Algorithmic Pricing, a New Compliance Risk: A Discussion with Bart Daniel, Partner at Nelson Mullins

Episode Date: August 21, 2024

In this episode of Stark Integrity, Bart Daniel, Partner at Nelson Mullins, joins Bob Wade (Captain Integrity) to explore an emerging and increasingly important compliance issue:

Algorithmic pricing.

As organizations rely more on data, analytics, and automated tools to make pricing decisions, this episode highlights a key reality:

Technology may change how decisions are made—but it does not change the legal risk.

What Is Algorithmic Pricing?

Algorithmic pricing refers to the use of:

• Software
• Data analytics
• Artificial intelligence or machine learning

To determine or recommend pricing for services or products.

These systems may:

• Analyze large datasets
• Identify pricing trends
• Adjust prices dynamically

While these tools provide efficiency and insight, they also introduce:

New layers of compliance risk.

Why Algorithmic Pricing Matters

A key message from this episode is:

Organizations should be cautious—algorithmic pricing is under increasing scrutiny.

Regulators are focused on how these tools:

• Influence pricing decisions
• Interact with market data
• Potentially reduce competition

Because ultimately:

Delegating pricing decisions to an algorithm does not eliminate responsibility.

The Core Risk: Coordination and Collusion

One of the biggest concerns with algorithmic pricing is the potential for:

Unintentional coordination.

Algorithms can:

• Use similar datasets
• Follow similar patterns
• React to competitors’ pricing in real time

This can result in:

• Parallel pricing behavior
• Reduced competition
• Outcomes that resemble price-fixing

Even without direct communication:

Algorithms can create alignment that regulators may view as anti-competitive.

Regulators have specifically identified risks such as:

• “Hub-and-spoke” coordination through a common algorithm provider
• Information sharing through pricing tools
• Autonomous alignment of pricing through machine learning

The Role of Data Sharing

Another major issue involves:

Access to and use of data.

Algorithmic pricing tools often rely on:

• Market data
• Benchmarking information
• Aggregated pricing inputs

But when that data includes:

• Competitor-sensitive information
• Non-public data

The risk increases significantly.

Because:

Sharing pricing information—directly or indirectly—can create liability.

The “Black Box” Problem

A unique challenge with algorithmic pricing is:

Lack of transparency.

Organizations may:

• Use third-party tools
• Rely on automated outputs
• Not fully understand how prices are generated

This creates a “black box” problem:

• Decisions are made
• But the underlying logic is unclear

From a compliance standpoint:

You are still responsible for outcomes—even if you do not fully understand the process.

Private Equity and Financial Pressure

The episode also highlights the role of:

Private equity and financial performance pressure.

Increased focus on:

• Revenue optimization
• Margin improvement

Can lead organizations to:

• Adopt aggressive pricing tools
• Push the limits of automation

This creates a heightened risk environment where:

Technology + financial pressure = compliance exposure.

Reimbursement and Rate Scrutiny

In the healthcare context, algorithmic pricing also intersects with:

• Reimbursement methodologies
• Out-of-network pricing
• Benchmark rate calculations

This creates additional complexity, particularly when:

• Algorithms influence reimbursement levels
• Consistent patterns emerge across payors or providers

The takeaway:

Reimbursement rates driven by algorithms may attract regulatory attention.

Enforcement Is Evolving

One of the most important themes in this discussion is:

Enforcement is catching up to technology.

Regulators, including the DOJ, have made clear:

• Algorithmic pricing can fall under existing antitrust laws
• Use of a common pricing algorithm may be viewed as coordinated conduct

Recent cases show that:

• Authorities are actively investigating pricing algorithms
• Liability can arise even without explicit agreements
• Traditional legal principles still apply

In short:

“AI is not a defense.”

Practical Compliance Considerations

Organizations using algorithmic pricing should:

• Understand how pricing tools work
• Evaluate data inputs and sources
• Avoid sharing sensitive competitor data
• Monitor outputs for unusual patterns
• Document decision-making processes

Because ultimately:

You must be able to explain and defend your pricing decisions.

From Expense to Revenue—but at What Cost?

The episode also raises a practical business question:

Can pricing tools turn expense areas into revenue opportunities?

The answer may be yes—but with an important caveat:

• Increased revenue strategies must remain compliant
• Aggressive pricing can create regulatory exposure
• Short-term gains can lead to long-term risk

The key takeaway:

Efficiency should not come at the expense of compliance.

Key Takeaways

• Algorithmic pricing is an emerging and growing compliance risk
• Organizations remain responsible for pricing decisions—even when automated
• Algorithms can create unintended coordination or collusion risks
• Data inputs and sharing practices are critical risk factors
• Lack of transparency (“black box”) increases compliance challenges
• Regulators are actively focusing on algorithmic pricing practices
• Traditional antitrust principles apply to modern technology

Final Thoughts

This episode highlights a critical shift in compliance risk:

Technology is changing how decisions are made—but not how they are judged.

Algorithmic pricing offers:

• Efficiency
• Scale
• Insight

But it also introduces:

• Complexity
• Opacity
• Regulatory risk

Organizations must recognize that:

Automation does not eliminate accountability—it amplifies it.

Those that proactively manage these risks will be better positioned to:

• Leverage technology responsibly
• Avoid enforcement exposure
• Maintain trust and integrity

Ultimately:

The future of compliance will depend not just on what decisions are made—but how those decisions are made.

Because in today’s environment:

If your algorithm cannot be explained, it cannot be defended.

Click here to listen to this Stark Integrity Podcast Episode:
https://podcasts.apple.com/us/podcast/algorithmic-pricing-a-new-compliance-risk/id1588939373?i=1000666059127&l=fr-FR