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The Role of AI in Transforming Healthcare Benefits Reconciliation

Here, we’ll explore how AI can revolutionize reconciliation for benefits administration, by streamlining operations, and enhancing accuracy and trust.

Sarah SpeightsBrand Writer

Artificial Intelligence (AI) has numerous applications in financial services, and is already revolutionizing the industry through automation, optimization, and innovation. At Modern Treasury, AI has been top of mind for us as we continue to find ways to responsibly integrate AI into aspects of our platform.

By automating routine tasks and improving the accuracy and efficiency of payment operations, AI offers businesses the opportunity to focus on other aspects of their product. As Brian McKinney, Head of Payments Engineering, put it in our recent webinar, with the help of AI, “you're turning what is today viewed as a cost center into something that makes your business more effective, more efficient, and allows you to be more nimble.”

For financial services, AI is already a revolutionary force in simplifying and streamlining manual processes—in the healthcare and employee benefits industry, however, AI’s potential remains largely untapped.

AI-Enhanced Automatic Reconciliation Can Shift the Industry

Benefits administration involves managing employee benefits like Health Savings Accounts (HSAs) and Flexible Spending Accounts (FSAs), requiring precise and auditable tracking of contributions and expenditures. Reconciliation presents significant challenges due to the complexity of transactions, disparate systems, manual processes, high volume of transactions, and associated operational costs and risks.

Logical Rules To Improve Accuracy

Automation with AI can make it easier for benefits administrators to track balances and manage accounts for their end users. Manual reconciliation is prone to error, which can be detrimental to the efficiency and reliability of financial operations. Automating reconciliation using AI can significantly lower the risk of human error. AI can read and adjust data based on logic and semantic rules, ensuring that data is easily matched and discrepancies are quickly identified and resolved. In reconciliation, AI not only saves time but also increases confidence in accounting processes, ensuring that transactions are accurately settled and balances are up-to-date.

This is imperative in the case of healthcare and benefits. Managing accurate disbursements—for many businesses across multiple geographies—is a particularly complex task that AI is well-suited to managing. Different regions have varying laws and regulations regarding health benefits, taxation, and usage, and compliance with these regulations requires an in-depth understanding of local laws to stay current with any changes. For instance, some benefits might be taxable in one region but tax-exempt in another. Ensuring that benefits are taxed correctly according to local laws is critical to avoid legal issues and penalties.

In terms of reconciliation, this means managing data from multiple sources with varying formats and with differing regulations. Ensuring accuracy and consistency in reconciling this data is crucial for accurate disbursements and is a perfect fit for AI.

As Modern Treasury’s Head of AI, Patrick Harrington, puts it, “Finance is rule-based, similar to language, so a lot of these modern AI models are appropriate for financial applications. This technology is not just a future concept; it's a present tool that can significantly enhance efficiency and accuracy.”

Faster Data Processing for Better Efficiency

Forbes echoes our sentiment on AI reconciliation in the healthcare benefits space, saying it can “identify irregularities that human auditors might miss”—emphasizing its role in workflow automation and managing massive amounts of data. This capability is particularly relevant to benefits administration reconciliation, where the timely and accurate processing of large volumes of transactions is essential. Integrating AI into the reconciliation process can reduce administrative costs, mitigate fraud, and create a more personalized, equitable, and transparent experience for the end user.

As McKinsey & Company points out in a recent piece, there are several key points in the "payment integrity value chain" that are critical for effective benefits management. These include the complex clinical review of claims, the application of accurate algorithms to data sets, and the coordination and disbursement of benefits. Organizing payment data efficiently is a core strength of AI-driven reconciliation, ensuring that payments are processed correctly, efficiently, and swiftly.

In particular, faster data processing in benefits management can improve the accuracy and timeliness of information, which is vital for both administrators and account holders. Let’s look at an example to illustrate this point.

In the case of HSAs, where balances fluctuate based on the accrual and use of funds, AI-based reconciliation of those accounts can provide greater confidence in the accuracy of their account balances. HSAs are fully funded for individual consumers at the start of the year, with additional contributions made with each paycheck.This arrangement creates reconciliation complexities, as funds must be accurately matched while the employee spends from the account particularly for auditing purposes.. When the employee uses funds from their HSA, the account will need to be reconciled and updated to reflect their correct balance.

This is imperative to ensuring that users have access to accurate balances. Accurate balance information allows individuals to plan for medical expenses, allocate funds appropriately, and avoid financial surprises—which is of significant value to health benefits administrators as errors with HSA accounts (or any time of benefit account) can be detrimental. The operational expenses incurred from manual reconciliation errors and the need to cover unaccounted transactions can be costly, plus a poor consumer experience may reduce HSA adoption. Using AI for accurate, real-time reconciliation demonstrates a commitment to accuracy and efficiency, which can strengthen credibility and trustworthiness in the eyes of their customers.

How Modern Treasury Can Help

Modern Treasury is the platform for moving, tracking, and reconciling financial data at scale. Our reconciliation product has been explicitly designed to help teams leverage AI and reconcile 100% of transactions faster with a self-learning system. Wayne Lim, Modern Treasury Product Manager, explains, "Within our product, we've been able to incorporate AI technology effectively in two areas. One is in finding match suggestions when there are exceptions and the second, which we've seen a lot of interest in, is rule suggestion itself. The team can notice patterns based on what they know about the business and patterns can encode rules… Having this AI system that can help monitor for changes in financial data or additional fund flows that lead to new patterns, and then suggest rules for the team to review and approve, that's where we save people time."

As payments continue to get faster and more complex, we know that real-time, item-level, and AI-assisted reconciliation is only going to become more essential for benefits administrators and their end users.

Our team also has extensive experience bridging gaps between legacy systems and modern digital solutions and is already helping achieve greater efficiency, accuracy, and confidence in financial operations for our healthcare customers like Alegeus, PayZen, and Sana. Learn more about how our platform can help your business with reconciliation and more, reach out to us.

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