The Three-Way Transaction Problem: How Torch Dental Solved Healthcare’s Most Complex Payment Flow

Most payment flows involve two parties: buyer and seller. Dental practices navigate three: patient, practice, and insurance company. Each with different incentives, different timelines, and different information.

This three-way transaction creates operational chaos that consumes 30-40% of practice staff time. Khaled Boukadoum, Founder of Torch Dental, built a company processing $5 billion in transactions by automating what everyone else considered unsolvable. In a recent episode of Category Visionaries, he broke down the mechanics of dental’s payment nightmare—and why solving truly complex problems creates unassailable GTM advantages.

The Structural Complexity: Why Dental Is Different

Understanding why dental payments are uniquely difficult requires examining how money actually flows through the system.

In most medical care, providers bill insurance companies directly. The patient might pay a copay, but the primary transaction happens between provider and insurer. Simple two-party flow.

Dental works differently. The patient pays the practice upfront for treatment. The practice then submits a claim to the patient’s insurance company. Weeks or months later, the insurance company reimburses the practice. The practice must then reconcile everything and potentially refund or collect additional amounts from the patient.

“What we realized is that the biggest problem in the space is actually what happens after the clinical visit,” Khaled explains. “So the patient gets the treatment done, and then we need to figure out how much the patient owes, how much the insurance owes, and then how do we actually coordinate the collection of that money.”

This three-way transaction creates cascading operational complexity at every step. Before treatment, practices must verify insurance eligibility and estimate costs. During payment collection, they must determine how much to charge upfront versus bill to insurance. After treatment, they must submit claims with proper documentation, post payments when they arrive, reconcile accounts across three parties, and potentially chase down unpaid balances.

Each step involves uncertainty, manual work, and coordination across disconnected systems. Multiply this across dozens of patients daily, each with different insurance plans and treatment scenarios, and you have operational paralysis.

The Hidden Cost: Why 40% Staff Time Matters

The most striking aspect of dental’s payment complexity isn’t that it exists—it’s the sheer magnitude of resources practices throw at the problem.

Dental practices dedicate 30-40% of their staff purely to administrative tasks related to this three-way transaction flow. Not clinical work. Not patient care. Just the grinding operational machinery of figuring out who owes what and collecting money.

This represents a massive hidden tax on every practice. For a ten-person practice, four people spend their entire day on insurance verification, claims submission, payment posting, and collections. That’s salary, benefits, training, and management overhead—all dedicated to work that generates zero clinical value.

The economic implications are staggering. If you could eliminate even half this administrative burden, practices could either reduce costs dramatically or reallocate staff to revenue-generating activities. This becomes Torch’s value proposition: not making administrative work 20% more efficient, but eliminating entire categories of it.

Why Existing Solutions Failed

When Khaled entered the market, dental practices weren’t lacking software. They had practice management systems from companies like Dentrix and Open Dental. They had billing tools. They had payment processors.

Yet the administrative burden persisted. Understanding why existing solutions failed reveals the product design challenge Torch needed to solve.

“The practice management systems are really systems of record. They’re systems that track your clinical information, your operational workflows, your schedule, things like that,” Khaled notes. These systems helped practices track what happened manually, but they didn’t automate the workflows.

The fundamental issue: existing tools were built around human-driven processes. They provided forms to fill out, databases to update, and reports to review. But the actual work—calling insurance companies, reading explanation of benefits documents, reconciling payments, identifying collection opportunities—still required humans.

This wasn’t a failure of execution. It was an architectural limitation. These systems were designed in an era when automation meant digitizing forms, not eliminating human judgment. Solving the three-way transaction problem properly required AI-powered automation that could handle the complexity, ambiguity, and decision-making that humans were doing manually.

The Torch Architecture: Automation as Infrastructure

Torch’s approach to solving the three-way transaction problem centered on a fundamental principle: automate complete workflows, not individual tasks.

“Our bread and butter at Torch is really using AI to take all these manual workflows and automate them,” Khaled shares. But the sophistication lies in what “complete workflows” means.

Insurance verification isn’t just checking if someone has coverage. It’s understanding what procedures are covered, what the reimbursement rates are, whether prior authorization is needed, what documentation is required, and what the patient’s out-of-pocket maximum situation looks like. Torch’s AI handles all of this automatically, in seconds.

Payment posting isn’t just recording that money arrived. It’s reading explanation of benefits documents that come in various formats, matching payments to specific procedures and patients, identifying underpayments or denials, flagging issues that need attention, and updating account balances correctly across the three-way relationship. Again, Torch automates the entire workflow.

Collections isn’t just sending reminders. It’s analyzing payment patterns, identifying which accounts need attention, determining optimal outreach timing and channels, predicting collection likelihood, and prioritizing staff effort where it matters most. The AI learns from 15 million transactions to optimize strategies continuously.

The key insight: you can’t solve the three-way transaction problem by automating pieces of it. You need to automate the complete end-to-end flow, handling all the edge cases, exceptions, and complexity that make the problem genuinely hard.

The Data Advantage: Why Complexity Creates Moats

Here’s the paradox: the more complex the problem you solve, the stronger your competitive moat—if you solve it through data accumulation.

“We’ve processed about $5 billion of patient transactions over our life, which is about 15 million unique transactions,” Khaled explains. This transaction volume isn’t just a vanity metric. It represents the training data that makes Torch’s automation increasingly accurate.

Every insurance verification teaches the AI about that plan’s coverage patterns. Every claim submission teaches about documentation requirements and approval likelihood. Every payment posting teaches about reimbursement rates and denial patterns. Every collection attempt teaches about patient payment behaviors.

The three-way transaction problem is perfect for building data moats because it’s inherently probabilistic rather than deterministic. There’s no rule book that says exactly how every insurance plan will handle every claim. The only way to predict outcomes accurately is through pattern recognition across massive transaction volumes.

Competitors starting today would need years of transaction data to reach Torch’s current baseline capability. And during those years, Torch continues accumulating data, pushing the goal line further away. The complexity that makes the problem hard to solve initially becomes the moat that makes it hard to compete with later.

The GTM Implications: Solving Hard Problems Changes Everything

The decision to tackle dental’s three-way transaction problem rather than building easier software had profound GTM implications.

First, it created a value proposition that’s immediately measurable. Practices can calculate exactly how many staff hours Torch saves. This isn’t subjective quality-of-life improvement—it’s concrete operational savings that show up in headcount and payroll.

Second, it positioned Torch as infrastructure rather than a tool. When you eliminate 40% of administrative burden, you’re not providing a nice-to-have feature. You’re becoming essential operational infrastructure. This changes pricing power, retention dynamics, and competitive positioning entirely.

Third, it created natural expansion opportunities. Once Torch becomes the system of record for patient financial data, adjacent workflows become natural extensions. Payment processing, patient financing, insurance relationships, analytics—all flow naturally from controlling the three-way transaction infrastructure.

“Our ambition is really, again, to be the operating system, to be that piece of connective tissue that connects the insurance companies with the patients and with the practices and sits at the center of all those relationships,” Khaled explains. This vision is only possible because Torch solved a problem complex enough to become foundational infrastructure.

The Founder Lesson: Hard Problems as Strategy

For B2B founders evaluating opportunities, Torch Dental’s journey reveals a counterintuitive truth: harder problems often create easier GTM motions.

Most founders avoid complexity, seeking simple problems with obvious solutions. But simple problems attract competition, commoditize quickly, and struggle to justify premium pricing or create moats.

Complex problems—especially those involving multi-party coordination, probabilistic outcomes, and massive data requirements—create natural barriers to competition. If you can solve them properly, you build something genuinely defensible.

“We want to be the back office, effectively, for these practices, so that they can focus on providing great care,” Khaled notes. This vision of eliminating operational burden entirely only works when the problem is complex enough that solving it creates transformative value.

The three-way transaction problem in dental is a perfect example: complex enough that incumbents couldn’t solve it, important enough that solving it creates massive value, and structural enough that the solution becomes permanent infrastructure rather than a temporary tool.

For founders willing to tackle truly hard problems, the reward isn’t just building a successful company—it’s building something that becomes impossible to displace.