or dissecting loss runs for anomalies. Seeking a more efficient method, I turned to AI tools that mimic a skilled reviewer. These tools sift through claim notes, flagging keywords that might need further investigation. This has helped narrow our focus to the most pertinent cases, significantly reducing resource expenditure and sharpening the accuracy of our reviews. Furthermore, employing an audit portal has cut down both initial and subsequent review times, managing reviewers, enabling real-time assessments, and assisting in drafting KPI reports from reviewer notes.

The Problem

The crux of the operational issues with TPA audits lies in their expensive and extensive nature, exacerbated by reliance on manual processes. According to the National Association of Insurance Commissioners (NAIC), policyholder concerns about claims handling delays were highlighted in 2024 as a primary issue affecting trust in TPAs. The unpredictable nature of turnaround times, error rates, and service quality are compounded without standardized practices across TPAs. These inconsistencies are particularly impactful in high-volume sectors like auto insurance, where manual audits simply can’t keep pace with the complexity and sheer quantity of claims [Source].

Why It Persists

This problem persists due to an entrenched reliance on outdated processes that lack standardization. Many insurance companies outsource to TPAs to mitigate costs and handle the increasing complexity of claims, but this decision ironically leads to higher operational costs due to inefficiencies in the audit processes. The lack of standardized performance metrics and manual-driven procedures mean that claims handling remains inconsistent and error-prone [Source]. Furthermore, traditional audits struggle to adapt to sophisticated regulatory demands, often lagging behind the speed at which claims need to be processed.

The Enabling Approach

Incorporating AI and automation presents a viable solution to these challenges. My key takeaways are structured as follows:

• Focus on pre and post-review work, as extensive pre-review preparation increases costs, while post-review analysis ensures findings are actionable and problems are caught early.

• Implement AI-driven file selection and automated reviews to streamline the process, significantly cutting down review times and helping surface actionable insights—or finding those proverbial needles in the haystack.

• View AI and automation as augmentation rather than replacements for human auditors, allowing them to focus on complex, high-value assessments while AI handles mundane data trawling [Source].

Practical Example

Consider a mid-sized insurance company dealing with a surge in auto accident claims. Traditionally, sifting through thousands of these claims manually is both labor-intensive and error-prone. By employing an AI-driven toolset, the company can preemptively identify anomaly patterns or inconsistent data entries. For instance, KPMG’s Clara platform has been beneficial in reducing fraudulent activities by up to 45%, safeguarding significant losses through pattern recognition [Source]. This allows the auditing team to focus resources where they’re truly needed—mitigating risk and improving service quality rapidly.

Governance and Risk Note

While AI enhances audit efficacy, it does not exonerate organizations from the responsibility of robust governance. Proper oversight is vital to ensure AI applications uphold the principles of fairness, accountability, and transparency. Insurers must implement stringent AI governance frameworks that include regular audits of the tools themselves, ensuring they adapt accurately to new data and regulatory requirements [Source]. This will mitigate risks such as bias and non-compliance, preserving the integrity of the auditing process.

The Takeaway

Organizations stand to gain immense efficiency and accuracy in TPA audits by integrating AI and automation. This dual approach trims operational fat, highlights significant cases under review, and adjusts human resource focus toward critical assessments. However, sustained implementation success demands dedicated oversight and a responsive governance framework to address potential risks, ensuring that the technology enhances rather than complicates the auditing landscape.